Spatial tactile feedback can enhance the realism of geometry exploration in virtual reality applications. Current vibrotactile approaches often face challenges with the spatial and temporal resolution needed to render different 3D geometries. Inspired by the natural deformation of finger pads when exploring 3D objects and surfaces, we propose TactDeform, a parametric approach to render spatio-temporal tactile patterns using a finger-worn electro-tactile interface. The system dynamically renders electro-tactile patterns based on both interaction contexts (approaching, contact, and sliding) and geometric contexts (geometric features and textures), emulating deformations that occur during real-world touch exploration. Results from a user study \rr{(N=24)} show that the proposed approach enabled high texture discrimination and geometric feature identification compared to a baseline. Informed by results from a free 3D-geometry exploration phase, we provide insights that can inform future tactile interface designs.

Vision health is a critical domain of clinical practice, yet global access to diagnosis, rehabilitation, and education remains uneven due to economic and geographic disparities. Extended reality (XR) offers opportunities to extend clinical services through portable assessments and interactive simulations, but its design for vision health has been constrained by technical and translational challenges. This paper presents three studies within a sustained co-design process examining how immersive rendering tools can support vision care across clinical and educational contexts. First, we worked with clinicians to map rendering attributes to clinically meaningful functions, identifying opportunities for diagnostic and training use. Second, we evaluated a prototype visual field assessment, demonstrating feasibility in a diagnostic setting. Third, we applied vision simulations in orthoptic training, enhancing empathy and understanding in an educational setting. Together, these studies show how engaging domain experts advances XR tools for vision health, informing diagnosis, patient–clinician communication, and professional education.

Retrieval-augmented generation (RAG) is indispensable for enhancing large language models. However, RAGs are increasingly susceptible to poisoning attacks, in which adversarial documents are injected to manipulate generator outputs. Previous methods rely on output-side signals such as perplexity and consistency checks to detect such attacks. Nevertheless, our analysis reveals that deliberate attacks often induce false confidence, where poisoned outputs exhibit even lower perplexity than benign ones, rendering uncertainty-based detection ineffective. To address this challenge, we explore the internal dynamics of the generator and identify a distinctive signature termed Attention Collapse. Unlike the dispersed attention in benign generations, attacked generations exhibit a decrease in entropy as attention concentrates on poisoned documents. Building on these findings, we propose D-SCAN (Document-level Signal Collapse Analysis), a lightweight detection framework that monitors attention dynamics to identify attacked generations. Extensive experiments on multiple attack benchmarks demonstrate the effectiveness of our method. Moreover, D-SCAN can detect attacks even when they fail to alter the final answer. Code is available at https://github.com/yingtaoren/D-Scan.git.

Advances in 3D reconstruction and novel view synthesis have enabled efficient and photorealistic rendering. However, images for reconstruction are still either largely manual or constrained by simple preplanned trajectories. To address this issue, recent works propose generalizable next-best-view planners that do not require online learning. Nevertheless, robustness and performance remain limited across various shapes. Hence, this study introduces Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction (Hestia1), which addresses the shortcomings of the reinforcement learning-based generalizable approaches for five-degree-of-freedom viewpoint prediction. Hestia systematically improves the planners through four components: a more diverse dataset to promote robustness, a hierarchical structure to manage the high-dimensional continuous action search space, a close-greedy strategy to mitigate spurious correlations, and a face-aware design to avoid overlooking geometry. Experimental results show that Hestia achieves non-marginal improvements, with at least a 4% gain in coverage ratio, while reducing Chamfer Distance by 50% and maintaining real-time inference. In addition, Hestia outperforms prior methods by at least 12% in coverage ratio with a 5-image budget and remains robust to object placement variations. Finally, we demonstrate that Hestia, as a next-best-view planner, is feasible for the real-world application. Our project page is https://johnnylu305.github.io/hestia_web.

Presynaptic inhibition after spinal cord injury (SCI) has been hypothesised to disproportionately affect flexion reflex loops in locomotor spinal circuitry. Reducing gamma-aminobutyric acid (GABA) inhibitory activity increases the excitation of flexion circuits, restoring muscle activation and stepping ability. Conversely, nociceptive sensitisation and muscular spasticity can emerge from insufficient GABAergic inhibition. To investigate the effects of neuromodulation and proprioceptive sensory afferents in the spinal cord, a biologically constrained spiking neural network (SNN) was developed. The network describes the ankle flexor motoneuron (MN) reflex loop with inputs from ipsilateral Ia- and II-fibres and tonically firing interneurons. The model was tuned to a Baseline level of locomotive activity before simulating an inhibitory-dominant and body-weight supported (BWS) SCI state. Electrical stimulation (ES) and serotonergic agonists were simulated by the excitation of dorsal fibres and reduced conductance in excitatory neurons. ES was applied across all afferent fibres without phase- or muscle-specific protocols. The present computational findings suggest that reducing stance-phase GABAergic inhibition on flexor motoneurons could facilitate more physiological flexor activation during locomotion. The model further predicts that neuromodulatory therapy, together with body-weight support, modulates the balance of synaptic excitation and inhibition in ankle flexor motoneurons to mitigate excessive inhibitory drive in the ankle flexor circuitry.

Electroencephalogram (EEG) signals are inherently non-stationary and exhibit significant inter-subject variability, leading to pronounced cross-subject distribution shifts that hinder accurate emotion recognition. Although graph convolutional networks (GCNs) and domain adaptation (DA) methods have made progress in mitigating individual differences, existing approaches still face two fundamental limitations: (1) traditional GCNs rely on static functional connectivity graphs, which fail to capture the dynamic temporal evolution of neural interactions during emotional processes, and (2) most DA-based methods only emphasize global feature alignment while overlooking emotion-specific semantic structures, thereby impairing both fine-grained discriminability and cross-subject generalization. To overcome these challenges, we propose the Prototypical Contrastive Learning with Temporal Dynamic Graph Convolutional Network (PCL-TDGCN) for EEG-based emotion recognition. Specifically, we construct an adaptive global EEG pattern memory mechanism to model temporally dynamic brain networks, thereby facilitating spatiotemporal neural interactions essential for emotion representation learning. Furthermore, we design a prototypical contrastive learning strategy that incorporates: (i) intra-domain contrastive learning to enhance the discriminability of emotional state representations, and (ii) inter-domain contrastive learning to mitigate distribution shifts across domains via semantic-aware prototypical alignment. Extensive experiments on three public datasets demonstrate that the proposed PCL-TDGCN outperforms state-of-the-art methods, achieving accuracy improvements of 1.08% (SEED), 6.53% (HIED), and 0.98% (SEED-IV) in subject-dependent experiments, and 1.08% (SEED), 7.51% (HIED), and 1.99% (SEED-IV) in subject-independent scenarios, respectively.

Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an inter pretable fuzzy rule-based learning framework that preserves human-understandable reasoning structures within modern neural architectures. Each fuzzy rule corresponds to a semantic and spatial prototype defined in the original feature space, enabling transparent inference and direct interpretability. Meta-learning is employed as an analytical paradigm to examine how these interpretable rules reorganize across tasks and domains, providing a principled means to link algorithmic adaptation with cognitive representation. A knowledge-guided regularization mechanism further enables a top-down–bottom-up integration, in which theoretical priors act as soft inductive biases while data-driven learning refines and extends them. This dual process ensures that adaptation proceeds along semantically and physiologically meaningful trajectories, rather than arbitrary parameter shifts. Evaluations demonstrate that iFuzz-Meta achieves interpretable reasoning and stable cross-domain generalization, establishing a potential general pathway toward explainable and knowledge aware fuzzy systems.

Time series classification (TSC) is a critical area with broad applications. In the field of evidence theory, quantum evidence theory (QET) offers a promising framework for onedimensional TSC tasks, leveraging the capabilities of quantum basic probability amplitude (QBPA) to capture two-dimensional uncertainty. However, as the first step for the application of QET to TSC, how to construct QBPA still remains an open issue. In this paper, a novel approach to generate QBPA is devised. Specifically, we first apply the discrete Fourier transform (DFT) to the original data, extracting two-dimensional features embedded in the magnitude and phase from the frequency domain based on the front-few multi-frequency components, achieved by setting a threshold frequency index (TFI) to limit the frequencies considered. Next, we introduce the complex dual gaussian fuzzy number (CDGFN) as a carrier for QBPA, effectively representing two-dimensional uncertainty in the data. A CDGFN-based multisource information fusion (CDGFN-MSIF) algorithm for decision-making is proposed to combine information from different frequency components. Finally, the decisionmaking algorithm is validated on multiple time series datasets. Experimental results highlight the superior performance of the proposed approach over other state-of-the-art models, demonstrating its effectiveness and enhanced classification accuracy.

This paper explores silent speech decoding in active brain-computer interface (BCI) systems, which offer more natural and flexible communication than traditional BCI applications. We collected a new silent speech dataset of over 120 hours of electroencephalogram (EEG) recordings from 12 subjects, capturing 24 commonly used English words for language model pretraining and decoding. Following the recent success of pretraining large models with self-supervised paradigms to enhance EEG classification performance, we propose Large Brain Language Model (LBLM) pretrained to decode silent speech for active BCI. To pretrain LBLM, we propose Future Spectro-Temporal Prediction (FSTP) pretraining paradigm to learn effective representations from unlabeled EEG data. Unlike existing EEG pretraining methods that mainly follow a masked-reconstruction paradigm, our proposed FSTP method employs autoregressive modeling in temporal and frequency domains to capture both temporal and spectral dependencies from EEG signals. After pretraining, we finetune our LBLM on downstream tasks, including word-level and semantic-level classification. Extensive experiments demonstrate significant performance gains of the LBLM over fully-supervised and pretrained baseline models. For instance, in the difficult cross-session setting, our model achieves 47.2% accuracy on semantic-level classification and 42.3% in word-level classification, outperforming baseline methods substantially. Our research advances silent speech decoding in active BCI systems, offering an innovative solution for EEG language model pretraining and a new dataset for fundamental research.

This paper presents MindSpeak, a real-time brain-computer interface (BCI) system for recording, processing, and decoding silent speech to enable online multimodal communication between the human brain and a computer, involving both noninvasive multichannel EEG signals and text output. To enable hand-free and brain-only control, our system incorporates steady-state visual evoked potential (SSVEP) for users to select incomplete sentences from a predefined pool and confirm the correctness of decoded words. An intuitive graphical interface is designed for natural communication. We evaluate the effectiveness of our real-time BCI system, which achieves 77.3% accuracy in decoding silent speech and 98.9% accuracy in SSVEP-based selection and confirmation of correct sentences. Unlike existing BCI systems, the presented MindSpeak system significantly expands the application scope of existing BCI systems by enabling users to express complete thoughts through a fully BCI-controlled interactive interface. Our demonstration video is on: https://youtu.be/B1wt1dmCCrg.

Clustering is an essential analytical tool across a wide range of scientific fields, including biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel clustering algorithm, called Torque Clustering, as a competitive alternative to existing methods, based on the intuitive principle that a cluster should merge with its nearest neighbor with a higher mass, unless both clusters have relatively large masses and the distance between them is also substantial. By identifying peaks in mass and distance, the algorithm effectively detects and removes incorrect mergers. The proposed method is entirely parameter-free, enabling it to autonomously recognize various cluster types, determine the optimal number of clusters, and identify noise. Extensive experiments on synthetic and real-world data sets demonstrate the algorithm's versatility and consistently strong performance compared to other state-of-the-art methods.

In this article, we introduce the Fuzzy logic-based attention (Fuzzy Attention Layer) mechanism, a novel computational approach designed to enhance the interpretability and efficacy of neural models in psychological research. The fuzzy attention layer integrated into the transformer encoder model to analyze complex psychological phenomena from neural signals captured by functional near-infrared spectroscopy (fNIRS). By leveraging fuzzy logic, the fuzzy attention layer learns and identifies interpretable patterns of neural activity. This addresses a significant challenge in using transformers: the lack of transparency in determining which specific brain activities most contribute to particular predictions. Our experimental results, obtained from fNIRS data engaged in social interactions involving handholding, reveal that the fuzzy attention layer not only learns interpretable patterns of neural activity but also enhances model performance. In addition, these patterns provide deeper insights into the neural correlates of interpersonal touch and emotional exchange. The application of our model shows promising potential in understanding the complex aspects of human social behavior, verify psychological theory with machine learning algorithms, thereby contributing significantly to the fields of social neuroscience and AI.

Deep-learning models have been widely used in image recognition tasks due to their strong feature-learning ability. However, most of the current deep-learning models are "black box" systems that lack a semantic explanation of how they reached their conclusions. This makes it difficult to apply these methods to complex medical image recognition tasks. The vision transformer (ViT) model is the most commonly used deep-learning model with a self-attention mechanism that shows the region of influence as compared to traditional convolutional networks. Thus, ViT offers greater interpretability. However, medical images often contain lesions of variable size in different locations, which makes it difficult for a deep-learning model with a self-attention module to reach correct and explainable conclusions. We propose a multigranularity random walk transformer (MGRW-Transformer) model guided by an attention mechanism to find the regions that influence the recognition task. Our method divides the image into multiple subimage blocks and transfers them to the ViT module for classification. Simultaneously, the attention matrix output from the multiattention layer is fused with the multigranularity random walk module. Within the multigranularity random walk module, the segmented image blocks are used as nodes to construct an undirected graph using the attention node as a starting node and guiding the coarse-grained random walk. We appropriately divide the coarse blocks into finer ones to manage the computational cost and combine the results based on the importance of the discovered features. The result is that the model offers a semantic interpretation of the input image, a visualization of the interpretation, and insight into how the decision was reached. Experimental results show that our method improves classification performance with medical images while presenting an understandable interpretation for use by medical professionals.

The creation of a spatial sound scene through binaural rendering draws increasing research interest, given the rising application of virtual reality and augmented reality. High-fidelity augmented reality audio requires accurate room acoustic simulation. Typically, this is achieved with binaural rendering of spatial sounds using recording of a Higher-Order Microphone (HOM) and Head-Related Transfer Functions (HRTFs) based simulations. However, HOM often fails to deliver full immersion due to the limited order of recording. In this paper, we introduce a Binaural Room Impulse Response (BRIR) calculation method based on HOM recordings of Room Impulse Responses (RIRs) to improve binaural sound rendering. The proposed method enables a more accurate analysis and rendering of the directive components in the limited-order spherical harmonic (SH) recording of RIRs with sparse recovery beamforming. Results with measured RIRs using a HOM indicate that the proposed method achieves a more accurate BRIR calculation than the conventional SH domain method.

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity nature.Individual variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

Driving under drowsy conditions significantly escalates the risk of vehicular accidents. Recent endeavors to prevent driving accidents have focused on using electroencephalography (EEG) to identify drowsy mental states. However, a more versatile EEG-based assistant system is better equipped to achieve smooth human-machine interaction in driving scenarios. This system should be capable of understanding a driver’s intention while demonstrating resilience to artifacts induced by unexpected sudden movements. Building on this goal, this paper pioneers a novel research direction in the field of brain-computer interface for assisted driving, studying the neural patterns associated with driving intentions and presenting a novel method for driving intention prediction. In particular, our preliminary analysis of the EEG signal using independent component analysis suggests a close relation between the intention of driving maneuvers and the neural activities in central-frontal and parietal areas. Power spectral density analysis at a group level also reveals a notable distinction among various driving intentions in the frequency domain. To exploit these brain dynamics, we propose a novel Masked EEG Modeling (MEM) framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding. Extensive experiments, encompassing comprehensive quantitative and qualitative assessments on a publicly available driving dataset, demonstrate the proposed MEM method is proficient in predicting driving intentions across various vigilance states. Specifically, our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving. Ablation also demonstrates that our method significantly enhances the flexibility in handling missing channels. Notably, our method maintains over 75% accuracy when more than half of the channels are missing or corrupted, underscoring its adaptability in real-life driving scenarios.

In the realm of digital forensics and document authentication, writer identification plays a crucial role in determining the authors of documents based on handwriting styles. The primary challenge in writer-id is the “open-set scenario”, where the goal is accurately recognizing writers unseen during the model training. To overcome this challenge, representation learning is the key. This method can capture unique handwriting features, enabling it to recognize styles not previously encountered during training. Building on this concept, this paper introduces the Contrastive Masked Auto-Encoders (CMAE) for Character-level Open-Set Writer Identification. We merge Masked Auto-Encoders (MAE) with Contrastive Learning (CL) to simultaneously and respectively capture sequential information and distinguish diverse handwriting styles. Demonstrating its effectiveness, our model achieves state-of-the-art (SOTA) results on the CASIA online handwriting dataset, reaching an impressive precision rate of 89.7%. Our study advances universal writer-id with a sophisticated representation learning approach, contributing substantially to the ever-evolving landscape of digital handwriting analysis, and catering to the demands of an increasingly interconnected world.

Modern marine navigation places significant physical and mental demands on officers stationed on ship bridges, primarily due to the continuous observation and evaluation of real-time navigational information displayed on scattered electronic equipment. To alleviate the high cognitive load experienced by marine officers and allow them to focus on essential tasks during complex situations, the integration of head-up displays (HUDs) in marine applications has emerged as a promising solution. HUDs offer the potential to provide crucial information, enhancing the accessibility and organization of previously disordered data. However, there is limited information on the impact of HUDs on marine officers, which has been explored in the presented work. In this work, a novel immersive navigation experiment with three conditions: traditional display (NonAR), augmented reality (AR) based information presentation, and a variant of AR with essential information only (AR-Indicator) has been conducted. The objective is to explore the effects of these three conditions on navigation performance and mental workload. Our findings indicate that the AR-based information presentation, specifically the variant that includes only essential information, is preferred by participants and showed performance improvements measured by time to complete tasks, gaze duration, and pupil dilation compared to the traditional display and full AR condition. These results have an impact on the design and development of HUDs in marine-related tasks. This pilot research sheds light on the potential benefits of HUDs in improving maritime navigation and paves the way for further advancements in this field.

Maritime surveillance is essential in understanding, predicting, and ensuring the security of events in the complex marine environment. In this context, we have used instance segmentation techniques, which provides an accurate and efficient method for segmenting objects (Ships) in maritime surveillance. However, prevalent two-stage algorithms have limitations, including complex models, extended training time, and high memory consumption, making them impractical for real-world application. To address these challenges, we present an efficient solution called Multiscale Attention for Single-Stage Ship Instance Segmentation, or MASSNet. MASSNet uses the power of attention mechanisms to enhance multiscale feature extraction across various dimensions, resulting in a more refined and contextually-aware representation. This approach significantly improves segmentation accuracy and overall performance. In our extensive experiments, we evaluate the effectiveness of MASSNet on three challenging datasets: MariboatS, ShipInsSeg, and ShipSG. Our proposed model achieves mask Average Precision (mask AP) scores of 55.4%, 55.5%, and 74.1% on MariboatS, ShipInsSeg, and ShipSG datasets and outperforms other models such as YOLACT, SOLO and SOLOv2 architectures. MASSNet offers a robust and efficient solution for Ship Instance Segmentation, making significant improvement in the capabilities of maritime surveillance.

Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the entire data into memory at once, which is computationally inefficient when the size of the dataset is large. To address these problems, we propose a novel Spark-based attribute reduction and NEC for rough evidence in this article. Specifically, we first construct a multigranular sample space using the parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale datasets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency.

