State-of-the-art attention-based models in Sequential Recommendation (SR) often struggle to accurately model the recency effect, where recent interactions disproportionately influence future behavior. Empirically, conventional learnable positional embeddings exhibit erratic fluctuations, failing to guarantee necessary influence decay over time. To address this, we propose POSITIVE4Rec, a model-agnostic framework injecting Positional Inductive Bias (PIB) directly into self-attention. A novel attention reweighting module imposes an adaptive monotonic trend on the attention distribution. Acting as a soft inductive bias rather than a rigid constraint, it prioritizes temporal proximity while retaining flexibility. This plug-and-play mechanism integrates seamlessly into diverse SR architectures. Extensive benchmark experiments show POSITIVE4Rec revitalizes state-of-the-art models with significant performance gains. Code is available at https://anonymous.4open.science/r/POSITIVE4Rec.

False alarm suppression is becoming increasingly important as it directly impacts the reliability and efficiency of synthetic aperture radar (SAR) image detection systems. previous methods for false alarm suppression have focused primarily on identifying the motion properties of targets and removing the embedded noise. However, SAR images are captured in a single band, which means they lack continuous bands and dynamic information. In addition, noise removal often results in a significant loss of detail in the image. In this paper, we innovatively propose a classification-based false alarm suppression framework for SAR object detection, avoiding the need for motion identification and noise removal. In practice, we first train a classification network to categorize the SAR image slices into ocean, land, and offshore scenes. Based on the classification results, we then dynamically adjust the Intersection Over Union (IoU) threshold of Non-Maximum Suppression (NMS) in different scenes. Experimental results on a newly large multi-class target SAR dataset, MSAR-1.0, show that the false alarm rate decreased from 21% to 13%.

Different from natural videos, where artifacts distributed evenly, the artifacts of compressed screen content videos mainly occur in the edge areas. Besides, these videos often exhibit abrupt scene switches, resulting in noticeable distortions in video reconstruction. Existing multiple-frame models using a fixed range of neighbor frames face challenges in effectively enhancing frames during scene switches and lack efficiency in reconstructing high-frequency details. To address these limitations, we propose a novel method that effectively handles scene switches and reconstructs high-frequency information. In the feature extraction part, we develop long-term and short-term feature extraction streams, in which the long-term feature extraction stream learns the contextual information, and the short-term feature extraction stream extracts more related information from shorter input to assist the long-term stream to handle fast motion and scene switches. To further enhance the frame quality during scene switches, we incorporate a similarity-based neighbor frame selector before feeding frames into the short-term stream. This selector identifies relevant neighbor frames, aiding in the efficient handling of scene switches. To dynamically fuse the short-term feature and long-term features, the muti-scale feature distillation focuses on adaptively recalibrating channel-wise feature responses to achieve effective feature distillation. In the reconstruction part, a high-frequency reconstruction block is proposed for guiding the model to restore the high-frequency components. Experimental results demonstrate the significant advancements achieved by our proposed Long Short-term Fusion by Multi-Scale Distillation (LSFMD) method in enhancing the quality of compressed screen content videos, surpassing the current state-of-the-art methods.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-art performance on the largest clean dataset, PulseDB. The rU-Net architecture, a fusion of U-Net and ResNet, enhances both generalization and feature extraction accuracy. Accurate multi-scale feature capture is facilitated by short-time Fourier transform (STFT) time-frequency distributions and multi-head attention mechanisms, allowing data-driven feature selection. The inclusion of demographic parameters as supervisory information further elevates performance. On the calibration-based dataset, our model excels, achieving outstanding accuracy (SBP MAE ± std: 4.49 ± 4.86 mmHg, DBP MAE ± std: 2.69 ± 3.10 mmHg), surpassing AAMI standards and earning a BHS Grade A rating. Addressing the challenge of calibration-free data, we propose a fine-tuning-based transfer learning approach. Remarkably, with only 10% data transfer, our model attains exceptional accuracy (SBP MAE ± std: 4.14 ± 5.01 mmHg, DBP MAE ± std: 2.48 ± 2.93 mmHg). This study sets the stage for the development of highly accurate and reliable wearable cuffless blood pressure monitoring devices.

Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher. To address these challenges, in this work, we develop an adaptive cross-modal fusion strategy for unsupervised multimodal medical image fusion. Specifically, we propose a novel lightweight cross Transformer based on cross multi-axis attention mechanism. It includes cross-window attention and cross-grid attention to mine and integrate both local and global interactions of multimodal features. The cross Transformer is further guided by a spatial adaptation fusion module, which allows the model to focus on the most relevant information. Moreover, we design a special feature extraction module that combines multiple gradient residual dense convolutional and Transformer layers to obtain local features from coarse to fine and capture global features. The proposed strategy significantly boosts the fusion performance while minimizing computational costs. Extensive experiments, including clinical brain tumor image fusion, have shown that our model can achieve clearer texture details and better visual quality than other state-of-the-art fusion methods.

Depression is a global health issue that necessitates severity identification for effective treatment. Although early detection and monitoring of mental abnormalities are crucial for mitigating adverse conditions, standard diagnostic approaches for depression, including self-rating scales and clinician-based interviews, fall short of meeting these needs. Wearable sensors offer an opportunity for unobtrusive evaluation of patients' status throughout their daily life, but it presents challenges in feature extraction and depression severity prediction. Studies have shown that eye gaze dynamics in term of 3-D rotations are a good indicator of depression. However, their traditional representation in Euler angles is computationally inefficient and overlooks the channel-wise interrelations, thereby reducing their discriminative power in medical diagnosis. This paper proposes a novel approach to address these challenges. It uses a window-based quaternion principal component analysis algorithm to extract features of eye gaze dynamics, preserving the channel-wise interrelations and temporal information. The proposed algorithm was tested and validated using a benchmark depression dataset from previous studies. Results showed that the algorithm achieved higher accuracy in depression severity prediction with a root mean square error of 5.08, outperforming existing state-of-the-art prediction methods that only use visual data. The results were further analyzed and discussed, providing valuable insights into the effectiveness and potential applications of wearable sensor for mental health diagnostics.

Compressed screen content videos often exhibit artifacts in edge areas and suffer from distortions during scene switches, where content abruptly changes between frames. Existing multi-frame models, which use a fixed range of neighbor frames, struggle with these switches. To address this, we propose a novel method that effectively handles scene switches. Our approach utilizes Long-term Feature Extraction (LFE) to capture contextual information, while the Frame Similarity-based Short-term Feature Extraction (FSFE) focuses on texture information to manage fast motion and scene switches. In FSFE, a Similarity-based Neighbor Frame Selector (SNFS) is designed to choose relevant neighbor frames for the short-term stream, enhancing the quality of scene switch frames. To fuse short-term and long-term features adaptively, we introduce a local-spatial and global-channel attention module, which recalibrates spatial and channel-wise feature responses. Experimental results show that our Frame Similarity-Based via Adaptive Long Short-Term Fusion (FSLST) method significantly improves the quality of compressed videos, outperforming current state-of-the-art methods.

Abstract Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

To drive upgrades of Depth-Image-Based Rendering (DIBR) algorithms, depth image refinement, etc., quality assessment models for DIBR-synthesized images in 3D video systems are developed. However, most of these models could not effectively evaluate distortion due to irregular stretching (e.g., crumbling), which is more complex and common than black holes and regular stretching (e.g., horizontal stretching) in synthesized images. To make an attempt at this issue, a new quality assessment method is proposed for DIBR views. First, feature point matching and affine transformation are adopted to remove and compensate for the global object shift between reference and synthesized view images. Second, multi-scale discrete wavelet transform is utilized to extract multi-scale structure distortion; gradient magnitude similarity is further integrated to highlight the distortion features; morphological open operation and median filtering are adopted to exclude perceptually unimportant features. Third, scores are obtained by standard deviation pooling on distortion feature maps for each wavelet scale and sub-band. Experimental results demonstrate that our proposed model outperforms the state-of-the-art handcrafted feature-based DIBR-synthesized image quality assessment models on IETR database, and performs the best on average on IETR and IRCCyN/IVC databases. The source code will be available athttps://github.com/House-yuyu/DIBR_IQA.