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data using fuzzy mappings and if-then rules. However, they suffer from generalization and dimensionality issues. Although deep neural networks (DNNs) represent a step toward processing high-dimensional data, their capacity to address data uncertainty is limited. Furthermore, deep learning algorithms designed to improve robustness are either time consuming or yield unsatisfactory performance. In this article, we propose a robust fuzzy neural network (RFNN) to overcome these problems. The network contains an adaptive inference engine that is capable of handling samples with high-level uncertainty and high dimensions. Unlike traditional FNNs that use a fuzzy AND operation to calculate the firing strength for each rule, our inference engine is able to learn the firing strength adaptively. It also further processes the uncertainty in membership function values. Taking advantage of the learning ability of neural networks, the acquired fuzzy sets can be learned from training inputs automatically to cover the input space well. Furthermore, the consequent layer uses neural network structures to enhance the reasoning ability of the fuzzy rules when dealing with complex inputs. Experiments on a range of datasets show that RFNN delivers state-of-the-art accuracy even at very high levels of uncertainty. Our code is available online. https://github.com/leijiezhang/RFNN.

Weakly supervised anomaly detection (WSAD) has gained increasing attention due to its core idea of enhancing the performance of unsupervised anomaly detection by leveraging prior knowledge from a limited number of labeled anomalies. In this article, we introduce a novel WSAD framework that surpasses current state-of-the-art methods in terms of accuracy, exhibits greater robustness to data uncertainty, and is more efficient in utilizing limited labeled anomalies. Our method is built upon twin fuzzy networks (TFN) that learn robust fuzzy if–then rules from a pairwise training set. TFN can extract informative prototypes of training instances, exploiting the very few labeled anomalies efficiently. A two-stage sequential training scheme, comprising fuzzy C-means clustering and interpolation consistency regularization, ensures that the fuzzy rules form a solid foundation for anomaly detection while improving TFN's generalization ability. The training process of TFN relies on closed-form optimization rather than gradient-based methods, leading to significantly faster training speeds. Comprehensive experiments conducted on numerous real-world datasets confirm the advantages of the TFN framework over existing alternatives.

Histopathological image classification constitutes a pivotal task in computer-aided diagnostics. The precise identification and categorization of histopathological images are of paramount significance for early disease detection and treatment. In the diagnostic process of pathologists, a multitiered approach is typically employed to assess abnormalities in cell regions at different magnifications. However, feature extraction is often performed at a single granularity, overlooking the multigranular characteristics of cells. To address this issue, we propose the fuzzy-guided multigranularity deep neural network (FMDNN). Inspired by the multi-granular diagnostic approach of pathologists, we perform feature extraction on cell structures at coarse, medium, and fine granularity, enabling the model to fully harness the information in histopathological images. We incorporate the theory of fuzzy logic to address the challenge of redundant key information arising during multigranular feature extraction. Cell features are described from different perspectives using multiple fuzzy membership functions, which are fused to create universal fuzzy features. A fuzzy-guided cross-attention module guides universal fuzzy features toward multigranular features. We propagate these features through an encoder to all patch tokens, aiming to achieve enhanced classification accuracy and robustness. In experiments on multiple public datasets, our model exhibits a significant improvement in accuracy over commonly used classification methods for histopathological image classification and shows commendable interpretability.

The data mining community has a major challenge in classifying datasets with noise, outliers and imbalanced classes. Twin support vector machine (TSVM) is a well-known plane-based learning technique for classification, however, it has poor performance on aforementioned datasets. To address the issue, in this paper, we propose a novel class probability and generalized bell fuzzy twin SVM for imbalanced data (CGFTSVM-ID). The proposed CGFTSVM-ID assigns membership value to the data points by a new membership function called class probability and generalized bell (CPGB) function. The membership function for majority class is a combination of generalized bell (gbell) function, class probability and imbalance ratio. The gbell function suppress the negative impact of outliers in the training data by assigning them less value. The less class probability of the majority class data points denotes their higher possibility to be noise. The imbalance ratio of the classes considered in the membership function tackles the imbalancing issue of the datasets. In order to ensure the importance of minority class samples in model learning, relatively high memberships are assigned to them. Thus, the proposed CPGB function handles the class imbalance learning problem having noise, and outliers. We employ successive overrelaxation technique to solve the proposed optimization problem. The extensive numerical experiments and statistical analysis carried out over imbalanced real-world UCI and KEEL datasets clearly reveal that the proposed CGFTSVM-ID has superior generalization performance in comparison to baseline models. Moreover, the experiments are also conducted on the publicly available ADNI dataset for Alzheimer's disease classification and results demonstrate the superiority of the proposed CGFTSVM-ID. The code for the proposed CGFTSVM-ID can be found onhttps://github.com/mtanveer1/CGFTSVM-ID.

In practical applications, datasets frequently encompass noise, outliers, and imbalanced classes, which can markedly affect a model's generalization performance. Support vector machine (SVM) and its twin variant i.e., TWSVM tend to be biased towards the majority class samples, leading to misclassification of the minority class samples. TWSVM suffers from this biasness as it generates hyperplanes without considering preceding data information. To address the aforementioned issues, we propose bell-shaped fuzzy least square twin support vector machine for imbalance data (BSFLSTSVM-ID). The proposed BSFLSTSVM-ID allocates weight to the majority class samples through a novel membership function, namely, “class probability and bell-shaped” (CPBS). The CPBS function is amalgamation of class probability, bell-shaped function, and imbalance ratio of the dataset. The bell-shaped function's value diminishes as data points move farther away from the class center, reducing the influence of noise or outliers in constructing hyperplanes. To underscore the importance of samples from minority class, a weight of one is assigned to them. Furthermore, the proposed BSFLSTSVM-ID utilizes the conjugate gradient method to handle the challenge of matrix inversion. To demonstrate its effectiveness, we conducted experiments on 59 UCI and KEEL datasets with imbalance ratios from 1 to 72.69. Additionally, we tested the proposed BSFLSTSVM-ID model's scalability on NDC datasets and applied it to diagnosing breast cancer and Alzheimer's disease using the BreakHis and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets, respectively. The results show that the proposed BSFLSTSVM-ID outperforms baseline models, highlighting its potential for tackling classification challenges in the biomedical domain.

The explainability of reinforcement learning (RL) models has received vast amount of interest as its applications have widened. Most existing explainable RL models focus on improving the explainability of an agent's observations instead of the relationships between agent states and actions. This study presents a fuzzy centered explainable network (FCEN) for RL tasks to interpret the relationships between agent states and actions. The proposed FCEN leverages the interpretability of fuzzy neural networks to establish if–then rules and a generative model to visualize learned knowledge. Precisely, the FCEN includes if–then rules that formulate state-action mappings with human-understandable logic, such as the form “IF Input is A THEN Output is B.” In addition, these rules connect with a generative model that concretizes the states into human-understandable patterns (figures). Our experimental results obtained on 4 Atari games show that the proposed FCEN can achieve a high level of performance in RL tasks and enormously boost the explainability of RL agents both globally and locally. In other words, the FCEN maintains a high-level explanation for the agent decision logic and the possibility of low-level analysis for each given observation sample. The explainability boost does not undermine reward learning performance, humans can even enhance the agent's performance with the provided explainability.

Alzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of dementia. Researchers are increasingly drawn to automated diagnosis of AD using neuroimaging analyses. Conventional deep learning (DL) models excel in constructing learning classifiers in early-stage AD diagnosis. However, they often struggle with AD diagnosis due to uncertainties stemming from unclear annotations by experts, challenges in data collection, such as data harmonization issues, and limitations in equipment resolution. These factors contribute to imprecise data, hindering accurate analysis, interpretation of obtained results, and understanding of complex symptoms. In response, the integration of fuzzy logic into DL, forming fuzzy deep learning (FDL), effectively manages imprecise data and provides interpretable insights, offering a valuable advancement in AD. Therefore, exploring recent advancements in integrating DL with fuzzy logic is crucial for improving AD diagnosis. In this review, we explore the contributions of fuzzy logic within FDL models, focusing on fuzzy-based image preprocessing, segmentation, and classification. Moreover, in exploring research directions, we discuss the possibility of the fusion of multimodal data with fuzzy logic, addressing challenges in AD diagnosis. Leveraging fuzzy logic and membership while integrating diverse datasets, such as genomics, proteomics, and metabolomics may provide an effective development of a DL classifier. In addition, fuzzy explainable DL promises more accurate and linguistically interpretable decision support systems for AD diagnosis. The primary objective of this article is to serve as a comprehensive and authoritative resource for newcomers, researchers, and clinicians interested in employing FDL models for AD diagnosis.

Multisource information fusion is a comprehensive and interdisciplinary subject. Dempster-Shafer (D-S) evidence theory copes with uncertain information effectively. Pattern classification is the core research content of pattern recognition, and multisource information fusion based on D-S evidence theory can be effectively applied to pattern classification problems. However, in D-S evidence theory, highly-conflicting evidence may cause counterintuitive fusion results. Belief divergence theory is one of the theories that are proposed to address problems of highly-conflicting evidence. Although belief divergence can deal with conflict between evidence, none of the existing belief divergence methods has considered how to effectively measure the discrepancy between two pieces of evidence with time evolutionary. In this study, a novel fractal belief Rényi (FBR) divergence is proposed to handle this problem. We assume that it is the first divergence that extends the concept of fractal to R/'enyi divergence. The advantage is measuring the discrepancy between two pieces of evidence with time evolution, which satisfies several properties and is flexible and practical in various circumstances. Furthermore, a novel algorithm for multisource information fusion based on FBR divergence, namely FBReD-based weighted multisource information fusion, is developed. Ultimately, the proposed multisource information fusion algorithm is applied to a series of experiments for pattern classification based on real datasets, where our proposed algorithm achieved superior performance.

Federated learning is an emerging learning paradigm where multiple clients collaboratively train a machine learning model in a privacy-preserving manner. Personalized federated learning extends this paradigm to overcome heterogeneity across clients by learning personalized models. Recently, there have been some initial attempts to apply transformers to federated learning. However, the impacts of federated learning algorithms on self-attention have not yet been studied. In this article, we investigate this relationship and reveal that federated averaging (FedAvg) algorithms actually have a negative impact on self-attention in cases of data heterogeneity, which limits the capabilities of the transformer model in federated learning settings. To address this issue, we propose FedTP, a novel transformer-based federated learning framework that learns personalized self-attention for each client while aggregating the other parameters among the clients. Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scalability and generalization of FedTP. Specifically, we achieve this by learning a hypernetwork on the server that outputs the personalized projection matrices of self-attention layers to generate clientwise queries, keys, and values. Furthermore, we present the generalization bound for FedTP with the learn-to-personalize mechanism. Extensive experiments verify that FedTP with the learn-to-personalize mechanism yields state-of-the-art performance in the non-IID scenarios. Our code is available online https://github.com/zhyczy/FedTP.

Multisource data fusion analysis, particularly in decision-level fusion strategies, is emerging for application in real-life scenarios. The Dempster–Shafer evidence theory (DSET) is a prevalent approach that has significant importance in managing the fusion tasks. However, existing fusion approaches have limitations in dealing with redundant information and computational complexity associated with the fusion procedure. Though conflict management has been thoroughly studied, other limitations have not been well addressed. In this article, we propose a novel approach for evidential multisource data fusion based on game-theoretic analysis. The introduction of the Shapley function considers the interaction effect of focal elements, mitigating the negative influence of redundant evidence. Additionally, the computational complexity of the fusion procedure is reduced to the same level as the approximate Bayesian update model. We provide a numerical example with conflicting and redundant evidence to show that the proposed approach outperforms current advanced weighted average-based fusion methods. Moreover, a simulation experiment demonstrates the practicality and effectiveness of the proposed approach in identifying driver fatigue states based on electroencephalography (EEG) signals.

In real-world data stream mining, the composition of classes undergoes unpredictable changes, giving rise to the challenge of class evolution, encompassing class emergence, disappearance, and reoccurrence. However, most existing approaches require the storage of past data to adapt their model. While some studies have focused on online learning approaches, they are built on an underlying assumption that the number of instances in any single class is consistently less than the sum of other classes. This assumption becomes invalid when a class emerges with a dominant amount, e.g., news about a pandemic outbreak, harming the performance of existing methods. In this paper, we thoroughly investigate this scenario and propose a novel online ensemble of ensemble one-versus-all framework (EEOF) to handle class evolution adaptively. The novel ensemble of ensemble architecture boosts diversity in each one-versus-all classifier. A novel adaptive model adaptation method is also designed to balance the error feedback between the emerging class and the other classes. A confidence-triggered fallback mode is integrated to prevent performance drop due to a wrong decision regarding class disappearance. Experimental studies are conducted on both synthetic and real-world data streams to show that our method achieves higher accuracy in diverse class evolution scenarios compared with the state-of-the-art method, particularly when classes emerge with dominant amounts.

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. These event markers may not be readily available or could be challenging to acquire during real-time inference, and the sequence of eye fixations may not align with the order of spoken words. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it alleviates the order mismatch between eye fixations and spoken words by introducing text-EEG contrastive alignment training, and 2) it minimizes the interference caused by individual differences in EEG waves through an invariant discrete codex. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34\%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. Furthermore, this work is the first to facilitate the translation of entire EEG signal periods without the need for word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset, respectively.

Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience VR sickness when wearing VR headsets that allow free walking in room-scale spaces. This paper studies VR sickness and postural instability while the user walks in an immersive virtual environment using an electroencephalogram (EEG) headset and a full-body motion capture system. The experiment induced VR sickness by gradually increasing the translation gain beyond the user's detection threshold. A between-group comparison between participants with and without VR sickness symptoms found some significant differences in postural stability but found none on gait patterns during the walking. In the EEG analysis, the group with VR sickness showed a reduction of alpha power, a phenomenon previously linked to a higher workload and efforts to maintain postural control. In contrast, the group without VR sickness exhibited brain activities linked to fine cognitive-motor control. The EEG result provides new insights into the postural instability theory: participants with VR sickness could maintain their postural stability at the cost of a higher cognitive workload. Our result also indicates that the analysis of lower-frequency power could complement behavioral data for continuous VR sickness detection in both stationary and mobile VR setups.

Abstract Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.

Information can be quantified and expressed by uncertainty, and improving the decision level of uncertain information is vital in modeling and processing uncertain information. Dempster-Shafer evidence theory can model and process uncertain information effectively. However, the Dempster combination rule may provide counter-intuitive results when dealing with highly conflicting information, leading to a decline in decision level. Thus, measuring conflict is significant in the improvement of decision level. Motivated by this issue, this paper proposes a novel method to measure the discrepancy between bodies of evidence. First, the model of dynamic fractal probability transformation is proposed to effectively obtain more information about the non-specificity of basic belief assignments (BBAs). Then, we propose the higher-order fractal belief Rényi divergence (HOFBReD). HOFBReD can effectively measure the discrepancy between BBAs. Moreover, it is the first belief Rényi divergence that can measure the discrepancy between BBAs with dynamic fractal probability transformation. HoFBReD has several properties in terms of probability transformation as well as measurement. When the dynamic fractal probability transformation ends, HoFBReD is equivalent to measuring the Rényi divergence between the pignistic probability transformations of BBAs. When the BBAs degenerate to the probability distributions, HoFBReD will also degenerate to or be related to several well-known divergences. In addition, based on HoFBReD, a novel multisource information fusion algorithm is proposed. A pattern classification experiment with real-world datasets is presented to compare the proposed algorithm with other methods. The experiment results indicate that the proposed algorithm has a higher average pattern recognition accuracy with all datasets than other methods. The proposed discrepancy measurement method and multisource information algorithm contribute to the improvement of decision level.

Fuzzy membership is an effective approach used in twin support vector machines (SVMs) to reduce the effect of noise and outliers in classification problems. Fuzzy twin SVMs (TWSVMs) assign membership weights to reduce the effect of outliers, however, it ignores the positioning of the input data samples and hence fails to distinguish between support vectors and noise. To overcome this issue, intuitionistic fuzzy TWSVM combined the concept of intuitionistic fuzzy number with TWSVMs to reduce the effect of outliers and distinguish support vectors from noise. Despite these benefits, TWSVMs and intuitionistic fuzzy TWSVMs still suffer from some drawbacks as: 1) the local neighborhood information is ignored among the data points and 2) they solve quadratic programming problems (QPPs), which is computationally inefficient. To overcome these issues, we propose a novel intuitionistic fuzzy weighted least squares TWSVMs for classification problems. The proposed approach uses local neighborhood information among the data points and also uses both membership and nonmembership weights to reduce the effect of noise and outliers. The proposed approach solves a system of linear equations instead of solving the QPPs which makes the model more efficient. We evaluated the proposed intuitionistic fuzzy weighted least squares TWSVMs on several benchmark datasets to show the efficiency of the proposed model. Statistical analysis is done to quantify the results statistically. As an application, we used the proposed model for the diagnosis of Schizophrenia disease.

Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this article, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm that can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. First, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multitask optimization (MTO) problem and employ an evolutionary MTO algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Second, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are nonindependent and identically distributed (non-IID). In this article, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods.

The papers in this special section (Part II) focus on advanced machine learning algorithms for biomedical data and imaging. The papers in Part II aim at bringing together contributions from both academia and industry to highlight the recent progress of machine-learning algorithm specific to medical data.

Complex evidence theory (CET) is an effective method for uncertainty reasoning in knowledge-based systems with good interpretability that has recently attracted much attention. However, approaches to improve the performance of uncertainty reasoning in CET-based expert systems remains an open issue. One key to performance improvement is the adequate management of conflict from multisource information. In this paper, a generalized correlation coefficient, namely, the complex evidential correlation coefficient (CECC), is proposed for the complex mass functions or complex basic belief assignments (CBBAs) in CET. On this basis, a complex conflict coefficient is proposed to measure the conflict between CBBAs; when CBBAs turn into classic BBAs, the complex correlation and conflict coefficients will degrade into traditional coefficients. The complex conflict coefficient satisfies nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement properties, which are desirable for conflict measurement. Several numerical examples validate through comparisons the superiority of the complex conflict coefficient. In this context, a weighted discounting multisource information fusion algorithm, which is called the CECC-WDMSIF, is designed based on the CECC to improve the performance of CET-based expert systems. By applying the CECC-WDMSIF method to the pattern classification of diverse real-world datasets, it is demonstrated that the proposed CECC-WDMSIF outperforms well-known related approaches with higher classification accuracy and robustness.

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the framework of actor-critic RL, we introduce multiple cooperative critics from two levels of a hierarchy and propose an RL from the hierarchical critics (RLHC) algorithm. In our approach, each agent receives value information from local and global critics regarding a competition task and accesses multiple cooperative critics in a top-down hierarchy. Thus, each agent not only receives low-level details, but also considers coordination from higher levels, thereby obtaining global information to improve the training performance. Then, we test the proposed RLHC algorithm against a benchmark algorithm, that is, proximal policy optimization (PPO), under four experimental scenarios consisting of tennis, soccer, banana collection, and crawler competitions within the Unity environment. The results show that RLHC outperforms the benchmark on these four competitive tasks.

Advances in virtual reality technology have greatly benefited the acrophobia research field. Virtual reality height exposure is a reliable method of inducing stress with low variance across ages and demographics. When creating a virtual height exposure environment, researchers have often used haptic feedback elements to improve the sense of realism of a virtual environment. While the quality of the rendered for the virtual environment increases over time, the physical environment is often simplified to a conservative passive haptic feedback platform. The impact of the increasing disparity between the virtual and physical environment on the induced stress levels is unclear. This article presents an experiment that explored the effect of combining an elevated physical platform with different levels of virtual heights to induce stress. Eighteen participants experienced four different conditions of varying physical and virtual heights. The measurements included gait parameters, heart rate, heart rate variability, and electrodermal activity. The results show that the added physical elevation at a low virtual height shifts the participant's walking behaviour and increases the perception of danger. However, the virtual environment still plays an essential role in manipulating height exposure and inducing physiological stress. Another finding is that a person's behaviour always corresponds to the more significant perceived threat, whether from the physical or virtual environment.

Most existing multi-view clustering methods have problems with parameter selection and high computational complexity, and there have been very few works based on hierarchical clustering to learn the complementary information of multiple views. In this paper, we propose a Multi-view Adjacency-constrained Nearest Neighbor Clustering (MANNC) and its parameter-free version (MANNC-PF) to overcome these limitations. Experiments tested on eight real-world datasets validate the superiority of the proposed methods compared with the 13 current state-of-the-art methods.

Abstract In the financial market, the stock price prediction is a challenging task which is influenced by many factors. These factors include economic change, politics and global events that are usually recorded in text format, such as the daily news. Therefore, we assume that real-world text information can be used to forecast stock market activity. However, only a few works considered both text and numerical information to predict or analyse stock trends. These works used preprocessed text features as the model inputs; therefore, latent information in text may be lost because the relationships between the text and stock price are not considered. In this paper, we propose a fusion network, i.e. a spatial-temporal attention-based convolutional network (STACN) that can leverage the advantages of an attention mechanism, a convolutional neural network and long short-term memory to extract text and numerical information for stock price prediction. Benefiting from the utilisation of an attention mechanism, reliable text features that are highly relevant to stock value can be extracted, which improves the overall model performance. The experimental results on real-world stock data demonstrate that our STACN model and training scheme can handle both text and numerical data and achieve high accuracy on stock regression tasks. The STACN is compared with CNNs and LSTMs with different settings, e.g. a CNN with only stock data, a CNN with only news titles and LSTMs with only stock data. CNNs considering only stock data and news titles have mean squared errors of 28.3935 and 0.1814, respectively. The accuracy of LSTMs is 0.0763. The STACN can achieve an accuracy of 0.0304, outperforming CNNs and LSTMs in stock regression tasks.

Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.

This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

In this article, we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two motor-imagery brain–computer interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions, which preserve the width of the input intervals. In order to apply them in an MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued ordered weighted averaging operators, and obtaining competitive results versus some nonaggregation-based frameworks.

Twin support vector machines (TSVMs) have been successfully employed for binary classification problems. With the advent of machine learning algorithms, data have proliferated and there is a need to handle or process large-scale data. TSVMs are not successful in handling large-scale data due to the following: 1) the optimization problem solved in the TSVM needs to calculate large matrix inverses, which makes it an ineffective choice for large-scale problems; 2) the empirical risk minimization principle is employed in the TSVM and, hence, may suffer due to overfitting; and 3) the Wolfe dual of TSVM formulation involves positive-semidefinite matrices, and hence, singularity issues need to be resolved manually. Keeping in view the aforementioned shortcomings, in this article, we propose a novel large-scale fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL). We formulate the LS-FLSTSVM-CIL such that the proposed optimization problem ensures that: 1) no matrix inversion is involved in the proposed LS-FLSTSVM-CIL formulation, which makes it an efficient choice for large-scale problems; 2) the structural risk minimization principle is implemented, which avoids the issues of overfitting and results in better performance; and 3) the Wolfe dual formulation of the proposed LS-FLSTSVM-CIL model involves positive-definite matrices. In addition, to resolve the issues of class imbalance, we assign fuzzy weights in the proposed LS-FLSTSVM-CIL to avoid bias in dominating the samples of class imbalance problems. To make it more feasible for large-scale problems, we use an iterative procedure known as the sequential minimization principle to solve the objective function of the proposed LS-FLSTSVM-CIL model. From the experimental results, one can see that the proposed LS-FLSTSVM-CIL demonstrates superior performance in comparison to baseline classifiers. To demonstrate the feasibility of the proposed LS-FLSTSVM-CIL on large-scale classification problems, we evaluate the classification models on the large-scale normally distributed clustered (NDC) dataset. To demonstrate the practical applications of the proposed LS-FLSTSVM-CIL model, we evaluate it for the diagnosis of Alzheimer’s disease and breast cancer disease. Evaluation on NDC datasets shows that the proposed LS-FLSTSVM-CIL has feasibility in large-scale problems as it is fast in comparison to the baseline classifiers.

Recently, distributed semisupervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate nonsensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semisupervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semisupervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost. Our code is available online.1

Recently, demand for biometric access controls and online payments in smartphones increased, necessitating further investigation and development in this area. This article proposes a new low-cost palm vein recognition system for smartphones using red, green, blue (RGB) images. First, we detect and enhance palm vein patterns, using the saturation channel instead of the red channel as in the existing approaches. Then, to address the challenging contactless capturing problems of smartphones—such as scale variants, rotation, closed fingers, or rings on hand—we introduce an improved method for the region of interest extraction, based on the convex hull, with a new idea for key vector use. We also designed a new lightweight deep learning-based model for smartphones, which was overlooked in previous palm vein recognition studies. The proposed model comprises suitable blocks of convolution, depthwise separable convolution, inverted residual bottleneck, and spatial pyramid pooling module; in addition, the accuracy is enhanced with fusion strategy. Results show that the proposed model is both smaller and more accurate than related models. The integrated proposed model obtains the best equal error rate, 0.49%, and an inference time of 8 ms.

The papers in this special section focus on advanced machine learning algorithms for biomedical data and image processing. Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance, and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation and to explore large-scale data in biomedical sciences.

Face recognition can be installed in a surveillance system so that it can be used for monitoring, tracking and access control. An excellent, intelligent surveillance system should be sensitive to the objects far away from the camera. Unfortunately, due to the long-distance, objects like human faces captured by the camera are too small to identify. As to enhance the subtle color differences in the face image, in this paper we first improve the resolution of the captured image using deep convolution neural networks (DCNNs). Then the efficient features are extracted and used to do classification. As for verifying the effectiveness of the proposed method, we used three databases including AR face database, Georgia Tech face database (GT) database, and Labelled Faces in the Wild (LFW) database, altogether, to conduct the training and testing. Compared to the existing approaches, experimental results show that the identification accuracy of the proposed method outperforms any existing approaches.

Distracted driving refers to multisensory integration and attention shifts between attentional driving and different interferences from different modalities, including visual and auditory stimuli. Here, we compared the behavioral performance with interacting multisensory distractors during attentional driving. Then, the independent component analysis (ICA) and event-related spectral perturbation (ERSP) were applied to investigate the neural oscillation changes. The behavioral results showed that the response times (RTs) increased when distractors appeared in response to attentional driving. Moreover, the RTs were longer when the distractor interference was presented in the auditory modality compared with the visual modality. Eye movement intervals showed shorter tracking saccades under distractor interference. These results may indicate that attentional driving performance was impaired under the exposure to multisensory distractor interference. The ERSPs under visual and auditory distraction exposure showed decreased beta power in the frontal area, increased theta and delta power in the central area, and decreased alpha power in the parietal area. During this process, distracted driving under cross-modal sensory interference required more neural oscillation involvement. Moreover, the visual modality showed increased gamma power in the frontal, central, parietal and occipital areas, while the auditory modality showed decreased gamma power in the frontal area, indicating that auditory interference could intervene in top-down attentional processing.

Imparting biological realism during the learning process is gaining attention towards producing computationally efficient algorithms without compromising the performance. Feedback alignment and mirror neuron concept are two such approaches where the feedback weight remains static in the former and update via Hebbian learning in the later. Though these approaches have proven to work efficiently for supervised learning, it remained unknown if the same can be applicable to reinforcement learning applications. Therefore, this study introduces RHebb-DFA where the reward-based Hebbian learning is used to update feedback weights in direct feedback alignment mode. This approach is validated on various Atari games and obtained equivalent performance in comparison with DDQN.

The multiple-preys pursuit (MPP) is the adversarial game between predators and preys. If the capture of a prey is defined as that it cannot move anymore due to the surrounding of predators, there are two kinds of task allocations. One is about assigning which prey to which group of predators so that all preys can be captured. The other is about assigning which capturing position to which predator to encircle the prey simultaneously. In this paper, the MPP is modeled as a dynamic optimization problem and each its time step is solved in two stages. Firstly, the first kind of task allocation problem is modeled as the biquadratic assignment problem (BiQAP) and a MPP fitness function is proposed for the evaluation of such BiQAP task allocations. In this way, the MPP is transformed to several single-prey pursuit (SPP) problems. Secondly, for each SPP, we extend the coordinated SPP strategy CCPSO-R (cooperative coevolutionary particle swarm optimization for robots) to its parallel version as PCCPSO-R to enable the parallel implicit capturing position allocating by parallel observation, decision making, and moving of predators. Through experiments of the current BiQAP solvers on the task allocation, we improve the best one of them in statistic based on the domain knowledge. Moreover, the advantages of PCCPSO-R in the capturing efficiency over CCPSO-R is testified in the MPP experiments.

Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be recorded on the scalp through the medium of electroencephalogram (EEG). EEG waveforms are recorded, analysed and categorized across directives concerning a Brain-Computer Interface (BCI). Deteriorated signal to noise ratio and non-stationarities stand as a paramount obstacle in steady decoding of EEG. Appearance of non-stationarities across EEG patterns notably upset the feature waveforms thus worsening the functioning of detection block and as a whole the Brain Computer Interface. Stationary Subspace schemes bring to light subspaces within which data distribution persists stably over time. Current work focuses on the development of a novel spatial transform based feature extraction scheme to address nonstationarity in EEG signals recorded against a drowsiness detection problem (a machine learning regression scenario). The presented approach: F-DIV-IT-JAD-WS derived features distinctly surpassed DivOVR-FuzzyCSP-WS based standard features across RMSE and CC performance criteria pair. We construe that the propounded feature derivation approach based on F-DIV-IT-JAD-WS will usher a significant attention in researchers who are developing algorithms for signal processing, specifically, for BCI regression scenarios.

In physical human-robot collaboration (pHRC), singularity avoidance strategies are often critical to obtain stable interaction dynamics. It is hypothesised a predictable singularity avoidance strategy is preferred in pHRC as humans tend to maximise predictability when using complex systems. By using an electroencephalogram (EEG), it is possible to assess the predictability of a task through a feature found in event-related potentials (ERP) and called prediction-error negativity (PEN). In this paper, two research questions are addressed. Can a complex pHRC singularity avoidance strategy generate a detectable PEN? Are PEN and human preferences related when comparing different control settings in a singularity avoidance strategy? Fourteen participants compared two different sets of parameters (modes) in a singularity avoidance strategy based on the exponentially damped least-squared (EDLS) method. ERP results are presented in terms of power spectral density (PSD). ERP results were then compared with human preferences to see whether they are related. Results show that the mode that causes PEN is also the one that participants did not like, suggesting that a lack of predictability might have an impact on human preference.

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment.Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications.EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas.In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years.Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals.Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications.Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.!

Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.

This special issue explores foundation methodologies, the latest directions, and emerging applications in a new generation of fuzzy systems related to human-explainable AI. The presented studies offer a snapshot of the latest advances in human-centric intelligence and interdisciplinary research, including computational behavioral science, learning methods for interpretable models, theoretical approaches and knowledge representation to explainability, decision support systems, and fuzzy control systems to explain robot behavior. They provided comprehensible explanations of AI decisions that enhanced the training performance on the benchmarks and the transparency through an interface to the end-users and engineers.

Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this article, we present a novel multigranulation supertrust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation supertrust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme was adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular coevolution is employed to ensure a wide range of balancing of exploration and exploitation, and this strategy can classify super elitists' preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.

Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.

Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning difficult, from providing enough processing power to maintaining model accuracy to protecting privacy. However, perhaps the most imposing problem is that big data is often interspersed with sensitive personal data. Hence, we propose a privacy-preserving hierarchical fuzzy neural network to address these technical challenges while also alleviating privacy concerns. The network is trained with a two-stage optimization algorithm, and the parameters at low levels of the hierarchy are learned with a scheme based on the well-known alternating direction method of multipliers, which does not reveal local data to other agents. Coordination at high levels of the hierarchy is handled by the alternating optimization method, which converges very quickly. The entire training procedure is scalable, fast, and does not suffer from gradient vanishing problems like the methods based on backpropagation. Comprehensive simulations conducted on both regression and classification tasks demonstrate the effectiveness of the proposed model. Our code is available online.

In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.

Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.

The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgment of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator through supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion-MuJoCo games-relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01% of the agent's interactions with the environment and significantly reduces the cost of human inputs by up to 30% compared with the existing approaches. To present the flexibility of our approach, we released a video (https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviors of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.

In this paper, classification of mental task-root brain-computer interfaces (BCIs) is being investigated. The mental tasks are dominant area of investigations in BCI, which utmost interest as these system can be augmented life of people having severe disabilities. The performance of BCI model primarily depends on the construction of features from brain, electroencephalography (EEG), signal, and the size of feature vector, which are obtained through multiple channels. The availability of training samples to features are minimal for mental task classification. The feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper suggests an approach to augment the performance of a learning algorithm for the mental task classification on the utility of power spectral density (PSD) using feature selection. This paper also deals a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above stated method, the findings demonstrate substantial improvements in the performance of learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and compare various combinations of PSD and feature selection methods.

Brain-computer interfaces (BCIs) allow users to communicate directly with external devices via their brain signals. Recently, BCIs, and wearable computers in particular, have been receiving more attention by government and industry as an alternative means of interacting with technology. Wearable computers can combine highly immersive virtual/augmented/mixed reality experiences for entertainment, health monitoring, utilitarian purposes, and, most importantly at present, research. With wearable computers, researchers can design, simulate, and finely control experiments to examine human-brain dynamics outside the laboratory. Yet despite the power of BCIs, take-up is slow. This form of interaction is unnatural to humans and often requires external stimuli. Further, the response feedback produced by the computer part of the system is nowhere near as quick as our brains. Hence, we undertook a review of the current state-of-the-art in BCI research and distilled the current findings into a stimulus-free BCI, called direct-sense BCIs, that operates directly and seamlessly from our thinking. This is a novel paradigm that, in the short term, could substantially improve the quality of a user's experience with BCI, and, over the long term, lead to much more widespread take-up of BCI technology.

Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction.

Room-scale 3D position tracking enables users to explore a virtual environment by physically walking, which improves comfort and the level of immersion. However, when users walk with their eyesight blocked by a head-mounted display, they may unexpectedly lose their balance and fall if they bump into real-world obstacles or unintentionally shift their center of mass outside the margin of stability. This paper evaluates balance recovery methods and intervention timing during the use of VR with the assumption that the onset of a fall is given. Our experiment followed the tether-release protocol during clinical research and induced a fall while a subject was engaged in a secondary 3D object selection task. The experiment employed a two-by-two design that evaluated two assistive techniques, i.e., video-see-through and auditory warning at two different timings, i.e., at fall onset and 500ms prior to fall onset. The data from 17 subjects showed that video-see-through triggered 500 ms before the onset of fall can effectively help users recover from falls. Surprisingly, video-see-through at fall onset has a significant negative impact on balance recovery and produces similar results to those of the baseline condition (no intervention).

EEG signals can be processed and classified into commands for brain-computer interface (BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to low signal to noise ratio and non-stationarities. Presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. Stationary Subspace methods discover subspaces in which data distribution remains steady over time. In this paper, we develop novel spatial filtering based feature extraction methods for dealing with nonstationarity in EEG signals from a drowsiness detection problem (a machine learning regression problem). The proposed method: DivOVR-FuzzyCSP-WS based features clearly outperformed fuzzy CSP based baseline features in terms of both RMSE and CC performance metrics. It is hoped that the proposed feature extraction method based on DivOVR-FuzzyCSP-WS will bring in a lot of interest in researchers working in developing algorithms for signal processing, in general, for BCI regression problems.

Writer verification has drawn significant attention over the past few decades due to its extensive applications in forensics and biometrics. In traditional writer verification, handwriting similarity/dissimilarity analysis is mostly performed by extracting two feature vectors from two respective handwritten samples, followed by comparing them in relation to their similarity. In the state-of-the-art writer verification approaches, a distance metric is usually employed in terms of the similarity between two handwritten samples. If the distance between two handwritten samples is greater than a given threshold, then the samples are assumed to be written by two different writers, otherwise, they are considered to be due to the same writer. In this paper, for the very first time, we propose a model that generates English sentences to explain reasons for writer dissimilarity/similarity. First, our proposed model obtains features from handwritten images by employing a convolutional neural network, verifies the writer using a Siamese architecture, and generates English words using a recurrent neural network. Finally, these two networks are merged using an affine transformation to produce an explanatory sentence in support of writer similarity/dissimilarity. We evaluated our model on a handwritten numeral database of 100 writers and obtained promising results.

Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.

This paper presents a multi-objective vibration-based particle-swarm-optimization (MO-VBPSO) algorithm with enhanced exploration ability and convergence performance, for training fuzzy-controller (FC) to achieve robot control. The MO-VBPSO applies a reference point-based leader selection schema that assigns leaders for MO-PSOs' searching optimal parameters of the FC. Besides, the MO-VBPSO framework is integrated with a vibration factor to strengthen the exploration ability for resolving the local minima issue, which is inspired by the amplitude of the Firework Algorithm (FWA). The evaluation of MO-VBPSO focuses on the effect of the vibration factor by applying it to training a mobile robot in a simulation environment. The evaluation results are discussed concerning exploration ability, convergence performance, and performance stability. Experimental results reveal that the proposed MO- VBPSO lifts the performance of robot training significantly.

Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain’s responses as evidence of state changes during fatigue driving.

A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.

Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.

Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDRadjustedp <; 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjustedp <; 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 ± 6% and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.

The papers in this special section focus on fuzzy rough sets for Big Data. Recent advances in computing technology imply collecting vast amount of data coming from various sources, such as the Internet, senor monitoring systems, social networks, mobile communication systems, transportation systems, and so on. We continue to encounter an explosive growth in big data coming with highly visible aspects of volume, variety, velocity, veracity, and value.

Forecasting time series is an emerging topic in operational research. Existing time-series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, in this article we propose a fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation. Specifically, a time-series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time-series data, Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Index, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize its parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensionality, how to increase its generalization ability, etc. Literature has shown that by making appropriate connections between fuzzy systems and other machine learning approaches, good practices from other domains may be used to improve the fuzzy systems, and vice versa. This article gives an overview on the functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four classic machine learning approaches-neural networks, mixture of experts, classification and regression trees, and stacking ensemble regression-for regression problems. We also point out some promising new research directions, inspired by the functional equivalence, that could lead to solutions to the aforementioned problems. To our knowledge, this is so far the most comprehensive overview on the connections between fuzzy systems and other popular machine learning approaches, and hopefully will stimulate more hybridization between different machine learning algorithms.

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.

It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and y band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.

In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods.

Designing immersion is the key challenge in virtual reality; this challenge has driven advancements in displays, rendering and recently, haptics. To increase our sense of physical immersion, for instance, vibrotactile gloves render the sense of touching, while electrical muscle stimulation (EMS) renders forces. Unfortunately, the established metric to assess the effectiveness of haptic devices relies on the user's subjective interpretation of unspecific, yet standardized, questions.

The ubiquitous availability of smartphones and the increasing popularity of social media provide a platform for users to express their feelings, including suicidal ideation. Suicide prevention by suicidal ideation detection on social media lights the path to controlling the rapidly increasing suicide rates amongst youth. This paper proposes a diverse set of features and investigates into feature selection using the Firefly algorithm to build an efficient and robust supervised approach to classifying tweets with suicidal ideation. The development of a suicidal language to create three diverse, manually annotated datasets leads to the validation of the proposed model. An in-depth result and error analysis lead to an accurate system for monitoring suicidal ideation on social media along with the discovery of optimal feature subsets and selection methods using a penalty based Firefly algorithm.

In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate text/character recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural network-based architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long Short-Term Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publicly-available offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.

Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (p <; 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.

Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.

We present a hierarchical neural-fuzzy system for precision coordination of multiple mobile agents for simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning LiDAR to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of controller modules to enable synchronisation of the agents arrival times while avoiding collision with obstacles. The first approach is based on cascading SONFIN (Self-Organizing Neural Fuzzy Inference Network) controllers, and the second approach considers the use of an LSTM (Long ShortTerm Memory) recurrent neural network module alongside SONFIN modules. Parameters of all the controllers are optimised using the Particle Swarm optimization algorithm. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware which will follow in the next phase of our research.