Abstract Incomplete Multi-View Clustering (IMVC) attempts to give an optimal clustering solution for incomplete multi-view data that suffer from missing instances in certain views. However, most existing IMVC methods still have various drawbacks in practical applications, such as arbitrary incomplete scenarios cannot be handled; the computational cost is relatively high; most valuable nonlinear relations among samples are often ignored; complementary information among views is not sufficiently exploited. To address the above issues, in this paper, we present a novel and flexible unified graph learning framework, called Multiple Kernel-based Anchor Graph coupled low-rank Tensor learning for Incomplete Multi-View Clustering (MKAGT_IMVC), whose goal is to adaptively learn the optimal unified similarity matrix from all incomplete views. Specifically, according to the characteristics of incomplete multi-view data, MKAGT_IMVC innovatively improves an anchor selection strategy. Then, a novel cross-view anchor graph fusion mechanism is introduced to construct multiple fused complete anchor graphs, which captures more the intra-view and inter-view nonlinear relations. Moreover, a graph learning model combining low-rank tensor constraint and consensus graph constraint is developed, where all fused complete anchor graphs are regarded as prior knowledge to initialize this model. Extensive experiments conducted on eight incomplete multi-view datasets clearly show that our method delivers superior performance relative to some state-of-the-art methods in terms of clustering ability and time-consuming.

In linear registration, a floating image is spatially aligned with a reference image after performing a series of linear metric transformations. Additionally, linear registration is mainly considered a preprocessing version of nonrigid registration. To better accomplish the task of finding the optimal transformation in pairwise intensity-based medical image registration, in this work, we present an optimization algorithm called the normal vibration distribution search-based differential evolution algorithm (NVSA), which is modified from the Bernstein search-based differential evolution (BSD) algorithm. We redesign the search pattern of the BSD algorithm and import several control parameters as part of the fine-tuning process to reduce the difficulty of the algorithm. In this study, 23 classic optimization functions and 16 real-world patients (resulting in 41 multimodal registration scenarios) are used in experiments performed to statistically investigate the problem solving ability of the NVSA. Nine metaheuristic algorithms are used in the conducted experiments. When compared to the commonly utilized registration methods, such as ANTS, Elastix, and FSL, our method achieves better registration performance on the RIRE dataset. Moreover, we prove that our method can perform well with or without its initial spatial transformation in terms of different evaluation indicators, demonstrating its versatility and robustness for various clinical needs and applications. This study establishes the idea that metaheuristic-based methods can better accomplish linear registration tasks than the frequently used approaches; the proposed method demonstrates promise that it can solve real-world clinical and service problems encountered during nonrigid registration as a preprocessing approach.The source code of the NVSA is publicly available at https://github.com/PengGui-N/NVSA.

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Encouraging progress has been made for FSS by leveraging semantic features learned from base classes with sufficient training samples to represent novel classes. The correlation-based methods lack the ability to consider interaction of the two subspace matching scores due to the inherent nature of the real-valued 2D convolutions. In this paper, we introduce a quaternion perspective on correlation learning and propose a novel Quaternion-valued Correlation Learning Network (QCLNet), with the aim to alleviate the computational burden of high-dimensional correlation tensor and explore internal latent interaction between query and support images by leveraging operations defined by the established quaternion algebra. Specifically, our QCLNet is formulated as a hyper-complex valued network and represents correlation tensors in the quaternion domain, which uses quaternion-valued convolution to explore the external relations of query subspace when considering the hidden relationship of the support sub-dimension in the quaternion space. Extensive experiments on the PASCAL-$5^{i}$and COCO-$20^{i}$datasets demonstrate that our method outperforms the existing state-of-the-art methods effectively.