No abstract available.

Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.

Although the European Foundation for Quality Management (EFQM) is one of the best-known business excellence frameworks, its inherent self-assessment approaches have several limitations. A critical review of self-assessment models reveals that most models are ambiguous and limited to precise data. In addition, the impact of expert knowledge on scoring is overly subjective, and most methodologies assume the relationships between variables are linear. This paper presents a new fuzzy multilayer assessment method that relies on fuzzy inference systems to accommodate imprecise data and varying assessor experiences to overcome uncertainty and complexity in the EFQM model. The method was implemented, tested, and verified under real conditions at a regional electricity company. The case was assessed by internal company experts and external assessors from an EFQM business excellence organization and the model was implemented using MATLAB software. When comparing the classical model with the new model, assessors and experts favored outputs from the new model.

Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain-computer interfaces. Large dimensional EEG recordings suffer from poor signal-tonoise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by 9.94%, increased the correlation to true RT on an average by 7.38%, and reduced the MAPE by 7.09%.

The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.

Many machine learning (ML) applications rely on large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data. The emergence of consumer -grade, low-cost brain -computer interfaces (BCIs) and corresponding software development kits' is bringing the use of BCI within reach of application developers. The access that BCI applications have to neural signals rightly raises privacy concerns. Application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other personal data. The challenge is how to engage in meaningful ML with EEG data while protecting the privacy of users.

This paper proposes applying recurrent residual network (RRN) for analyzing electroencephalogram (EEG) data captured during a simulated sustained attention driving task. We first address the suitableness of utilizing residual structure as well as adopting recurrent structure for EEG signal processing. Then based on these descriptions a recurrent residual network is tailored and depicted in detail. Thirdly we use an EEG dataset obtained from a sustained-attention experiment for our model justification. By applying the RRN model to the experimental data and via the competitive result achieved, we demonstrate the elegance of the proposed model. At last, we discuss the characteristics of the learned filters and their interpretations from EEG frequency band perspectives.

Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods.

Presents a brief historical review of the various Editors in Chief of the IEEE Transactions on Fuzzy Systems.

In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation.

The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum-behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles' states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered coevolutionary model with multiagent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependence among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR*has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise as compared with representative algorithms. Moreover, LCQ-ABR*can be successfully applied in the consistent segmentation for neonatal brain three-dimensional MRI, and the consistent segmentation results further demonstrate its stronger applicability.

This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidence obtained by different classification approaches, to realize a fusion system for integrating multimodal information. The Dempster-Shafer theory (D-S theory) of evidence enables us to integrate heterogeneous information from multiple sources to obtain collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, our system assigns beliefs and plausibilities to possible hypotheses of each decision maker and uses a combination rule to fuse multimodal information. For information fusion, an important step in D-S aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which remains an arduous and unsolved problem. Here, we propose a mathematical structure to aggregate weighted evidence extracted from two different types of approaches: fuzzy Naïve Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to address uncertainties between hypotheses/classes. Finally, 12 benchmark problems from the UCI machine learning repository for classification are employed to validate the proposed WFDSF-based scheme. In addition, an application of WFDSF to a practical brain-computer interface problem involving multimodal data fusion is demonstrated in this study. The experimental results show that the WFDSF is superior to several existing methods.

Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BC!s), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BC! classification problems, but their applications in BC! regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BC!, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77%, and at the same time increase the correlation to the true response speed by 19.39-86.47%.

TheIEEE Transactions on Fuzzy Systems(TFS) is a reputable international journal in the research domain of computer science and engineering. This study surveys the TFS publications between 1994 and 2015 which are indexed in Web of Sciences. The purpose is to present a comprehensive overview of the main influencing factors that affect the journal and identify the theme and citation structures of TFS. First of all, the publication and general citation structure of the journal as well as the most cited articles are analyzed. Then, the TFS authorship and coauthorship are well investigated, the maps of author cocitation network and representative subnetwork of the TFS coauthorship are presented. Next, this study gives a global overview of TFS publications. The most influential and productive countries/territories as well as the temporal analysis of the leading countries/territories are presented. Finally, the cocitation network for exploring the ground-breaking research is generated and the main cocitation clusters of TFS publications are discovered.

The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.

This study considers the dynamic changes of complexity feature by fuzzy entropy measurement and repetitive steady-state visual evoked potential (SSVEP) stimulus. Since brain complexity reflects the ability of the brain to adapt to changing situations, we suppose such adaptation is closely related to the habituation, a form of learning in which an organism decreases or increases to respond to a stimulus after repeated presentations. By a wearable electroencephalograph (EEG) with Fpz and Oz electrodes, EEG signals were collected from 20 healthy participants in one resting and five-times 15 Hz SSVEP sessions. Moreover, EEG complexity feature was extracted by multi-scale Inherent Fuzzy Entropy (IFE) algorithm, and relative complexity (RC) was defined the difference between resting and SSVEP. Our results showed the enhanced frontal and occipital RC was accompanied with increased stimulus times. Compared with the 1st SSVEP session, the RC was significantly higher than the 5th SSVEP session at frontal and occipital areas (p <; 0.05). It suggested that brain has adapted to changes in stimulus influence, and possibly connected with the habituation. In conclusion, effective evaluation of IFE has a potential EEG signature of complexity in the SSEVP-based experiment.

© 2017 IEEE. Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems are often with lower applicability and do not provide much help for reducing burdens of users because of the time-consuming preparation required by adopted wet sensors and the shortage of provided interactive functions. Here, by integrating a state visually evoked potential (SSVEP)-based BCI system and a robotic eating assistive system, we propose a non-invasive wireless steady state visually evoked potential (SSVEP)-based BCI eating assistive system that enables users with physical disabilities to have meals independently. The analysis compared different methods of classification and indicated the best method. The applicability of the integrated eating assistive system was tested by an Amyotrophic Lateral Sclerosis (ALS) patient, and a questionnaire reply and some suggestion are provided. Fifteen healthy subjects engaged the experiment, and an average accuracy of 91.35%, and information transfer rate (ITR) of 20.69 bit per min are achieved. For online performance evaluation, the ALS patient gave basic affirmation and provided suggestions for further improvement. In summary, we proposed a usable SSVEP-based BCI system enabling users to have meals independently. With additional adjustment of movement design of the robotic arm and classification algorithm, the system may offer users with physical disabilities a new way to take care of themselves.

Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-state-drift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-state-drift detector to improve the detection accuracy.

Drowsiness driving is one major factor of traffic accident. Monitoring the changes of brain signals provides an effective and direct way for drowsiness detection. One 3D convolutional neural network (3D CNN)-based forecasting system has been proposed to monitor electroencephalography (EEG) signals and predict fatigue level during driving. The limited weight sharing and channel-wise convolution were both applied to extract the significant phenomenon in various frequency bands of brain signals and the spatial information of EEG channel location, respectively. The proposed 3D CNN with limited weight sharing and channel-wise convolution has been demonstrated to predict reaction time (RT) of driving with low root mean square error (RMSE) through the brain dynamics. This proposed approach outperforms with the state-of-the-art algorithms, such as traditional CNN, Neural Network (NN), and support vector regression (SVR). Compared with traditional CNN and Artificial Neural Network, the RMSE of 3D CNN-based RT prediction has been improved 9.5% (RMSE from 0.6322 to 0.5720) and 8% (RMSE from 0.6217 to 0.5720), respectively. We envision that this study might open a new branch between deep learning application in neuro-cognitive analysis and real world application.

One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.

A brain-computer interface (BCI) system using elec-troencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications.

This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets.

We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-the-arts models.

A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.

Previous experimental studies on rabbits using electrocorticograms (ECoGs) over the cortical surface indicate spatio-temporal dynamics in the form of amplitude modulation (AM) patterns, which intermittently collapse at theta rates and give rise to rapidly propagating phase modulated (PM) patterns. The observed dynamics have been shown to be of cognitive relevance carrying useful information on the meaning of sensory information perceived by the subject. We have extended these studies to human scalp EEG measurements, which show evidence that cognitively relevant AM and PM patterns are observable by non-intrusive experimental techniques as well. The present work develops experimental techniques for studying cognitively relevant spatio-temporal neural dynamics using a high-density EEG array. Theoretical considerations indicate that the required spatial resolution to detect and categorize amplitude and phase patterns should be in the range of 3–5 mm. A prototype 1-dimensional array (MINDO-48S) has been developed, which has 48 electrodes in a flexible linear array of 5 mm spacing. The present work focuses on the extraction of broadly distributed spatio-temporal patterns, which carry cognitively relevant information. Preliminary analysis of the signal-to-noise ratio indicates that the sensitivity of the experiment allows the predicted AM patterns to be measured.

An important problem that remains in online data mining systems is how to accurately and efficiently detect changes in the underlying distribution of large data streams. The challenge for change detection methods is to maximise the accumulative effect of changing regions with unknown distribution, while at the same time providing sufficient information to describe the nature of the changes. In this paper, we propose a novel change detection method based on the estimation of equal density regions, with the aim of overcoming the issues of instability and inefficiency that underlie methods of predefined space partitioning schemes. Our method is general, nonparametric and requires no prior knowledge of the data distribution. A series of experiments demonstrate that our method effectively detects concept drift in single dimension as well as high dimension data, and is also able to explain the change by locating the data points that contribute most to the change. The detection result is guaranteed by statistical tests.

Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class that most base classifiers vote for. However, improved performance can be obtained by assigning weights to the base classifiers according to their accuracy. This paper proposes an agreement rate initialized maximum likelihood estimator (ARIMLE) to optimally fuse the base classifiers. ARIMLE first uses a simplified agreement rate method to estimate the classification accuracy of each base classifier from the unlabeled samples, then employs the accuracies to initialize a maximum likelihood estimator (MLE), and finally uses the expectation-maximization algorithm to refine the MLE. Extensive experiments on visually evoked potential classification in a brain-computer interface application show that ARIMLE outperforms majority voting, and also achieves better or comparable performance with several other state-of-the-art classifier combination approaches.

There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.

To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.

Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha (r = 0.421) and gamma (r =0.478) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.

Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed coevolutionary cloud model. First, the framework of N-populations distributed coevolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the reward of different subpopulations' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent data set. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions is adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator's robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment cortical surfaces of the neonatal brain 3-D-MRI records, and the DCCAEDR shows the superior competitive results, when compared with the representative algorithms.

In this paper, we study the multiattribute decision-making (MADM) problem with intuitionistic fuzzy values that represent information regarding alternatives on the attributes. Assuming that the weight information of the attributes is not known completely, we use an approach that utilizes the relative comparisons based on the advantage and disadvantage scores of the alternatives obtained on each attribute. The relative comparison of the intuitionistic fuzzy values in this research use all the three parameters, namely membership degree (“the more the better”), nonmembership degree (“the less the better”), and hesitancy degree (“the less the better”), thereby leading to the tradeoff values of all the three parameters. The score functions (advantage and disadvantage scores) used for this purpose are based on the positive contributions of these parameters, wherever applicable. Furthermore, these scores are used to obtain the strength and weakness scores leading to the satisfaction degrees of the alternatives. The optimal weights of the attributes are determined using a multiobjective optimization model that simultaneously maximizes the satisfaction degree of each alternative. The optimal solution is used for ranking and selecting the best alternative on the basis of the overall attribute values. To validate the proposed methodology, we present a numerical illustration of a real-world case. The methodology is further extended to treat MADM problem with interval-valued intuitionistic fuzzy information. Finally, a thorough comparison is done to demonstrate the advantages of the solution methodology over the existing methods used for the intuitionistic fuzzy MADM problems.

Drowsy driving is a pervasive problem among drivers, and is also an important contributor to motor vehicle accidents. It is very important to be able to estimate a driver's drowsiness level online so that preventative actions could be taken to avoid accidents. However, because of large individual differences, it is very challenging to design an estimation algorithm whose parameters fit all subjects. Some subject-specific calibration data must be used to tailor the algorithm for each new subject. This paper proposes a domain adaptation with model fusion (DAMF) online drowsiness estimation approach using EEG signals. By making use of EEG data from other subjects in a transfer learning framework, DAMF requires very little subject-specific calibration data, which significantly increases its utility in practice. We demonstrate using a simulated driving experiment and 15 subjects that DAMF can achieve much better performance than several other approaches.

In this paper, we develop a novel multi-fusion brain-computer interface (BCI) based on linear discriminant analysis (LDA) to deal with motor imagery (MI) classification problem. We combine filter bank and sub-band common spatial pattern (SBCSP) to extract features from EEG data in the preprocessing phase, and then LDA classifiers are applied to classify brain activities to identify either left or right hand imagery. To further foster the performance of the proposed system, a fuzzy integral (FI) approach is employed to fuse information sources, and particle swarm optimization (PSO) algorithm is exploited to globally update parameters in the fusion structure. Consequently, our experimental results indicate that the proposed system provides superior performance compared to other approaches.

Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs' resting-state EEG power. We collect migraineurs' O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time.σ

Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments.

In this study, we develop a novel multi-fusion brain-computer interface (BCI) system based on a fuzzy neural network (FNN) and information fusion approaches to cope with a classification task for identifying right/left hand motor imagery. In the proposed system, we utilize a filter bank and sub-band common spatial pattern (SBCSP) to extract features from raw EEG data. A self-organizing neural fuzzy inference network (SONFIN) is then applied for a recognition task. In order to improve the classification performance, we form a committee of networks and employ fuzzy integral (FI) to attain a joint decision. To further optimize the fusion approaches, a particle swarm optimization (PSO) algorithm is exploited to globally update parameters used in the fusion stage. In consequence, our experimental result shows that the proposed fuzzy fusion system possesses superior performance compared to other comparative models.

This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-the-art models regardless of the use of recurrent or non-recurrent structures.

On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject's pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual's pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets.

This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.

In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies the collective knowledge of CFC as input variables and the consequent part is a linear combination of the input variables. Through the intensive experimental tests on prediction problem, the performance of the proposed method is as higher as other methods. The proposed method only uses one half information of given dataset for training process and provide an accurate modeling platform while other methods use whole information of given dataset for training.

Preprocessing is generally used for data analysis in the real world datasets that are noisy, incomplete and inconsistent. In this paper, preprocessing is used to refine the inconsistency of the prototype and partition matrices before getting involved in the collaboration process. To date, almost all organizations are trying to establish some collaboration with others in order to enhance the performance of their services. Due to privacy and security issues they cannot share their information and data with each other. Collaborative clustering helps this kind of collaborative process while maintaining the privacy and security of data and can still yield a satisfactory result. Preprocessing helps the collaborative process by using an induced partition matrix generated based on cluster prototypes. The induced partition matrix is calculated from local data by using the cluster prototypes obtained from other data sites. Each member of the collaborating team collects the data and generates information locally by using the fuzzy c-means (FCM) and shares the cluster prototypes to other members. The other members preprocess the centroids before collaboration and use this information to share globally through collaborative fuzzy clustering (CFC) with other data. This process helps system to learn and gather information from other data sets. It is found that preprocessing helps system to provide reliable and satisfactory result, which can be easily visualized through our simulation results in this paper.

Fuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.

Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify Mi-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.

This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.

Using physiological signals to control brain-computer interface (BCI) becomes more popular. Among many kinds of physiological signals, Electrooculography (EOG) signal is more stable which can be used to control BCI systems based on eye movement detection and signal processing methods. Also, the use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. In this paper, we described a signal processing method, which uses a wireless EEG-based BCI system designed to be worn near forehead that can detect both EEG and EOG signals, for detecting eye movements to have 9 direction controls (via EOG) and one action of execution (via EEG). This system included a wireless EEG signal acquisition device, a mechanism that can be worn stably, and an application program (APP) with signal processing algorithms. This algorithm and its classification procedure provided an effective method for identifying eye movements and attention. Finally, we designed a baseball game to test the BCI system. The results demonstrated that player can control the game well with high accuracy.

For many applications, to reduce the processing time and the cost of decision making, we need to reduce the number of sensors, where each sensor produces a set of features. This sensor selection problem is a generalized feature selection problem. Here, we first present a sensor (group-feature) selection scheme based on Multi-Layered Perceptron Networks. This scheme sometimes selects redundant groups of features. So, we propose a selection scheme which can control the level of redundancy between the selected groups. The idea is general and can be used with any learning scheme. We have demonstrated the effectiveness of our scheme on several data sets. In this context, we define different measures of sensor dependency (dependency between groups of features). We have also presented an alternative learning scheme which is more effective than our old scheme. The proposed scheme is also adapted to radial basis function (RBS) network. The advantages of our scheme are threefold. It looks at all the groups together and hence can exploit nonlinear interaction between groups, if any. Our scheme can simultaneously select useful groups as well as learn the underlying system. The level of redundancy among groups can also be controlled.

This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.

Having a well sleep quality is important factor in our daily life. The evaluation of sleep stages has become an important issue due to the distribution of sleep stages across a whole night relates to sleep quality. This study aims to propose a sleep classification system, consists of a preliminary wake detection rule, sleep feature extraction, fuzzy c-means based dimension reduction, support vector machine with radial basis function kernel, and adaptive adjustment scheme, with only FP1 and FP2 electroencephalography. Compared with the results from the sleep technologist, the average accuracy and Kappa coefficient of the proposed sleep classification system is 70.92% and 0.6130, respectively, for individual 10 normal subjects. Thus, the proposed sleep classification system could provide a preliminary report of sleep stages to assistant doctors to make decision if a patient needs to have a detailed testing in a sleep laboratory.

In this paper, an application of the innovative fuzzy model [1] is applied to simulate and synchronize two classical Sprott chaotic systems with unknown noise and disturbance. In traditional Takagi-Sugeno fuzzy (T-S fuzzy) model, there will be 2Nlinear subsystems (according to 2Nfuzzy rules) and m × 2Nequations in the T-S fuzzy system, where N is the number of minimum nonlinear terms and m is the order of the system. Through the new fuzzy model, a complicated nonlinear system is linearized to a simple form - linear coupling of only two linear subsystems and the numbers of fuzzy rules can be reduced from 2Nto 2 × N. The fuzzy equations become much simpler. There are two Sprott systems in numerical simulations to show the effectiveness and feasibility of new model.

Monitoring the neurophysiological activities of driver in an operational environment poses a severe measurement challenge using a current laboratory-oriented biosensor technology. The aims of this research are to 1) introduce a dry and wireless EEG system used for conveniently recording EEG signals from forehead regions, 2) propose an effective system for processing EEG recordings and translating them into the vigilance level, and 3) implement the proposed system with a JAVA-based graphical user interface (GUI) for online analysis. To validate the performance of the proposed system, this study recruited eight voluntary subjects to participate a 90-min sustained-attention driving task in a virtual-realistic driving environment. Physiological evidence obtained from the power spectral analysis showed that the dry EEG system could distinguish an alert EEG from a drowsy EEG by evaluating the spectral dynamics of delta and alpha activities. Furthermore, the experimental result of the comparison of the prediction performance using four forehead electrode sites (AF8, FP2, FP1, and AF7) implied that a single-electrode EEG signal used in the mobile and wireless EEG system is able to obtain a high prediction accuracy (~93%). Taken together, the proposed system applied a dry-EEG device combined with an effective algorithm can be a promising technology for real driving applications.

In this paper, a mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) is proposed for the identification of nonlinear and time-varying systems. The MRIT2NFS uses type-2 fuzzy sets in order to enhance noise tolerance of the system. In the MRIT2NFS, the antecedent part of each recurrent fuzzy rule is defined using interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang type with interval weights. The antecedent part of MRIT2NFS forms a local internal feedback and interaction loop by feeding the rule firing strength of each rule to others including itself. The consequent is a linear combination of exogenous input variables. The learning of MRIT2NFS starts with an empty rule base and all rules are learned online via structure and parameter learning. The structure learning of MRIT2NFS uses online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by rule-ordered Kalman filter algorithm to reinforce parameter learning ability. The type-2 fuzzy sets in the antecedent and weights representing the mutual feedback are learned by the gradient descent algorithm. After the training, a weight-elimination scheme eliminates feedback connections that do not have much effect on the network behavior. This method can efficiently remove redundant recurrence and interaction weights. Finally, the MRIT2NFS is used for system identification under both noise-free and noisy environments. For this, we consider both time series prediction and nonlinear plant modeling. Compared with type-1 recurrent fuzzy neural networks, simulation results show that our approach produces smaller root-mean-squared errors using the same number of iterations.

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of ~82%.

In this paper, the second order sliding mode control strategy is applied on a 6-dof parallel manipulator, so-called Stewart platform. The advantages such as robustness and design simplicity of sliding mode control are the main reason that it was often used on robotic systems. However, the chattering phenomenon of conventional sliding mode control may cause unstability due to the practical implementations of the switching control. As a result of discontinuous control signal, the chattering phenomenon can be eliminated via designing a higher order control strategy. Once the switching signal is designed in higher order manifold, the real input will be smoothed because of the integration. The paper represents a robustness control method without chattering for Stewart platform which is a multi-input nonlinear system. The control performance of the second-order approach was compared to conventional sliding mode control in the simulation results.

Motion sickness occurs when the brain receives conflicting sensory information from body, inner ear and eyes [1]. In some cases, a decreased ability to actively control the body's postural motion also causes motion sickness [2][3]. Many previous studies have indicated that motion sickness had negative effect on driving performance, and sometimes lead to serious traffic accidents due to self-control ability decline. Therefore motion sickness becomes a very important issue in our daily life especially considering driving safety. There are many attempts made by researchers to realize motion sickness, and detect motion sickness in the early stage. Although many motion-sickness-related biomarkers have been identified, estimating human motion sickness level (MSL) remains a challenge in operational environment. In our past studies, we found that features in the occipital area were highly correlated with the driver's driving performance. In this study, we designed a virtual-reality (VR) based driving environment with instinct-MSL-reporting mechanism. When a subject performed a driving task, his/her brain EEG was recorded simultaneously. From those EEG data, features associated with left motor brain area, parietal brain area and occipital midline brain area which predicted MSL were extracted by an optimal classifier implemented by an inheritable bi-objective combinatorial genetic algorithm (IBCGA) with support vector machine. Unlike traditional correlation-based method, IBCGA aims to select a small set of EEG features and maximize the prediction accuracy simultaneously in BCI applications. Once the optimal feature set predicting MSL is successfully found, a driver's cognitive state can be monitored.

Brain-computer interface (BCI) has shown explosive growth for multiple applications in the recently years. Removing artifacts and selecting useful brain sources are essential in BCI research. Independent Component Analysis (ICA) has been proven as an effective technique to remove artifacts and many brain related researches are based on ICA. However, the useful independent components with brain sources are usually selected manually according to the scalp-plots. This is great inconvenience and a barrier for real-time BCI applications of EEG. In this investigation, a two-layer automatic identification model is proposed to select useful brain sources. It is based on neural network including support vector machine with radial basis function (SVMRBF) and self-organizing map (SOM). In the first layer, SVM discriminates useful independent components from the artifact effectively. In the second layer, these selected useful components are automatically classified to different spatial brain sources according to SOM. This study suggests this model to one general application for EEG study. It can reduce the effect of subjective judgment and improve the performance of EEG analysis.

Having local data communication (without global broadcast of signals) among the elements is important in VLSI designs. Recently, 2-D systolic digital filter architectures were presented which eliminated the global broadcast of the input and output signals. The delta discrete-time operator based 1-D and 2-D digital filters (in γ-domain) were shown to offer better numerical accuracy and lower coefficient sensitivity in narrowband filter designs when compared to the traditional shiftoperator formulation. Further, the complexity in the design and implementation of 2-D filters can be reduced considerably if the symmetries that might be present in the frequency responses of these filters are utilized. With this motivation we present new 2-D VLSI filter structures, without global broadcast, using delta discrete-time operator for the first time. We also present frame works in γ-domain that realizes 2-D filters possessing quadrantal symmetry in its magnitude response. The separable denominator and quadrantal symmetry structures have the advantage of reduced number of multipliers while ensuring the 2-D filter stability.

It is very important to reduce the possibility of spatial disorientation because spatial disorientation is a major cause of aircraft crashes. In this research, we assess the effect of transcutaneous electrical nerve stimulation (TENS) on spatial cognitive function by measuring physiological signals, including brain waves measured by electroencephalography (EEG). Through physiological signals such as brain waves, we can objectively understand a subject's cognitive state. Moreover, we use virtual reality technology to build 3D scenery that provides navigation clues in an environment with no visual reference frame. Using the virtual environment, we confirm that TENS lessens spatial disorientation. Across subjects and sessions, the parietal component of brain activity exhibited baseline elevation predominantly in the theta (4-7 Hz) and alpha (8-12 Hz) band as the subjects navigated. This study also found that subjects performed better after TENS in terms of behavioral and EEG data. The results facilitate understanding of the cognitive function of maintaining spatial orientation and development of a device for assisting pilots and reducing the occurrence of spatial disorientation.

Interpretation of cardiac rhythms is highly dependent on accurate detection of atrial activity. The robustness is an important requirement for clinical usage. This study presents an adaptive QRS detection method for real-time clinical ECG signals. In this method, center differentiation is applied as a whitening filer, and a composite function enhances the high frequency QRS energy. To robustly detect clinical data, a novel threshold selection method based on statistics is proposed. Moreover, this study provides a benchmarking clinical dataset acquired from cardiac patients. Our extensive experimental results using the MIT-BIH arrhythmia database show that our technique can detect beats with 99.67% accuracy, and the sensitivity is 99.83%. With the exceptional QRS detection result, further testing of the proposed method with clinical data shows the accuracy for atrial and ventricular arrhythmias is 82.9% and 90.2%, respectively.

The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications.

Recent studies show that hyperspectral image classification techniques that use both spectral and spatial information are more suitable, effective, and robust than those that use only spectral information. Using a spatial-contextual term, this study modifies the decision function and constraints of a support vector machine (SVM) and proposes two kinds of spatial-contextual SVMs for hyperspectral image classification. One machine, which is based on the concept of Markov random fields (MRFs), uses the spatial information in the original space (SCSVM). The other machine uses the spatial information in the feature space (SCSVMF), i.e., the nearest neighbors in the feature space. The SCSVM is better able to classify pixels of different class labels with similar spectral values and deal with data that have no clear numerical interpretation. To evaluate the effectiveness of SCSVM, the experiments in this study compare the performances of other classifiers: an SVM, a context-sensitive semisupervised SVM, a maximum likelihood (ML) classifier, a Bayesian contextual classifier based on MRFs (ML_MRF), and nearest neighbor classifier. Experimental results show that the proposed method achieves good classification performance on famous hyperspectral images (the Indian Pine site (IPS) and the Washington, DC mall data sets). The overall classification accuracy of the hyperspectral image of the IPS data set with 16 classes is 95.5%. The kappa accuracy is up to 94.9%, and the average accuracy of each class is up to 94.2%.

In this paper, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically, and the remaining pixels are classified to the nearest region. After the region growing procedure, two neighboring regions with high similarity are merged. The human body is determined by confining semantic human body region in segmented regions, and those belonging to the human face and human body are merged afterward. Lastly, we will detect the human vertical y-coordinate values in the image, and the depths can then be estimated according to the depth look-up tables of the camera.

In this paper, the Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network (FLIT2CFNN) is a six-layer structure, which combines compensatory fuzzy reasoning method, and the consequent part is combined the proposed functional-link neural network with interval weights. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Initially, there is no rule in the FLIT2CFNN. A FLIT2CFNN is constructed using concurrent structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. All of the antecedent part parameters and compensatory degree values are learned by gradient descent algorithm. Several simulation results show that the FLIT2CFNN achieves better performance than other feedforword type-1 and type-2 FNNs.

This study presents an efficient immune symbiotic evolution learning algorithm for the compensatory neural fuzzy network (CNFN). The proposed immune symbiotic evolution learning method (ISEL) includes three major components initial population, subgroup symbiotic evolution and immune system algorithm. The advantage of the proposed ISEL method are that the subgroup symbiotic evolution method uses the subgroup based population to evaluate the fuzzy rules locally and the adopted immune system algorithm can accelerate the search and increase global search capacity. Finally, the simulation results have shown that the proposed CNFN-ISEL can outperform other methods.

Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, a semisupervised feature extraction method which is based on the scatter matrices of the fuzzy-type LDA and uses the semi-information is proposed. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.

This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on l/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model.

Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, spatial information is acquired by the concept of the Markov random field (MRF), and this spatial information is used to form the membership values of every pixel in the hyperspectral image. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.

Distraction during driving has been recognized as a significant cause of traffic accidents. The aim of this study is to investigate Electroencephalography (EEG) -based brain dynamics in response to driving distraction. To study human cognition under specific driving tasks in a simulated driving experiment, this study utilized two simulated events including unexpected car deviations and mathematics questions. The raw data were first separated into independent brain sources by Independent Component Analysis. Then, the EEG power spectra were used to evaluate the time-frequency brain dynamics. Results showed that increases of theta band and beta band power were observed in the frontal cortex. Further analysis demonstrated that reaction time and multiple cortical EEG power had high correlation. Thus, this study suggested that the features extracted by EEG signal processing, which were the theta power increases in frontal area, could be used as the distracted indexes for early detection of driver inattention in real driving.

Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. This study employs genetic feature selection to find a subset of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. The features selected by genetic feature selection were very different from those obtained by correlation analysis. Results of this study demonstrate that genetic feature selection is a very effective method to optimize the estimation of motion-sickness level. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments.

Motion sickness is a normal response to real, perceived, or even anticipated movement. People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Motion sickness occurs when the body, the inner ear, and the eyes send conflicting signals to the brain. Sensory conflict theory that came about in the 1970's has become the most widely accepted theorem of motion-sickness among scientists [1]. The theory proposed that the conflict between the incoming sensory inputs could induce motion- sickness. However, some new research studies have appeared to tackle the issue of the vestibular function in central nervous system (CNS). In the previous human subject studies, researchers attempt to confirm the brain areas involved in the conflict in multi-modal sensory systems by means of clinical or anatomical methods. Our past studies had investigated the EEG activities correlated with motion sickness in a virtual-reality based driving simulator. We found that the parietal, motor, occipital brain regions exhibited significant EEG power changes in response to vestibular and visual stimuli. Based on these experimental results, we attempt to implement an EEG-based evaluation system to estimate subject's motion sickness level upon the major EEG power spectra from these motion sickness related brain area in this study. The evaluation system can be applied to early detect the subject's motion sickness level and prevent the uncomfortable syndromes occurred in advance in our daily life.

Research has shown fuzzy c-means (FCM) clustering to be a powerful tool to partition samples into different categories. However, the objective function of FCM is based only on the sum of distances of samples to their cluster centers, which is equal to the trace of the within-cluster scatter matrix. In this study, we propose a clustering algorithm based on both within- and between-cluster scatter matrices, extended from linear discriminant analysis (LDA), and its application to an unsupervised feature extraction (FE). Our proposed methods comprise between- and within-cluster scatter matrices modified from the between- and within-class scatter matrices of LDA. The scatter matrices of LDA are special cases of our proposed unsupervised scatter matrices. The results of experiments on both synthetic and real data show that the proposed clustering algorithm can generate similar or better clustering results than 11 popular clustering algorithms: K-means, K-medoid, FCM, the Gustafson-Kessel, Gath-Geva, possibilistic c-means (PCM), fuzzy PCM, possibilistic FCM, fuzzy compactness and separation, a fuzzy clustering algorithm based on a fuzzy treatment of finite mixtures of multivariate Student's t distributions algorithms, and a fuzzy mixture of the Student's t factor analyzers model. The results also show that the proposed FE outperforms principal component analysis and independent component analysis.

Support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the RBF kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.

Growing numbers of traffic accidents had become a serious social safety problem in recent years. The main factor of the high fatalities was the obvious decline of the driver's cognitive state in their perception, recognition and vehicle control abilities while being sleepy. The key to avoid the terrible consequents is to build a detecting system for ongoing assessment of driver's cognitive state. A quickly growing research, brain-computer interface (BCI), offers a solution offering great assistance to those who require alternative communicatory and control mechanisms. In this study, we propose an alertness/drowsiness classification system based on investigating electroencephalographic (EEG) brain dynamics in lane-keeping driving experiments in a virtual reality (VR) driving environment with a motion platform. The core of the classification system is composed of dimension reduction technique and classifier learning algorithm. In order to find the suitable method for better describing the data structure, we explore the performances using different feature extraction and feature selection methods with different classifiers. Experiment results show that the accuracy is over 80% in most combinations and even near 90% under Principal Component Analysis (PCA) and Nonparametric Weighted Feature Extraction (NWFE) going with Gaussian Maximum Likelihood classifier (ML) and k-Nearest-Neighbor classifier (kNN), respectively. In addition, this developed classification system can also solve the individual brain dynamic differences caused from different subjects and overcome the subject dependent limitation. The optimized solution with better accuracy performance out of all combinations can be considered to implement in the kernel brain-computer interface.

Drivers' distraction is widely recognized as a leading cause of car accidents. To investigate the distracting effect of dual-tasks involving driving and answering mathematical equations in the stimulus onset asynchrony (SOA) conditions, we design five different cases: two cases involving single-tasks and three cases involving dual-tasks. We have found that there is no statistically significant change in the behavioral data among the three dual-tasks. This raises an important question - is there any detectable effect of the dual tasks on the brain waves? To answer this, we use the Self-Organizing Map (SOM) to recognize the changes, if any, in the Electroencephalography (EEG) dynamics associated with such dual-tasks. Our SOM analysis based on independent components corresponding to EEG signals extracted from Frontal and Motor areas revealed that single- and dual-tasks have distinguishable signatures in the EEG signals. Specifically, each of the two single-task conditions is clustered in a distinct spatial area of the map. Two of the dual-tasks also exhibit distinct spatial clusters, while the third case although shows differences from the other two, the neurons corresponding to this case are sub-clustered reflecting the fact that different subjects may give different priorities to the tasks when confronted with two tasks simultaneously. SOM-based exploratory analysis reveals the existence of distinct EEG signatures among the distracting and non-distracting tasks, although there is no any noticeable difference in the behavioral data among these cases.

Past studies reported that the main electrogastrography (EEG) dynamic changes related to motion sickness (MS) were occurred in occipital, parietal, and somatosensory brain area, especially in the power increasing of the alpha band (8-13 Hz) and theta band (4-7 Hz) which had positive correlation with the subjective MS level. Depend on these main findings correlated with MS, we attempt to develop an EEG based classification system to automatically classify subject's MS level and find the suitable EEG features via common feature extraction, selection and classifiers technologies in this study. If we can find the regulations and then develop an algorithm to predict MS occurring, it would be a great benefit to construct a safe and comfortable environment for all drivers and passengers when they are cruising in the car, bus, ship or airplane. EEG is one of the best methods for monitoring the brain dynamics induced by motion-sickness because of its high temporal resolution and portability. After collecting the EEG signals and subjective MS level in a realistic driving environment, we first do the data pre-processing part including ICA, component clustering analysis and time-frequency analysis. Then we adopt three common feature extractions and two feature selections (FE/FS) technologies to extract or select the correlated features such as principal component analysis (PCA), linear discriminate analysis (LDA), nonparametric weighted feature extraction (NWFE), forward feature selections (FFS) and backward feature selections (BFS) and feed the feature maps into three classifiers (Gaussian Maximum Likelihood Classifier (ML), k-Nearest-Neighbor Classifier (kNN) and Support Vector Machine (SVM)). Experimental results show that classification performance of all our proposed technologies can be reached almost over 95%. It means it is possible to apply the effective technology combination to predict the subject's MS level in the real life applications. The better combination in this study is using LDA and Gaussian based ML classifier. This advantage can be widely used in machine learning area for developing the prediction algorithms in the future.

In this study, a real-time wireless embedded EEG-based brain computer interface (BCI) system was developed for drowsiness detection in a realistic driving task. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Therefore, real-time drowsiness monitoring is important to avoid traffic accidents. In this study, an embedded EEG-based BCI system which includes a wireless physiological signal acquisition module and an embedded signal processing module was designed, and a real-time drowsiness detection algorithm based on our unsupervised approach was implemented in the embedded signal processing module. EEG signal would be monitored and analyzed by the embedded signal processing module, and the warning tone would be triggered to prevent traffic accidents when the drowsiness condition occurred.

Driver's cognitive state monitoring has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. In our past studies, we found that the EEG power spectrum changes were highly correlated with the driver's driving behavior performance. In this study, we attempt to construct an EEG-based self-constructing neural fuzzy system to monitor and predict the driver's cognitive state. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. Our experimental results including correlation and prediction show that the performances of our proposed system are significantly higher than using the traditional neural networks. Besides, the proposed EEG-based self-constructing neural fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.

In this study, a system with force feedback assistance was adopted to improve human's motor learning. Subjects tried to perform a trajectory tracking task of visuo-motor coordination by using a joystick which involved force feedback assistance. Force feedback was proportionally generated (maximal output 9N) by the joystick and warned subjects of the deviation of the tracking task. In the experiments, motor behavior performances of assistance group and non-assistance group were taken into comparison. The results demonstrated that a joystick with force feedback assistance brought significant influence. Motor performance was improved in the motion of flexion and extension while adding the force feedback assistance. This study provided important new light on the force feedback assistance. It could be an advanced function to improve and deepen people's motor learning capabilities.

Centrifugal force caused by eccentricity in a vertical magnetic bearing (VMB) is dealt with an innovative design by featuring 1) 3-pole 2) 6-step 3) PD fuzzy switching controller 4) hybrid controller. The hybrid controller is composed of a fuzzy switching controller and a traditional ID controller. Fuzzy switching controller regulates the instability of plant when pump shaft deviates by a small perturbation from the nominal gap. The traditional ID controller compensates for the centrifugal force caused by eccentricity. In the simulation, 6-step switching works as a relay race in terms of a competitor. Simulation results approve the approach developed in this paper be feasible to overcome the effect arisen from eccentricity.

A compensatory neurofuzzy system (CNFS) with on-line learning ability is proposed in this paper. The proposed CNFS model uses a compensatory layer to raise the diversity of fuzzy rules by compensatory weights. The compensatory layer can automatically compare with each fuzzy rule and select higher resources for more important fuzzy rule. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the fuzzy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the weights of the compensatory layer. To demonstrate the capability of the proposed CNFS, it is applied to the Iris, and Wisconsin breast cancer classification datasets from the UCI Repository. Experimental results show that the proposed CNFS for pattern classification can achieve good classification performance.

In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semiparametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the nonparametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.

This paper proposes an Internal /Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for dynamic system identification. The antecedent part of IRT2FNN forms a self and interconnection feedback loop by feeding the past and current firing strength of each rule. The TSK-type consequent part is a linear model of exogenous inputs with interval weights. The initial rule base in the IRT2FNN is empty, and an on-line constructing method is proposed to generate fuzzy rules which flexibly partition the input space. The recurrent structure in the IRT2FNN enable to handle dynamic system identification problems with a priori knowledge of system input and output delay numbers. Simulations on dynamic system identification verify the performance of IRT2FNN with clean and noisy outputs as well.