As the first step to study the coding mechanism and synergistic behaviour of neurons, spike sorting plays an important role in the neurosciences research community. Despite many empirical successes in spike sorting models, there are still sufferings from the overlapping and noise corruption problems. To ease these situations, in this paper, we present an efficient and effective method with the help of optimization theory. Firstly, by introducing the low-rank strategy, the global structure underlying the spike data could be discovered. Secondly, by engaging the sparse coding to balance the noise, the proposed model is robust in the overlapping and noise spike sorting scenario. We have conducted experiments on the Wave-clus dataset compared with two state of the art models. The results verify the efficacy of our scheme and confirm the claims above.

The quaternion‐valued signals consist of four signal components. The discrete quaternion Fourier transform is to map these four signal components in the time domain to that in the frequency domain. These four signal components in the frequency domain are called the discrete quaternion Fourier transform components. There are a total of 16 inner products among any two discrete quaternion Fourier transform components. The total orthogonal error among the discrete quaternion Fourier transform components is defined based on these 16 inner products. This study aims to find the optimal quaternion number in the discrete quaternion Fourier transforms so that the total orthogonal errors among the discrete quaternion Fourier transform components are minimised. It is worth noting that finding the optimal quaternion number in the discrete quaternion Fourier transform is equivalent to finding the optimal rescaling factors. Since the discrete quaternion Fourier transform components are expressed in terms of the high‐order polynomials of the trigonometric functions of the rescaling factors, this optimisation problem is non‐convex. To address this problem, a two‐stage approach is employed for finding the solution to the optimisation problem. The comparison results show that the authors proposed method outperforms the existing methods in terms of achieving the low total orthogonal error among the discrete quaternion Fourier transform components.

Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical‐based methods for performing the non‐invasive blood glucose estimation for the consumer applications becomes very important. The authors’ previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non‐invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L 1 norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state‐of‐the‐art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.

Despite the successful applications of probabilistic collaborative representation classification (PCRC) in pattern classification, it still suffers from two challenges when being applied on hyperspectral images (HSIs) classification: 1) ineffective feature extraction in HSIs under noisy situation; and 2) lack of prior information for HSIs classification. To tackle the first problem existed in PCRC, we impose the sparse representation to PCRC, i.e., to replace the 2-norm with 1-norm for effective feature extraction under noisy condition. In order to utilize the prior information in HSIs, we first introduce the Euclidean distance (ED) between the training samples and the testing samples for the PCRC to improve the performance of PCRC. Then, we bring the coordinate information (CI) of the HSIs into the proposed model, which finally leads to the proposed locality regularized robust PCRC (LRR-PCRC). Experimental results show the proposed LRR-PCRC outperformed PCRC and other state-of-the-art pattern recognition and machine learning algorithms.

In current vector perturbation (VP) precoding architecture, the optimum perturbation vector is found with a closest lattice vector search for a given query point. In this work, we show that the query point should be judiciously chosen such that the effective noise power is minimized. The reduced noise power results in a better error rate performance for VP. The crux in the design is to decode an integer-multiple of lattice point within a modulo lattice architecture, where the integer- multiple can be a prime or a product of primes. Simulations show that around 2 dBs' performance gain can be observed even in the small-scale systems.

This study proposes a computer cryptographic system through performing the chaotic modulation on the intrinsic mode functions with a non‐dyadic number of the encrypted signals. First, the empirical mode decomposition is applied to an input signal to generate a set of intrinsic mode functions. Then, these intrinsic mode functions are categorised into two groups of signals. Next, a type 1 polyphase is employed to represent each group of signals. These polyphase components are combined to generate a non‐dyadic number of polyphase components. Second, the chaotic modulation is applied to these combined polyphase components for performing the encryption in the time frequency domain. To reconstruct the original signal, first, the chaotic demodulation is applied to the encrypt components to reconstruct the combined polyphase components. Then, the original groups of intrinsic mode functions are reconstructed through the type 2 polyphase representation and the original signal is reconstructed. Compared with the chaotic filter bank system, the proposed approach enjoys the nonlinear and adaptive property of the empirical mode decomposition. Therefore, a better security performance can be achieved particularly for the non‐stationary signals. Compared with the conventional chaotic modulation approach, the proposed system allows performing the cryptography in the time frequency domain.