This study presents a novel wireless, ambulatory, real-time, and autoalarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world. This system consists of a lightweight and power-saving wireless ECG device equipped with a built-in automatic warning expert system. This device is connected to a mobile and ubiquitous real-time display platform. The acquired ECG signals are instantaneously transmitted to mobile devices, such as netbooks or mobile phones through Bluetooth, and then, processed by the expert system. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel tracking cardiac-rhythm disorders. The proposed system also activates an emergency medical alarm system when problems occur. Clinical testing reveals that the proposed system is approximately 94% accurate, with high sensitivity, specificity, and positive prediction rates for ten normal subjects and 20 AF patients. We believe that in the future a business-card-like ECG device, accompanied with a mobile phone, can make universal cardiac protection service possible.

The aim of this study is to investigate the difference of EEG dynamics on navigation performance. A tunnel task was designed to classify subjects into allocentric or egocentric spatial representation users. Despite of the differences of mental spatial representation, behavioral performance in general were compatible between the two strategies subjects in the tunnel task. Task-related EEG dynamics in power changes were analyzed using independent component analysis (ICA), time-frequency and non-parametric statistic test. ERSP image results revealed navigation performance-predictive EEG activities which is is expressed in the parietal component by source reconstruction. For egocentric subjects, comparing to trails with well-estimation of homing angle, the power attenuation at the frequencies from 8 to 30 Hz (around alpha and beta band) was stronger when subjects overestimated homing directions, but the attenuated power was decreased when subjects were underestimated the homing angles. However, we did not found performance related brain activities for allocentric subjects, which may due to the functional dissociation between the use of allo- and egocentric reference frames.

Removal of artifacts is an important step in any research in /application of electroencephalogram (EEG). The artifacts may contain eye-blinking, muscle noise, heart signal, line noise, and environmental effect. Such noises often make the raw EEG signals not very useful for extraction/identification of physiological phenomena from EEG. The independent component analysis (ICA) is a popular technique for artifact removal in brain research and some reports demonstrate that ICA can remove the artifacts with lower (acceptable) loss of information. But, these reports select useful independent components manually, primarily by looking at the scalp-plots. This is of great inconvenience and is a barrier for BCI or real-time applications of EEG. In this paper, we demonstrate that machine learning methods could be quite effective to discriminate useful independent components from artifacts and our findings suggests the possibility of developing a ldquouniversalrdquo machine for artifact removal in EEG.

Music provides a tool to study numerous aspects of neuroscience from motion-skill to emotion since listening to and producing music involves many brain functions. The musician's brain is also regarded as an ideal model to investigate plasticity of the human brain. In this paper, an EEG-based neural network is proposed to assess neuroplasticity induced by musical training. A musical chord perception experiment is designed to acquire and compare the behavioral and neural responses of musicians and non-musicians. The ERPs elicited by the consonant and dissonant chords are combined together as the features of the model. The principle component analysis (PCA) is used to reduce feature dimensions and the dimension-reduced features are input to a feedforward neural network to recognize the brain potentials belong to a musician or a non-musician. The accuracy can reach 97% in average for leave-one-out cross validation of six subjects in this experiment. It demonstrates the feasibility of assessing effects of musical training by ERP signals elicited by musical chord perception.

In this paper, we propose two new two-dimensional (2-D) IIR and FIR filter architectures for 2-D transfer function using fourfold rotational symmetry. The presented type-I structure with fourfold rotational symmetry has the lowest number of multipliers, and zero latency. Importantly, the proposed type-II IIR filter possesses high speed, local broadcast, and the same number of multipliers and latency as the type I shows at expense of a slight increment of number of delay elements.

Four new 2-D filter VLSI architectures without global broadcast are presented. The first is a transposed systolic structure which requires fewer delay elements compared to the original systolic structure in [1]. By combining the sub-blocks of the original with the new transposed structure, two additional systolic structures are obtained to realize transfer functions with separable denominators, which require fewer multipliers. These separable denominator structures have important symmetry applications. A structure which possesses diagonal symmetry is then shown which requires even fewer multipliers.

This paper presents a novel, real-time digital image stabilization (DIS) system using a modified proportional integrated (MPI) controller to remove stably unwanted shaking from an image sequence that is captured by a hand-held video camera without affecting the deliberate panning motion of the camera. Researchers have addressed the trade-off problem between the removal of shaking and the preservation of global camera movement. This paper analyzes motion compensation, which is formulated as a control problem. A compensating motion vector (CMV) estimation method with a modified PI control system is proposed both to remove the unwanted jitter and to preserve the deliberate, panning motion of the camera. Experimental results demonstrate that the proposed system provides robust motion compensation of image sequences in various conditions.

This paper presents an efficient immune symbiotic evolution learning (ISEL) algorithm for the compensatory neuro-fuzzy controller (CNFC). The proposed ISEL method includes three major components-initial population, subgroup symbiotic evolution, and immune system algorithm. First, the self-clustering algorithm that determines proper input space partitioning and finds the mean and variance of the Gaussian membership functions and number of rules is applied to the initial population. Second, the subgroup symbiotic evolution method that uses each subantibody represents a single fuzzy rule and the evolution of the rule itself. Third, the immune system algorithm uses the clonal selection principle, such that antibodies between others of high similar degree are canceled, and these antibodies, after processing, will have higher quality, accelerating the search, and increasing the global search capacity. Finally, the proposed CNFC with ISEL (CNFC-ISEL) method is adopted to solve several nonlinear control problems. The simulation results have shown that the proposed CNFC-ISEL can outperform other methods.

This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables. The proposed MODE learning algorithm adopts an evolutionary learning method to optimize the controller parameters. For design optimization, a new criterion is introduced. A hardware-in-the loop control technique is developed and applied to the designed ANFN controller using the MODE learning algorithm. The proposed ANFN controller with the MODE learning algorithm (ANFN-MODE) is used in two practical applications-the planetary-train-type inverted pendulum system and the magnetic levitation system. The experiment is developed in a real-time visual simulation environment. Experimental results of this study have demonstrated the robustness and effectiveness of the proposed ANFN-MODE controller.

This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.

This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.

Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Many studies show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. The detection of class boundaries is an important part in NWFE and the weighted mean was defined for this purpose. In this paper, a kernel-based feature extraction is proposed based on a new class boundary detection mechanism. The soft-margin support vector machine (SVM) binary classifier and the support vector domain description (SVDD) are applied to detect the boundaries between two classes and one class, respectively. The results of real data experiments show that the proposed method outperforms original NWFE.

Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness suggests that for many operators, group statistics cannot be used to accurately predict changes in cognitive states. Attempts have also been made to build a subject-dependent model for each individual based on his/her pilot data to account for individual variability. However, such methods assume the cross-session variability in EEG dynamics to be negligible, which could be problematic due to electrode displacements, environmental noises, and skin-electrode impedance. Here first we show that the EEG power in the alpha and theta bands are strongly correlated with changes in the subject’s cognitive state reflected through his driving performance and hence his departure from alertness. Then under very mild and realistic assumptions we derive a model for the alert state of the person using EEG power in the alpha and theta bands. We demonstrate that deviations (computed by Mahalanobis distance) of the EEG power in the alpha and theta bands from the corresponding alert models are correlated to the changes in the driving performance. Finally, for detection of drowsiness we use a linear combination of deviations of the EEG power in the alpha band and theta band from the respective alert models that best correlates with subject’s changing level of alertness, indexed by subject’s behavioral response in the driving task. This approach could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

For an embedded processor, the cache design almost occupies half chip area and power consumption. According to Amdahl's law, if the power consumption of cache memories is reduced, the embedded processor can significantly save much power. However, the cache misses result in the penalty of thousands of cycles waiting and power consumption due to increasing the number of external memory access. Based on the above reason, the phased cache design is proposed and can largely improve the power consumption which wastes a set-associative cache. In this paper, the embedded pipelining processor without stalling and low-power phase cache is practiced with high-level simulation to achieve high-performance and low-power design. As experimental results, the proposed phase cache can reduce 44% power consumption compared with traditional one-access-cycle cache and eliminate pipeline stalls incurred by phased cache with only 6% gate count overhead.

We proposed a novel analog circuit design which is suitable for various biomedical signal acquisitions. In addition to the consideration of low power and low noise, the analog front-end integrated circuit (AFEIC) is presented with design of high common-mode rejection ratio (CMRR) and high power supply ripple rejection ratio (PSRR). It has not only reduced the number of outer components, and enhances abetter signal-to-noise ratio (SNR). The chip includes a current-balancing instrumentation amplifier, switched-capacitor filter, non-overlapping clock generator, and a programmable gain amplifier (PGA). It was fabricated by TSMC 0.35 mum CMOS 2P4M standard process, with CMRR 155 dB CMRR, 131 dB of PSRR+, and 127 dB of PSRR- at 50 Hz. The power consumption is about 142.4 muW under +1.5 V supply.

Driver distraction has been recognized as a significant cause of traffic incidents. Therefore, the aim of this study was to investigate electroencephalography (EEG) dynamics in response to distraction during driving. To study human cognition under specific driving task, we used virtual reality (VR) based driving simulation to simulate events including unexpected car deviations and mathematics questions (math) in real driving. For further assessing effects of the stimulus onset asynchrony (SOA) between the deviation onset and math presented on the EEG dynamics, we designed five cases with different SOA. The scalp-recorded EEG channel signals were first separated into independent brain sources by independent component analysis (ICA). Then, the event-related-spectral-perturbations (ERSP) measuring changes of EEG power spectra were used to evaluate the brain dynamics in time-frequency domains. Results showed that increases of theta band (5~7.8 Hz) and beta band (12.2~17 Hz) power were observed in the frontal cortex. Results demonstrated that reaction time and multiple cortical EEG sources responded to the driving deviations and math occurrences differentially in the stimulus onset asynchrony. Results also suggested that the theta band power increase in frontal area could be used as the distracted indexes for early detecting driver's inattention in the future.

BACKGROUND: The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems. RESULTS AND DISCUSSION: To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis. CONCLUSION: Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.

Neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunctioning external body parts or internal organs, a much larger population of “healthy” people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses. For example, reduced alertness, lack of attention, or poor decision-making during monotonous, routine tasks can have catastrophic consequences. This study proposes a noninvasive mobile prosthetic platform for continuously monitoring high-temporal resolution brain dynamics without requiring application of conductive gels on the scalp. The proposed system features dry microelectromechanical system electroencephalography sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, online artifact cancellation, and signal processing. Its implications for neural prostheses are examined in two sample studies: 1) cognitive-state monitoring of participants performing realistic driving tasks in the virtual-reality-based dynamic driving simulator and 2) the neural correlates of motion sickness in driving. The experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.

This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.

In this paper, a novel two-stage noise removal algorithm to deal with impulse noise is proposed. In the first stage, an adaptive two-level feedforward neural network (NN) with a backpropagation training algorithm was applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by the human visual system (HVS) are proposed to classify the image pixels into human perception sensitive class and nonsensitive class, and to compensate the blur of the edge and the destruction caused by the median filter. An NN is proposed to enhance the sensitive regions with higher visual quality. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and smoothness in edge regions.

This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems.

This investigation proposes a novel radix-42algorithm with the low computational complexity of a radix-16 algorithm but the lower hardware requirement of a radix-4 algorithm. The proposed pipeline radix-42single delay feedback path (R42SDF) architecture adopts a multiplierless radix-4 butterfly structure, based on the specific linear mapping of common factor algorithm (CFA), to support both 256-point fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) and 8times8 2D discrete cosine transform (DCT) modes following with the high efficient feedback shift registers architecture. The segment shift register (SSR) and overturn shift register (OSR) structure are adopted to minimize the register cost for the input re-ordering and post computation operations in the 8times8 2D DCT mode, respectively. Moreover, the retrenched constant multiplier and eight-folded complex multiplier structures are adopted to decrease the multiplier cost and the coefficient ROM size with the complex conjugate symmetry rule and subexpression elimination technology. To further decrease the chip cost, a finite wordlength analysis is provided to indicate that the proposed architecture only requires a 13-bit internal wordlength to achieve 40-dB signal-to-noise ratio (SNR) performance in 256-point FFT/IFFT modes and high digital video (DV) compression quality in 8 times 8 2D DCT mode. The comprehensive comparison results indicate that the proposed cost effective reconfigurable design has the smallest hardware requirement and largest hardware utilization among the tested architectures for the FFT/IFFT computation, and thus has the highest cost efficiency. The derivation and chip implementation results show that the proposed pipeline 256-point FFT/IFFT/2D DCT triple-mode chip consumes 22.37 mW at 100 MHz at 1.2-V supply voltage in TSMC 0.13-mum CMOS process, which is very appropriate for the RSoCs IP of next-generation handheld devices.

Accidents caused by errors and failures in human performance among traffic fatalities have a high rate causing death and become an important issue in public security. The key problem causing these car accidents is mainly because that the drivers failed to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for ongoing assessment of driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. Three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), discriminant analysis feature extraction (DAFE) are applied to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data can be classified with fewer features and their classification results are compared by utilizing three different classifiers including Gaussian classifier (GC), k nearest neighbor classification (KNNC), and naive Bayes classifier (NBC). Experimental results show that the successful rate of nonparametric weighted feature extraction combined with Gaussian classifier is higher more than 10% compared with other combinations. It also demonstrates the feasibility of detecting and analyzing single-trail ERP signals that represent operators' cognitive states and responses to task events.

The Kannon project at the National Chiao Tung University (NCTU) in Taiwan has designed a smart PDA phone, christened Health Pal, to help elders to manage their healthcare needs such as planning their own physician visits, medication schedules, exercise routines as well as monitoring their blood pressures, pulse rates and oxygen levels. The paper reports the design concepts of the device as well as the activity oriented approach the team undertook during the design process. A comparison of this device against several similar prototypes was also included to highlight its unique features.

In this paper, a novel human visual system (HVS)-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.

Machine vision is a key technology used in an intelligent transportation system (ITS) to augment human drivers' visual capabilities. For the in-car applications, additional motion components are usually induced by disturbances such as the bumpy ride of the vehicle or the steering effect, and they will affect the image interpretation processes that is required by the motion field (motion vector) detection in the image. In this paper, a novel robust in-car digital image stabilization (DIS) technique is proposed to stably remove the unwanted shaking phenomena in the image sequences captured by in-car video cameras without the influence caused by moving object (front vehicles) in the image or intentional motion of the car, etc. In the motion estimation process, the representative point matching (RPM) module combined with the inverse triangle method is used to determine and extract reliable motion vectors in plain images that lack features or contain a large low-contrast area to increase the robustness in different imaging conditions, since most of the images captured by in-car video cameras include large low-contrast sky areas. An adaptive background evaluation model is developed to deal with irregular images that contain large moving objects (front vehicles) or a low-contrast area above the skyline. In the motion compensation processing, a compensating motion vector (CMV) estimation method with an inner feedback-loop integrator is proposed to stably remove the unwanted shaking phenomena in the images without losing the effective area of the images with a constant motion condition. The proposed DIS technique was applied to the on-road captured video sequences with various irregular conditions for performance demonstrations

In this work we propose a novel computer fovea model based on hexagonal-type cellular neural networks (hCNN). The hCNN represents a new image processing architecture that is motivated by the overwhelming evidence for hexagonal image processing in biological systems. The necessary new coupling templates and basic hCNN image operators are introduced. The fovea model includes the biological mechanisms of the photoreceptors, the horizontal cells, the ganglions, the bipolar cells, and their cooperation. Thus the model describes the signal processing from the optical stimulation at retina to the output of the ganglion cells. Different building blocks of the model turned out to be useful for practical image enhancement algorithms. Two such applications are considered in this work, namely the image sharpness improvement and the color constancy algorithm.

This paper is to investigate a novel design for local motion vectors (LMVs) of image sequences, which are often used in a digital image stabilization (IS) system. The IS technique removes unwanted shaking phenomenon in image sequences captured by hand-held camcorders. It includes two main parts such as motion estimation and compensation. Most of computation power occurs in the part of motion estimation. In order to reduce this complexity, an idea, which integrates an adaptive-threshold method and cellular neural networks (CNN) architecture, is designed to improve this problem. The design only implements the most important local motion estimation with the array size of 19/spl times/25 pixels. Experimental results with HSPICE simulation and CNNUM are shown that the proposed architecture fast searches the location of possible LVMs and has the capability of real-time operations. The complete design has integrated into the total area of 8.1mm/sup 2/ by using TSMC 0.35/spl mu/m mixed-signal process.

The public security has become an important issue in recent years, especially, the safe manipulation and control of vehicles in preventing the growing number of traffic accident fatalities. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. The ICAFNN is a fuzzy neural network (FNN) capable of parameter self-adapting and structure self-constructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. Our experiments show that the ICAFNN can achieve significant improvements in the accuracy of drowsiness estimation compared with our previous works.

In this paper, we propose a cost-effective and low-power 64-point fast Fourier transform (FFT)/inverse FFT (IFFT) architecture and chip adopting the retrenched 8-point FFT/IFFT (R8-FFT) unit and an efficient data-swapping method based output buffer unit The whole chip systematic performance concerning about the area, power, latency and pending cycles for the application of IEEE 802.11a WLAN standard has been analyzed. The proposed R8-FFT unit utilizing the symmetry property of the matrix decomposition achieves half computation-complexity and less power consumption compared with the recently proposed FFT/IFFT designs. On the other hand, applying the proposed data-swapping method, a low-cost and low-power output buffer can be obtained. So as to further increase system performance, we propose one scheme: the multiplication-after-write (MAW) method. Applying MAW method with R8-FFT unit, the resulting FFT/IFFT design not only leads to the balancing pending cycle, but also abbreviating computation latency to 8 clock cycles. Consequently, adopting the above proposed two units and one scheme, the whole chip consumes 22.36mW under 1.2V@20 MHz in TSMC 0.13 1P8M CMOS process.

In this paper, a portable real-time wireless embedded brain signal acquisition/processing system is developed. The proposed system integrates electroencephalogram signal amplifier technique, wireless transmission technique, and embedded real-time system. The development strategy of this system contains three parts: First, the Bluetooth protocol is used as a transmission interface and integrated with the bio-signal amplifier to transmit the measured physiological signals wirelessly. Second, the OMAP (open multimedia architecture platform) is used as a development platform and an embedded operating system for OMAP is also designed. Finally, DSP Gateway is developed as a mechanism to deal with the brain-signal analyzing tasks shared by ARM and DSP. A driver's cognitive-state estimation program has been developed and implemented on the proposed dual core processor-based real time wireless embedded system for demonstration.

The objective of this paper is to investigate a novel design for local motion vectors (LMVs) of image sequences, which are often used in a digital image stabilization (IS) system. The IS technique removes unwanted shaking phenomenon in image sequences captured by hand-held camcorders. It includes two main parts such as motion estimation and compensation. Most of computation power occurs in the part of motion estimation. In order to reduce this complexity, an idea, which integrates an adaptive-threshold method and cellular neural networks (CNN) architecture, is designed to improve this problem. The design only implements the most important local motion estimation with the array size of 19x25 pixels. Experimental results with HSPICE simulation and CNNUM are shown that the proposed architecture fast searches the location of possible LVMs and has the capability of real-time operations.

In this paper, we present a real-time multi-objects tracking system which can detect various types of moving objects in image sequences of traffic video obtained from a stationary video camera. Using the adaptive background reconstruction technique can effectively handle with environmental changes and obtain good results of objects extraction. Besides, we introduce a robust region-and feature-based tracking algorithm with plentiful features to track correct objects continuously. After tracking objects successfully, we can analyze the tracked objects' properties and recognize their behavior for extracting some useful traffic parameters. According to the structure of our proposed algorithms, we implemented a tracking system including the functions of objects classification and accident prediction. Experiments were conducted on real-life traffic video of some intersection and testing datasets of other surveillance research. The results proved the algorithms we proposed achieved robust segmentation of moving objects and successful tracking with objects occlusion or splitting events. The implemented system also extracted useful traffic parameters.

Driving safely has received increasing attention of the publics due to the growing number of traffic accidents that the driver's driving style is highly correlated to many accidents. The purpose of this study is to investigate the relationship between driver's driving style and driver's ERP response. In our research, a virtual reality (VR) driving environment is developed to provide stimuli to subjects. Independent component analysis (ICA) is used to decompose the electroencephalogram (EEG) data. The power spectrum analysis of ICA components and correlation analysis are employed to investigate the EEG activities related to driving style. Experimental results demonstrate that we may classify the drivers into aggressive or gentle styles based on the observed ERP difference corresponding to the proposed unexpected obstacle dodging tasks.

Root-locus-based PID controller and LQR-based controller always fail as system nonlinearity increases. We here propose the optimization-compensated block/diagram to reinforce the stabilization ability of these two classical control methods for nonlinear system, and besides, to achieve other performance requirements such as constrained overshoot and fast response. The controller design of a nonlinear sliding weights balancing mechanism is based on optimization-compensated root locus and LQR method. First, according to root-locus of the linearized dynamic system, we propose extra poles and zeros addition to roughly draw the locus shifting to left to achieve stabilization requirement. The poles and zeros are realized by P/PD/PID controllers. For LQR approach, we choose performance parameters to meet stabilization and minimum energy requirement. The controller is realized as feedback controller. Further, to compensate the model-error from nonlinearity and to meet other performance such as overshoot and setting time, some P/PID parameters for root-locus method and the feedback gain for LQR method are optimized via optimal parameter searching in NCD/Matlab toolbox. The simulation results demonstrate the stability and the constrained performances of the entire closed-loop system can be ensured by the proposed compensated control block diagrams.

A team of researchers including computer scientists, electrical and control engineers, architects, industrial designers, human factor engineers, and cognitive scientists in the National Chiao Tung University (NCTU), Taiwan, along with their overseas collaborators launched the project Kannon, a multi-disciplinary effort to develop Adaptive Assistive Technologies that can be deployed incrementally into existing private/public spaces and collaborate opportunistically to offer monitoring, assisting, communicating and rejuvenating services to healthy elders. The team combined the state-of-art information, communication and robotic know-how with the activity oriented method for product design and the modular functional approach in modern architecture in order to devise a holistic support for successful aging. This paper presents the philosophy, approach and first fruits of this project.

Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.

The classification of protein structures is essential for their function determination in bioinformatics. The success of the protein structure classification depends on two factors: the computational methods used and the features selected. In this paper, we use a combinatorial fusion analysis technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying these criteria to our previous work, the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than our previous work and demonstrate that combinatorial fusion is a valuable method for protein structure classification.

During the past years, the public security has become an important issue, especially, the safe manipulation and control of various vehicles. Maintaining high cognition is particularly important for the drivers behind the steering wheel. It requires an optimal estimation system to online continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control. In this paper, we proposed an EEG-based alertness estimation system with automatic feature selection mechanism. The independent component analysis (ICA) is used first to decompose the measured electroencephalogram (EEG). Then, a time-frequency analysis is performed to evaluate the time-frequency characteristic of each ICA component. We also proposed a new adaptive feature extracting mechanism for selections of frequency bands and ICA components. Different ranges of the alpha rhythm of subjects can be evaluated by the adaptive feature extracting mechanism according to the correlation coefficient between the ICA time-frequency response and the driving performance. The extracted features are then trained both by linear regression model and self-constructing neuro-fuzzy inference network (SONFIN) for the estimation of driving performance. The training and testing results of SONFIN are 96% and 91%, while the results of linear regression model are 90% and 85%, respectively. It demonstrates that the proposed adaptive feature extracting mechanism can achieve a great performance in alertness estimation with frequency band and component selection.

This paper proposes an algorithm for estimation the illumination of an image for the purpose of color constancy using chromaticity histogram. This method evaluates the color temperature of the light source by detecting the distribution of chromaticity histogram in an image. It has the advantages of high efficiency, good robustness, and no strict assumptions. We subsequently propose to use a multilayer perceptrons trained by backpropagation algorithm to model the nonlinear functional relationship between the chromaticity histogram and coefficients of illuminant functions. The trained neural network can then be used to estimate the spectral power distribution of light source. Finally, we use the trained neural network to estimate spectral power distribution in a finite-dimensional linear model of surface reflectance for color constancy. For performance evaluation, two color-recovery experiments on synthetic images and nature images captured from a still digital camera are performed in this paper. All the results are compared to those of two existing popular algorithms (Max-RGB and Gray-World algorithms).

A fuzzy neural network based on support vector learning mechanism for function approximation is proposed in this paper. Support vector regression (SVR) is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory. SVR has been shown to have robust properties against noise. A novel support-vector-regression based fuzzy neural network (SVRFNN) by integrating SVR technology into FNN is developed. The SVRFNN combines the high accuracy and robustness of support vector regression (SVR) and the efficient human-like reasoning of FNN for function approximation. Experimental results show that the proposed SVFNN for function approximation can achieve good approximation performance with drastically reduced number of fuzzy kernel functions.

Over one-third of protein structures contain metal ions, which are the necessary elements in life systems. Traditionally, structural biologists were used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpreting the function formation and reaction mechanism of enzyme by their structures and observations from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict the protein metal-binding amino acid residues from its sequence information only by neural networks with sliding window-based feature extraction and biological feature encoding techniques. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium), the metal-binding residues are identified by the proposed method with higher than 90% sensitivity and very good accuracy under 5-fold cross validation. With such promising results, it can be extended and used as a powerful methodology for metal-binding characterization from rapidly increasing protein sequences in the future.

This paper presents a new algorithm for detection and compensation of backlight images. The proposed technique attacks the weakness of the conventional backlight image processing methods such as over-saturation, losing contrast and so on. The proposed algorithm consists of two operation phases: detection and compensation phases. In the detection phase, we use the spatial position characteristic and histogram of backlight image to obtain two image indices, which can determine the backlight degree of an image. Fuzzy logic is then used to integrate these two indices into a final backlight index determining the final backlight degree of an image precisely. Second, in the compensation phase, to solve the over-saturation problem that exists usually in conventional image compensation methods, we propose the adaptive compensation-curve scheme to compensate and enhance the brightness of backlight images. The luminance of a backlight image is adjusted according to the compensation curve, which is adapted dynamically according to the backlight degree indicated by the backlight index estimated in the detection phase. The performance of the proposed technique is tested on 100 backlight images covering various kinds of backlight conditions and degrees. The experimental and comparison results clearly show the superiority of the proposed technique.

This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally, and perform 3-D surface reconstruction better than some existing approaches.

Photometric stereo technique deals with the reconstruction of three-dimensional (3-D) shape of an object by using several images of the same surface taken from the same viewpoint but under illuminations from different directions. In this paper, we propose a new photometric stereo scheme based on a new reflectance model and the postnonlinear (PNL) independent components analysis (ICA) method. The proposed nonlinear reflectance model consists of diffuse components and specular components for modeling the surface reflectance of a stereo object in an image. Unlike the previous approaches, these two components are not separated and processed individually in the proposed model. An unsupervised learning adaptation algorithm is developed to estimate the reflectance model based on image intensities. In this algorithm, the PNL ICA method is used to obtain the surface normal on each point of an image. Then, the 3-D surface model is reconstructed based on the estimated surface normal on each point of image by using the enforcing integrability method. Two experiments are performed to assess the performance of the proposed approach. We test our algorithm on synthetically generated images for the reconstruction of surface of objects and on a number of real images captured from the Yale Face Database B. These testing images contain variability due to illumination and varying albedo in each point of surface of human faces. All the experimental results are compared to those of the existing photometric stereo approaches tested on the same images. The results clearly indicate the superiority of the proposed nonlinear reflectance model over the conventional Lambertian model and the other linear hybrid reflectance model.

This work proposes a new fuzzy neutral network (FNN) capable of parameter self-adapting and structure self-constructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. The propsed FNN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model with neural network's learning ability. There are no rules initiated at the beginning and they are created and adapted through the newly propsed on-line independent component analysis (ICA) mixture model and back-propagation algorithm learning processing that performs simultaneous structure and parameter identification. Several experiments covering the areas of system identification and classification are carried out. These results show that the proposed FNN can achieve significant improvements in the convergence speed and prediction accuracy with simpler network structure.

We proposed a new index, which can be used to classify the texture image. Because of the adjustment of image capture device or the distortion of image capture, the texture image may be transformed. Usually those transformations included rotation and scale. The proposed method provides an algorithm to avoid those effects respectively. This approach is the combination of cellular neural networks and principle component analysis neural networks. This fact implies it is a feed-forward neural network, and it does not need any training set.

The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This work describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.

To make good range resolution and accuracy compatible with a high detection capability while maintaining the low average transmitted power, pulse compression processing giving low-range sidelobes is necessary. The traditional algorithms such as the direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter based on three-element Barker code (B13 code) have been developed. Recently, the neural network algorithms were issued. However, the traditional algorithms cannot achieve the requirements of high signal-to-sidelobe ratio and low integrated sidelobe level (ISL), and the normal neural networks such as the backpropagation (BP) network usually produce the extra problems of low convergence speed and are sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network to deal with pulse compression in a radar system is presented. Two different Barker codes are carried out by a six-layer self-constructing neural fuzzy network (SONFIN). Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has significant advantages in noise rejection performance, range resolution ability, and Doppler tolerance, which are superior to the traditional and BP algorithms.

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of Kalman filter nor modeling the maneuvering target, this new algorithm, SONFIN, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural network such as backpropagation (BP) network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural fuzzy network (SONFIN) with supervised learning algorithm. Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.

In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception non-sensitive class and sensitive class. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.

In this paper, a hybrid adaptive fuzzy control (HAFC) system is developed for a wing rock motion system. The design of HAFC system contains three parts: one is an indirect controller, the other is a direct controller and the last is a robust controller. A weighting factor /spl alpha/, which can be adjusted by a tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from the indirect and direct controllers. The robust controller is designed to achieve favorable control performance with a desired robustness. Simulation results demonstrate that the HAFC system can achieve favorable desired tracking performances for unknown the wing rock motion dynamics.

In this paper, a novel two-stage noise removal algorithm to deal with fixed-value impulse noise is proposed. In the first stage, the decision-based recursive adaptive median filter is applied to remove the noise cleanly and keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify pixels of the image into human perception sensitive class and non-sensitive class. A neural network is proposed to enhance the sensitive regions to perform better visual quality. According to the experiment results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions.

This paper points out some errors existing in the aforementioned paper. Unfortunately, it seems that no immediate correction can be done. The authors reply is provided.

Discusses the problem of single-channel speech enhancement in variable noise-level environment. Commonly used, single-channel subtractive-type speech enhancement algorithms always assume that the background noise level is fixed or slowly varying. In fact, the background noise level may vary quickly. This condition usually results in wrong speech/noise detection and wrong speech enhancement process. In order to solve this problem, we propose a subtractive-type speech enhancement scheme. This new enhancement scheme uses the RTF (refined time-frequency parameter)-based RSONFIN (recurrent self-organizing neural fuzzy inference network) algorithm we developed previously to detect the word boundaries in the condition of variable background noise level. In addition, a new parameter (MiFre) is proposed to estimate the varying background noise level. Based on this parameter, the noise level information used for subtractive-type speech enhancement can be estimated not only during speech pauses, but also during speech segments. This new subtractive-type enhancement scheme has been tested and found to perform well, not only in variable background noise level condition, but also in fixed background noise level condition.

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking ? To solve this problem, neural network and fuzzy algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size, and the fuzzy algorithms are not easy to partition the parameters. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. Without having to change the structure of Kalman filter for modeling the maneuvering target, SONFIN algorithm, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.

Proposes a noninvasive measurement method for tracking the tendency of mean arterial pressure (MAP) in the radial artery. The designed system consists of a tonometer, a micro syringe device, and a model-based fuzzy logic controller. The modified flexible diaphragm tonometer detects the continuous blood pressure waveform and vessel volume pulse. A precise mathematical model describing the interaction between the tonometer and artery is derived. To reach accurate measurement without distortion, an optimal controller is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the micro syringe device. Simulation results show that, for the real physiologic MAP with changing rates up to 20 or -20 mm-Hg/minute, the optimal controller can beat-to-beat adjust the tonometer's chamber pressure to follow the tendency of MAP accurately.

This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.

Proposes a systematic and theoretically sound way to design a global optimal discrete-time fuzzy controller to control and stabilize a nonlinear discrete-time fuzzy system with finite or infinite horizon (time). A linear-like global system representation of a discrete-time fuzzy system is first proposed by viewing such a system in a global concept and unifying the individual matrices into synthetic matrices. Then, based on this kind of system representation, a discrete-time optimal fuzzy control law which can achieve a global minimum effect is developed theoretically. A nonlinear two-point boundary-value-problem (TPBVP) is derived as a necessary and sufficient condition for the nonlinear quadratic optimal control problem. To simplify the computation, a multi-stage decomposition of the optimization scheme is proposed, and then a segmental recursive Riccati-like equation is derived. Moreover, in the case of time-invariant fuzzy systems, we show that the optimal controller can be obtained by just solving discrete-time algebraic Riccati-like equations. Based on this, several fascinating characteristics of the resultant closed-loop fuzzy system can easily be elicited. The stability of the closed-loop fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal closed-loop fuzzy system can not only be guaranteed to be exponentially stable, but also stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant closed-loop fuzzy system possesses an infinite gain margin, i.e. its stability is guaranteed no matter how large the feedback gain becomes. An example is given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proven stability properties.

In this paper, we propose a global optimal fuzzy tracking controller, implemented by fuzzily blending the individual local fuzzy tracking laws, for continuous and discrete-time fuzzy systems with the aim of solving, respectively, the continuous and discrete-time quadratic tracking problems with moving or model-following targets under finite or infinite horizon (time). The differential or recursive Riccati equations, and more, the differential or difference equations in tracing the variation of the target, are derived. Moreover, in the case of time-invariant fuzzy tracking systems, we show that the optimal tracking controller can be obtained by just solving algebraic Riccati equations and algebraic matrix equations. Grounding on this, several fascinating characteristics of the resultant closed-loop continuous or discrete time-invariant fuzzy tracking systems can be elicited easily. The stability of both closed-loop fuzzy tracking systems can be ensured by the designed optimal fuzzy tracking controllers. The optimal closed-loop fuzzy tracking systems cannot only be guaranteed to be exponentially stable, but also be stabilized to any desired degree. Moreover, the resulting closed-loop fuzzy tracking systems possess infinite gain margin; that is, their stability is guaranteed no matter how large the feedback gain becomes. Two examples are given to illustrate the performance of the proposed optimal fuzzy tracker design schemes and to demonstrate the proved stability properties.

This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise in cars. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed and sets fixed thresholds to find the boundary of word signal. In fact, the background noise level in cars varies in the procedure of recording due to speed change and moving environment, and some thresholds should be tuned according to the variation of background noise level. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. To solve this problem, we propose a minimum mel-scale frequency band (MiMSB) parameter which can estimate the varying background noise level in cars by adaptively choosing one band with minimum energy from the mel-scale frequency bank. With the MiMSB parameter, some preset thresholds used to find the boundary of word signal are no longer fixed in all the recording intervals. These thresholds will be tuned according to the MiMSB parameter. We also propose an enhanced time frequency (ETF) parameter by extending the time-frequency (TF) parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise. The ETF parameter can extract useful frequency information by choosing some bands of the mel-scale frequency bank. Based on the MiMSB and ETF parameters, we finally propose a new robust algorithm for word boundary detection in variable noise-level environment. The new algorithm has been tested over a variety of noise conditions in cars and has been found to perform well not only under variable background noise level condition, but also under fixed background noise level condition. The new robust algorithm using the MiMSB and ETF parameters achieved higher recognition rate than the TF-based robust algorithm, which has been shown to outperform several commonly used algorithms, by about 5% in variable background noise level condition. It also reduced the recognition error rate due to endpoint detection to 25%, compared to an average of 34% obtained with the TF-based robust algorithm.

Future broadband integrated services networks based on asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in ATM networks. Among general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but does not attain the high system utilization of the deadline driven algorithm. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is a combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for schedulability testing of mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines a GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way of carrying out mixed scheduling in ATM networks.

In this paper, a new measurement system for the noninvasive monitoring of the continuous blood pressure waveform in the radial artery is presented. The proposed system comprises a model-based fuzzy logic controller, an arterial tonometer and a micro syringe device. The flexible diaphragm tonometer registers the continuous blood pressure waveform. To obtain accurate measurement without distortion, the tonometer's mean chamber pressure must be kept equal to the mean arterial pressure (MAP), the so-called optimal coupling condition, such that the arterial vessel has the maximum compliance. Since the MAP cannot be measured directly, to keep the optimal coupling condition becomes a tracking control problem with unknown desired trajectory. To solve this dilemma, a model-based fuzzy logic controller is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the micro syringe device. The proposed controller consists of a model-based predictor and a synthetic fuzzy logic controller (SFLC). The model-based predictor estimates the MAPs changing tendency based on the identified arterial pressure-volume model.

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? To solve this problem, neural network and fuzzy algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size, and the fuzzy algorithms are not easy to partition the parameters. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. Without having to change the structure of Kalman filter nor modeling the maneuvering target, SONFIN algorithm, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.

Proposes a noninvasive measurement method for tracking the tendency of mean arterial pressure in the radial artery. The designed system consists of a tonometer, a micro syringe device, and a model-based fuzzy logic controller. The proposed control system consists of a linear predictor, and a synthetic fuzzy logic controller. The design of the fuzzy rules in each subcontroller is based on the oscillometric principle saying that the arterial vessel has the maximum compliance when the detected vessel volume pulse reaches its maximum amplitude.

Public security has become an important issue everywhere. In particular, the safe manipulation and control of various machines and vehicles has gained special attention such that the authorities keep emphasizing the strict training and censoring of human operators. Currently, such training and censoring processes usually rely on the actual machines, equipment, or vehicles in the real sites. This not only has high demands in space, time and cost, but also causes another phase of public security problem. In connection with this, the world-wide trend is to tackle the above dilemma by using virtual reality (VR). However, the current VR research or products are more mature in the software display part of VR. How to combine a 3D VR display with a motion platform to achieve the aforementioned training and censoring purposes is an important research issue. The paper focuses on this research issue, and the goal is to develop a multipurpose virtual-reality-based motion simulation system to meet the requirements of public security in training and censoring of human operators.

In this paper, we propose a new scheme to analyze the spectral structure of speech signals for fundamental frequency estimation. First, we propose a pitch measure to detect the harmonic characteristics of voiced sounds on the spectrum of a speech signal. This measure utilizes the properties that there are distinct impulses located at the positions of fundamental frequency and its harmonics, and the energy of voiced sound is dominated by the energy of these distinct harmonic impulses. The spectrum can be obtained by the fast Fourier transform (FFT) however, it may be destroyed when the speech is interfered with by additive noise. To enhance the robustness of the proposed scheme in noisy environments, we apply the joint time-frequency analysis (JTFA) technique to obtain the adaptive representation of the spectrum of speech signals. The adaptive representation can accurately extract important harmonic structure of noisy speech signals at the expense of high computation cost. To solve this problem, we further propose a fast adaptive representation (FAR) algorithm, which reduces the computation complexity of the original algorithm by 50%. The performance of the proposed fundamental-frequency estimation scheme is evaluated on a large database with or without additive noise. The performance is compared to that of other approaches on the same database. The experimental results show that the proposed scheme performs well on clean speech and is robust in noisy environments.