This study proposes an augmented Lagrange multiplier‐based method to perform the singular spectral analysis‐based denoising without computing the singular values. In particular, the one‐dimensional (1D) signal is first mapped to a trajectory matrix using the window length L . Second, the trajectory matrix is represented as the sum of the signal dominant matrix and the noise‐dominant matrix. The determination of these two matrices is formulated as an optimisation problem with the objective function being the sum of the rank of the signal dominant matrix and the norm of the noise‐dominant matrix. This study employs the Schatten q‐norm operator with and the double nuclear‐norm penalty for approximating the rank operator as well as the minimum concave penalty (MCP)‐norm operator for approximating the ‐norm operator. Third, some auxiliary variables are introduced and the augmented Lagrange multiplier algorithm is applied to find the optimal solution. Finally, the 1D denoised signal is obtained by applying the diagonal averaging method to the obtained signal dominant matrix. Computer numerical simulation results show that the authors' proposed method outperforms the existing methods.

This study proposes an integer quadratic programming method for grouping and selecting the singular spectral analysis components based on the empirical mode decomposition for performing the denoising. Here, the total number of the grouped singular spectral analysis components is equal to the total number of the intrinsic mode functions. The singular spectral analysis components are assigned to the group indexed by the corresponding intrinsic mode function where the two norm error between the corresponding intrinsic mode function and the sum of the grouped singular spectral analysis components is minimum. Actually, this assignment of the singular spectral analysis components to a particular group is an integer quadratic programming problem. However, the required computational power for finding the solution of the integer quadratic programming problem is high. On the other hand, by representing the integer quadratic programming problem as an integer linear programming problem and employing an existing numerical optimisation computer aided design tool for finding the solution of the integer linear programming problem, the solution can be found efficiently. Computer numerical simulation results are presented.

This study proposes an optimal design of the orders of the discrete fractional Fourier transforms (DFrFTs) and construct an overcomplete transform using the DFrFTs with these orders for performing the sparse representations. The design problem is formulated as an optimisation problem with an ‐norm non‐convex objective function. To avoid all the orders of the DFrFTs to be the same, the exclusive OR of two constraints are imposed. The constrained optimisation problem is further reformulated to an optimal frequency sampling problem. A method based on solving the roots of a set of harmonic functions is employed for finding the optimal sampling frequencies. As the designed overcomplete transform can exploit the physical meanings of the signals in terms of representing the signals as the sums of the components in the time–frequency plane, the designed overcomplete transform can be applied to many applications.

Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main drawbacks. The first is due to the randomly generated initial weights and bias, which cannot guarantee optimal output of ELM. The second is the lack of spatial information in the classifier as the conventional ELM only utilizes spectral information for classification of HSI. To tackle these two problems, a new framework for ELM-based spectral-spatial classification of HSI is proposed, where probabilistic modeling with sparse representation and weighted composite features (WCFs) is employed to derive the optimized output weights and extract spatial features. First, ELM is represented as a concave logarithmic-likelihood function under statistical modeling using the maximum a posteriori estimator. Second, sparse representation is applied to the Laplacian prior to efficiently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm. Third, the spatial information is extracted using the WCFs to construct the spectral-spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on three publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and also a number of state-of-the-art approaches.

There are increasing preferences to employ wireless communication technologies for high mobility, high scalability and low-cost applications in smart city development. This paper gives a brief synopsis of typical wireless technologies in smart city applications and the comparison analysis between them. The trend for smart city wireless technology is also presented. Examples, for several key applications within smart city development (healthcare, smart grid, localization) are studied and current advanced solutions supporting these applications are summarized with futuristic trends and demands are presented.