This paper presents an error shaping technique for line spectrum frequency (LSF) vector quantization. The error shaping technique based on the weighted logarithm spectral distortion (WLSD) measure can be used for shaping the spectral distortion distribution of quantization error into any different curve depending on what kind of weighting function is used. However, the high computational complexity of the WLSD measure deters this error shaping technique from practical use. To solve this problem, we approximate the WLSD measure by the quadratically weighted measure or the weighted mean squared error (WMSE) measure and propose an optimal error shaping technique of LSF vector quantization. In this proposed error shaping technique, the optimal WMSE weights (i.e., the optimal weights of LSF parameters) are determined based on the theoretical analysis of the WLSD measure. Three experiments are performed to check the performance of the proposed error shaping technique. One experiment is set up by incorporating human perception into the LSF quantization and another is set up by emphasizing the human-sensitivity frequency band in lower frequency bandwidth 0-3 kHz. In the third experiment, we apply the proposed error shaping technique to the LSF quantization of a CELP coder to test how it affects the overall speech quality in an actual speech coding algorithm.

Two noisy speech processing problems-speech enhancement and noisy speech recognition-are dealt with. The technique we focus on is by using the filtering approach; a novel filter, the recurrently adaptive fuzzy filter (RAFF), is proposed and applied to these two problems. The speech enhancement is based on adaptive noise cancellation with two microphones, where the RAFF is used to eliminate the noise corrupting the desired speech signal in the primary channel. As to the noisy speech recognition, the RAFF is used to filter the noise in the feature domain of speech signals. The RAFF is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. As compared to other existing nonlinear filters, three major advantages of the RAFF are observed: 1) a priori knowledge can be incorporated into the RAFF, which makes the fusion of numerical data and linguistic information possible; 2) owing to the dynamic property of the RAFF, the exact lagged order of the input variables need not be known in advance; 3) no predetermination, like the number of hidden nodes, must be given since the RAFF can find its optimal structure and parameters automatically Several examples on adaptive noise cancellation and noisy speech recognition problems using the RAFF are illustrated to demonstrate the performance of the RAFF.

This paper proposes a new noninvasive measurement method for tracking the tendency of mean arterial pressure (MAP) in the radial artery. The designed system consists of a tonometer, a microsyringe device, and a model-based fuzzy logic controller. The modified flexible diaphragm tonometer is to detect the continuous blood pressure waveform and vessel volume pulse. A precise mathematical model describing the interaction between the tonometer and artery is derived. To reach accurate measurement without distortion, a model-based fuzzy logic control system is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the microsyringe device. The proposed control system consists of a linear predictor, a Kalman filter, and a synthetic fuzzy logic controller (SFLC). Simulation results show that, for the real physiologic MAP with changing rates up to 20 or -20 mm-Hg/minute, the model-based SFLC can beat-to-beat adjust the tonometer's chamber pressure to follow the tendency of MAP accurately.

The stability analysis of the learning rate for a two-layer neural network (NN) is discussed first by minimizing the total squared error between the actual and desired outputs for a set of training vectors. The stable and optimal learning rate, in the sense of maximum error reduction, for each iteration in the training (back propagation) process can therefore be found for this two-layer NN. It has also been proven in this paper that the dynamic stable learning rate for this two-layer NN must be greater than zero. Thus it Is guaranteed that the maximum error reduction can be achieved by choosing the optimal learning rate for the next training iteration. A dynamic fuzzy neural network (FNN) that consists of the fuzzy linguistic process as the premise part and the two-layer NN as the consequence part is then illustrated as an immediate application of our approach. Each part of this dynamic FNN has its own learning rate for training purpose. A genetic algorithm is designed to allow a more efficient tuning process of the two learning rates of the FNN. The objective of the genetic algorithm is to reduce the searching time by searching for only one learning rate, which is the learning rate of the premise part, in the FNN. The dynamic optimal learning rates of the two-layer NN can be found directly using our innovative approach. Several examples are fully illustrated and excellent results are obtained for the model car backing up problem and the identification of nonlinear first order and second order systems.

This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information. Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent self-organizing neural fuzzy inference network (RSONFIN). Since RSONPIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed. Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition, It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition.

We propose a global optimal fuzzy tracking controller for continuous time-invariant fuzzy tracking system to trace moving or model-following targets under an infinite-horizon (time) quadratic performance index. We also demonstrate that the stability of the closed-loop fuzzy tracking systems can be ensured by the designed optimal fuzzy tracking controllers. One example is given to illustrate the proposed optimal fuzzy tracker design scheme and to show the proved stability property.

Among various kinds of speech features, the two-dimensional (2-D) cepstrum (TDC) is a special one, which can simultaneously represent several types of information contained in the speech waveform: static and dynamic features, as well as global and fine frequency structures. Analysis results show that the coefficients located at lower indexes portion of the TDC matrix seem to be more significant than others. Hence, to represent an utterance only some TDC coefficients need to be selected to form a feature vector instead of the sequence of feature vectors. It has the advantages of simple computation and less storage space. However, our experiments show that the selection of TDC coefficients is quite sensitive to background noise. In order to solve this problem, we propose the GA-based M-TDC (modified TDC) method in this paper to improve the representativeness and robustness of the selected TDC coefficients in noisy environments. The M-TDC differs from the standard TDC by the use of filters to remove the noise components. Furthermore, in the GA-based M-TDC method, we apply the genetic algorithms (GAs) to find the robust coefficients in the M-TDC matrix. From the experiments with five noise types, we find that the GA-based M-TDC method has better recognition results than the original TDC approaching noisy environments.

This paper addresses the problem of automatic word boundary detection in the presence of noise. We first propose an adaptive time-frequency (ATF) parameter for extracting both the time and frequency features of noisy speech signals. The ATF parameter extends the TF parameter proposed by Junqua et al. (1994) from single band to multiband spectrum analysis, where the frequency bands help to make the distinction of speech and noise signals clear. The ATF parameter can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. The ATF parameter increased the recognition rate by about 3% of a TF-based robust algorithm which has been shown to outperform several commonly used algorithms for word boundary detection in the presence of noise. The ATF parameter also reduced the recognition error rate due to endpoint detection to about 20%. Based on the ATF parameter, we further propose a new word boundary detection algorithm by using a neural fuzzy network (called SONFIN) for identifying islands of word signals in a noisy environment. Due to the self-learning ability of SONFIN, the proposed algorithm avoids the need of empirically determining thresholds and ambiguous rules in normal word boundary detection algorithms. As compared to normal neural networks, the SONFIN can always find itself an economic network size in high learning speed. Our results also showed that the SONFIN's performance is not significantly affected by the size of training set. The ATF-based SONFIN achieved higher recognition rate than the TF-based robust algorithm by about 5%. It also reduced the recognition error rate due to endpoint detection to about 10%, compared to an average of approximately 30% obtained with the TF-based robust algorithm, and 50% obtained with the modified version of the Lamel et al. (1981) algorithm.

In this paper, we present a global optimal and stable fuzzy controller design method for both continuous- and discrete-time fuzzy systems under both finite and infinite horizons. First, a sufficient condition is proposed which indicates that the global optimal effect can be achieved by the fuzzily combined local optimal controllers. Based on this sufficient condition, we derive a local concept approach to designing the optimal fuzzy controller by applying traditional linear optimal control theory. The stability of the entire closed-loop continuous fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal feedback continuous fuzzy system can not only be guaranteed to be exponentially stable, but also be stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant feedback continuous fuzzy system possesses an infinite gain margin; that is, its stability is guaranteed no matter how large the feedback gain becomes. Two examples are given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proved stability properties.

We propose a design method for a global optimal fuzzy controller to control and stabilize a continuous fuzzy system with free- or fixed-end point under finite or infinite horizon (time). A linear-like global system representation of continuous fuzzy system is first proposed by viewing a continuous fuzzy system in global concept and unifying the individual matrices into synthetical matrices. Based on this, the optimal control law which can achieve global minimum effect is developed theoretically. The nonlinear segmental two-point boundary-value problem is derived for the finite-horizon problem and a forward Riccati-like differential equation for the infinite-horizon problem. The stability of the closed-loop fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal closed-loop fuzzy system cannot only be guaranteed to be exponentially stable, but also be stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant closed-loop fuzzy system possesses an infinite gain margin.

Satellite sensor images usually contain many complex factors and mixed pixels, so a high classification accuracy is not easy to attain. Especially, for a nonhomogeneous region, gray values of satellite sensor images vary greatly and thus, direct statistic gray values fail to do the categorization task correctly. The goal of this paper is to develop a cascaded architecture of neural fuzzy networks with feature mapping (CNFM) to help the clustering of satellite sensor images. In the CNFM, a Kohonen's self-organizing feature map (SOFM) is used as a preprocessing layer for the reduction of feature domain, which combines original multi-spectral gray values, structural measurements from co-occurrence matrices, and spectrum features from wavelet decomposition. In addition to the benefit of dimensional reduction of feature space, Kohonen's SOFM can remove some noisy areas and prevent the following training process from being overoriented to the training patterns. The condensed measurements are then forwarded into a neural fuzzy network, which performs supervised learning for pattern classification. The proposed cascaded approach is an appropriate technique for handling the classification problem in areas that exhibit large spatial variation and interclass heterogeneity (e.g., urban-rural infringing areas). The CNFM is a general and useful structure that can give us favorable results in terms of classification accuracy and learning speed. Experimental results indicate that our structure can retain high accuracy of classification (90% in average), while the training time is substantially reduced if our system is compared to the commonly used backpropagation network.

A physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings. The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN. After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The "virtual string" corresponding to the SRN can respond to different "plucks" just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello "A"-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed.

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Proposes a recurrent learning algorithm for designing the controllers of continuous dynamical systems in optimal control problems. The controllers are in the form of unfolded recurrent neural nets embedded with physical laws from classical control techniques. The learning algorithm is characterized by a double forward-recurrent-loops structure for solving both temporal recurrent and structure recurrent problems. The first problem results from the nature of general optimal control problems, where the objective functions are often related to (evaluated at) some specific time steps or system states only, causing missing learning signals at some steps or states. The second problem is due to the high-order discretization of continuous systems by the Runge-Kutta method that we perform to increase accuracy. This discretization transforms the system into several identical interconnected subnetworks, like a recurrent neural net expanded in the time axis. Two recurrent learning algorithms with different convergence properties are derived; first- and second-order learning algorithms. Their computations are local and performed efficiently as net signal propagation. We also propose two new nonlinear control structures for the 2D guidance problem and the optimal PI control problem. Under the training of the recurrent learning algorithms, these controllers can be easily tuned to be suboptimal for given objective functions. Extensive computer simulations show the controllers' optimization and generalization abilities.

Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.

In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplane need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several examples are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.

Temperature measurement and control are two difficult problems in the rapid thermal processing (RTP) system. For many applications such as rapid thermal processing chemical vapor deposition (RTCVD) and rapid thermal oxidation (RTO), large changes in wafer emissivity can occur during film growing, leading to erroneous temperature measurements with a single wavelength pyrometer. The error in the inferred temperature will affect the temperature control of the RTP system. In order to correct the temperature reading of the pyrometer, a neural fuzzy network is used to predict the emissivity changes for the compensation of measured temperature. As for the temperature control, to overcome ill performance of the temperature tracking system due to the inaccuracy of the identified model, another neural fuzzy network is used in the RTP system for learning inverse control simultaneously. The key advantage of neural fuzzy approach over traditional ones lies on that the approach does not require a mathematical description of the system while performing pyrometer correction and temperature control. Simulation results show that the adopted neural fuzzy networks can not only correct the pyrometer reading accurately, but also be able to track a temperature trajectory very well.

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

This paper proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfill the reinforcement learning task. Structurely, the TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the TDGAR learning system. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. This can usually accelerate the GA learning since an external reinforcement signal may only be available at a time long after a sequence of actions have occurred in the reinforcement learning problems. By defining a simple external reinforcement signal, the TDGAR learning system can learn to produce a series of small perturbations to convert chaotic oscillations of a chaotic system into desired regular ones with a periodic behavior. The proposed method is an adaptive search for the optimum control technique. Computer simulations on controlling two chaotic systems, i.e., the Hénon map and the logistic map, have been conducted to illustrate the performance of the proposed method.

This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme.

Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.

In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.

This paper proposes Runge Kutta neural networks (RKNNs) for identification of continuous-time nonlinear systems. These networks are constructed according to the Runge Kutta approximation method. The RKNNs can thus precisely model continuous-time systems and do long-term prediction of system state trajectories. The RKNNs model continuous-time systems can incorporate available continuous relationship (physical laws) of the identified systems into their structures directly. Also, they are insensitive to the size of sampling interval in prediction. We also show theoretically the superior generalization and long-term prediction capability of the RKNNs over the normal neural networks. A class of novel recursive least square algorithms, called nonlinear recursive least square learning algorithms, are developed for the RKNNs. Computer simulations demonstrate the proved properties of the RKNNs.

A self-constructing neural fuzzy inference network (SONFIN) with online learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing neural network learning ability. There are no rules initially in the SONFIN. They are created and adapted as online learning proceeds via simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to an aligned clustering-based algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially. Afterwards, some additional significant terms selected via a projection-based correlation measure for each rule will be added to the consequent part incrementally as learning proceeds. The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN. In the parameter identification, the consequent parameters are tuned optimally by either least mean squares or recursive least squares algorithms and the precondition parameters are tuned by a backpropagation algorithm. To enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved.

Proposes a four-layered adaptive fuzzy command acquisition network (AFCAN) for adaptively acquiring fuzzy command via interactions with the user or environment. It can catch the intended information from a sentence (command) given in natural language with fuzzy predicates. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action. The proposed AFCAN has three important features. First, we can make no restrictions whatever on the fuzzy command input, which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar, and syntactic structure, Second, the linguistic information of an action is learned adaptively and it is represented by fuzzy numbers based on /spl alpha/-level sets. Third, the network can learn during the course of performing the task. The AFCAN can perform off-line as well as online learning. For the off-line learning, the mutual-information (MI) supervised learning scheme and the fuzzy backpropagation (FBP) learning scheme are employed when the training data are available in advance. The former learning scheme is used to learn meaningful semantic actions and the latter learn linguistic information. The AFCAN can also perform online learning interactively when it is in use for fuzzy command acquisition. For the online learning, the MI-reinforcement learning scheme and the fuzzy reinforcement learning scheme are developed for the online learning of meaningful actions and linguistic information, respectively. An experimental system is constructed to illustrate the performance and applicability of the proposed AFCAN.

This paper proposes Runge-Kutta neural networks (RKNNs) for identification of unknown dynamical systems described by ordinary differential equations (i.e., ordinary differential equation or ODE systems) with high accuracy. These networks are constructed according to the Runge-Kutta approximation method. The main attraction of the RKNNs is that they precisely estimate the changing rates of system states (i.e., the right-hand side of the ODE x =f(x)) directly in their subnetworks based on the space-domain interpolation within one sampling interval such that they can do long-term prediction of system state trajectories. We show theoretically the superior generalization and long-term prediction capability of the RKNNs over the normal neural networks. Two types of learning algorithms are investigated for the RKNNs, gradient-and nonlinear recursive least-squares-based algorithms. Convergence analysis of the learning algorithms is done theoretically. Computer simulations demonstrate the proved properties of the RKNNs.

This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data.

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically.

This paper proposes a four-layered fuzzy language acquisition network (FLAN) for acquiring fuzzy language. It can catch the intended information from a sentence (command) spoken in natural language with fuzzy terms. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action (for example, the phrase “move forward” represents the meaningful semantic action and the phrase “very high speed” represents the linguistic information in the fuzzy command “Move forward in a very high speed.”). The proposed FLAN has two important features. First, we can make no restrictions whatever on the fuzzy language input which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar and syntactic structure. Second, the linguistic information of an action is learned automatically and it is represented by fuzzy numbers based on α-level sets. A supervised learning scheme is proposed to train the FLAN on fuzzy training data. This learning scheme consists of the mutual-information (MI) supervised learning algorithm for learning meaningful semantic actions, and the fuzzy backpropagation (FBP) learning algorithm for learning linguistic information. An experimental system is constructed to illustrate the performance and applicability of the proposed FLAN.

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using temporal difference prediction, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. An ART-based reinforcement structure/parameter-learning algorithm is developed for constructing the RFALCON dynamically. During the learning process, structure and parameter learning are performed simultaneously. RFALCON can construct a fuzzy control system through a reward/penalty signal. It has two important features; it reduces the combinatorial demands of system adaptive linearization, and it is highly autonomous.

A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use alpha-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.

This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayer connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning Control Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCON-ART can on-line partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The proposed learning scheme has been successfully used to control two unstable nonlinear systems. They are the seesaw system and the inverted wedge system.

A neural fuzzy system learning with linguistic teaching signals is proposed. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. First, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use /spl alpha/-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, two kinds of learning schemes are developed for the proposed system: fuzzy supervised learning and fuzzy reinforcement learning. Simulation results are presented to illustrate the performance and applicability of the proposed system.>

The idea of a Hopfield network is based on the Ising spin glass model in which each spin has only two possible states: up and down. By introducing stochastic factors into this network and performing a simulated annealing process on it, it becomes a Boltzmann machine which can escape from local minimum states to achieve the global minimum. This paper generalizes the above ideas to multi-value case based on the XY spin glass model in which each spin can be in any direction in a plane. Simply using the gradient descent method and the analog Hopfield network, two different analog connectionist structures and their corresponding evolving rules are first designed to transform the XY spin glass model to distributed computational models. These two analog computational models are single-layered connectionist structures and multi-layered Hopfield analog networks. The latter network eases the node (neuron) computational requirement of the former at the expense of more neurons and connections. With the proposed evolving rules, the proposed models evolve according to a predefined Hamiltonian (energy function) which will decrease until it reaches a (perhaps local) minimum. Since these two structures can easily get stuck in local minima, a multi-valued Boltzmann machine is proposed which adopts the discrete planar spin glass model for the local minimum problem. Each neuron in the multi-valued Boltzmann machine can only take n discrete directions (states). The stochastic simulated annealing method is introduced to the evolving rules of the multi-valued Boltzmann machine to solve the local minimum problem. The multi-valued Boltzmann machine can be applied to the mobile robot navigation problem by defining proper artificial magnetic field on the traverse terrain. This new artificial magnetic field approach for the mobile robot navigation problem has shown to have several advantages over existing graph search and potential field techniques.>

This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC's), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC performs as a fuzzy predictor, and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, both structure learning and parameter learning are performed simultaneously in the two NN-FLC's using the fuzzy similarity measure. The proposed RNN-FLCS can construct a fuzzy logic control and decision-making system automatically and dynamically through a reward/penalty signal or through very simple fuzzy information feedback such as "high," "too high," "low," and "too low." The proposed RNN-FLCS is best applied to the learning environment, where obtaining exact training data is expensive. It also preserves the advantages of the original NN-FLC, such as the ability to find proper network structure and parameters simultaneously and dynamically and to avoid the rule-matching time of the inference engine. Computer simulations were conducted to illustrate its performance and applicability.>

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model.>

Efficient parallel algorithms for computing the kinematics, dynamics, and Jacobian of manipulators and their corresponding inverses are discussed and analyzed, and their characteristics are identified based on the type and degree of parallelism, uniformity of the operations, fundamental operations, data dependencies, and communication requirements. Most of the algorithms for robotic computations possess highly regular properties and some common structures, especially the linear recursive structure. They are well-suited to be implemented on a single-instruction-stream-multiple-data-stream (SIMD) computer with reconfigurable interconnection networks. A reconfigurable dual-network SIMD machine with internal direct feedback that best matches these characteristics has been designed. To achieve high efficiency in the computation of robotics algorithms on the proposed parallel machine, a generalized cube interconnection network is proposed. A centralized network switch control scheme is developed to support the pipeline timing of this machine. To maintain high reliability in the overall system, a fault-tolerant generalized cube network is designed to improve the original network.>