This study presents a new property of the filter bank‐based multicarrier (FBMC) system. Also, an efficient iterative algorithm for designing the system with a large number of subcarriers and a prototype filter with a very long length are proposed. For the system, the compact from conditions are derived for both the intersymbol interference free and the interchannel interference (ICI) free. Based on these new conditions, the design of the prototype filter is formulated as an unconstrained optimisation problem where the objective function is the weighted sum of the total distortion of the system and the stopband energy. By deriving the gradient vector of the objective function, an efficient iterative algorithm is proposed for finding the solution of the optimisation problem. In addition, an efficient matrix inversion approach is presented to greatly reduce the computational complexity of the iterative algorithm. As a result, it is feasible to design the FBMC system with thousands of subcarriers. The convergence of the iterative algorithm is proved. Computer numerical simulation results with the comparisons to the existing methods are presented. It is shown that the proposed design algorithm is more effective and efficient than the existing methods.

In this paper, an optimal design of a multibit interpolative sigma delta modulator (SDM) based on the absolute stability criterion is proposed. There are two types of midtread quantizers, namely, the uniform midtread quantizer and the nonuniform midtread quantizer. It is shown in this paper that the uniform midtread quantizer is the optimal one between these two types of the midtread quantizers in the sense of minimizing the maximum output input ratio of the midtread quantizer. A loop filter of the multibit interpolative SDM is designed based on the minimization of the energy of the noise transfer function in signal band subject to the strictly stable or the marginally stable condition of the loop filter, the absolute stability criterion, and the specifications on both the noise transfer function in the signal band and the magnitude response of the loop filter outside the signal band. Computer numerical simulation results show that our proposed multibit interpolative SDM achieves a broader stability margin and a higher signal-to-noise ratio compared to the state-of-art designs.

This paper proposes a chaotic filter bank based computer cryptographic system for wireless applications. The signal is first decomposed via an analysis filter bank. Second, a pair of chaotic modulators is employed for performing the encryption on the subband signals. Third, the encrypted signals are upsampled and filtered so as to obtain the oversampled signals. Fourth, the oversampled signals are quantized via a multi-dimensional sigma delta modulator. Fifth, the quantized signals are encoded via a low density parity check code scheme. The encoded signals are transmitted via a wireless channel. At the receiver side, first the encoded signals are decoded via a low density parity check decoder. Then, a multi-dimensional filter is employed for separating the signals and the quantization noises. Next, the filtered signals are downsampled to obtain the same number of samples. After that, a pair of chaotic demodulators is employed for performing the decryption. Finally, a synthesis filter bank is used to reconstruct the original signal. In this paper, the filters in both the filter bank system and the multidimensional sigma delta modulator are designed. Computer numerical simulation results show that the proposed system outperforms the existing computer cryptographic systems.

This paper proposes a method for selecting the singular spectrum analysis components via the empirical mode decomposition approach for extracting the useful information for noninvasive blood glucose estimation systems. To perform the grouping, the total number of the groups of the singular spectrum analysis components is equal to the total number of the intrinsic mode functions. First each normalized singular spectrum analysis component is compared to each normalized intrinsic mode function. Second, the singular spectrum analysis component is assigned to the group corresponding to the intrinsic mode function having the highest correlation coefficient. Third, all the singular spectrum components belong to the same group are summed up together. This technique is applied to extracting the useful information for noninvasive blood glucose estimation systems. In particular, the measured signal is decomposed into a number of components via both the singular spectrum analysis approach and the empirical mode decomposition approach. After applying our proposed grouping method to obtain the singular spectrum analysis components, the obtained components enjoy the advantages of both the singular spectrum analysis approach and the empirical mode decomposition approach. Computer numerical simulations are performed on the practical measurements. The results show that more robust information can be found in the obtained singular spectrum analysis components.

This paper proposes the optimal designs of both the rectified layer and the pooling layer of the convolutional neural network for a non-invasive blood glucose estimation system. The activation function of the neuron in the rectified layer is modelled by a high dimensional Gaussian function. The optimal design of the rectified layer becomes the optimal design of the parameters in the high dimensional Gaussian function. On the other hand, the pooling layer of the convolutional neural network is to represent a certain number of the outputs of the rectified layer by a value. In this paper, this representation value is defined as the Lp norm of a certain number of the outputs of the rectified layer, and the value of p is found via finding the solution of a smooth optimization problem. By finding the solutions of these optimization problems, the designed convolutional neural network is used in a non-invasive system for estimating the blood glucose concentration.

This paper proposes the designs of the periodic window functions in the filter window filter banks for harsh environments. Here, two different cases are considered. The first case assumes that the second periodic window functions are constant. The second case assumes that the second periodic window functions are time varying. For the first case, the design of the periodic window functions is formulated as an L0norm optimization problem. In particular, the total number of the nonzero coefficients of the impulse responses of the periodic windows is minimized subject to the exact perfect reconstruction condition. The solution of this L0norm optimization problem is found via an orthogonal matching pursuit method. For the second case, the design of these two periodic window functions is also formulated as an L0norm optimization problem. In particular, the total number of the nonzero coefficients of the impulse responses of these two periodic window functions is minimized subject to the exact perfect reconstruction condition. Likewise, the orthogonal matching pursuit method is employed for finding a solution of the optimization problem. The designed filter window filter banks are employed for harsh environments.

Wireless mobile handsets are widely used for the electromyogram acquisitions because of their portability property. However, as the electromyograms are with low amplitudes and they are very sensitive to the noises, a good analog-to-digital conversion system plays a very important role for the processing of the electromyograms. Among all the analog-to-digital converters, the sigma delta modulator is the most common analog-to-digital convertor employed in the mobile handsets. This is because the oversampling mechanism for bandlimited signals can be implemented using the existing hardware. However, the sigma delta modulator may suffer from the stability issue. This paper considers the stabilization of a single bit high order interpolative sigma delta modulator via flipping some values of the quantizer output. The values of the quantizer output are determined in such that certain frequency contents of the input signal are cancelled by those of the quantizer output. A stability condition for the proposed control strategy is derived. Since only some frequency detectors and a simple inverter are required for the implementation of the proposed control strategy, the implementation cost is low.

Many diseases can be detected via performing the electrocardiogram diagnosis. Although the electrocardiograms can be acquired in the public clinics, hospitals or even at homes by using the existing devices, these existing devices are not tiny enough to carry on the bodies. Hence, it is difficult to acquire the electrocardiograms all the time. In order to acquire the electrocardiograms all the time, this paper proposes a wearable electrocardiogram acquisition system. In particular, the R points of the electrocardiograms are detected by computing the regularities of the signals. If the regularity of a signal at a point v is about -1.5 and it is the minimum around its neighbor, then v is assumed to be the R point. Similarly, the Q points and the R points can be detected accordingly. Here, it is not required to compute the modulus maxima. Hence, the required computational power is very low. The simulation results show that the proposed algorithm outperforms the existing methods in terms of the required computational complexity.

This study proposes an iterative method to approximate an N ‐dimensional optimisation problem with a weighted L p and L 2 norm objective function by a sequence of N independent one‐dimensional optimisation problems. Inspired by the existing weighted L 1 and L 2 norm separable surrogate functional (SSF) iterative shrinkage algorithm, there are N independent one‐dimensional optimisation problems with weighted L p and L 2 norm objective functions. However, these optimisation problems are non‐convex. Hence, they may have more than one locally optimal solutions and it is very difficult to find their globally optimal solutions. This paper proposes to partition the feasible set of each approximated problem into various regions such that the sign of the convexity of the objective function in each region remains unchanged. Here, there is no more than one stationary point in each region. By finding the stationary point in each region, the globally optimal solution of each approximated optimisation problem can be found. Besides, this study also shows that the sequence of the globally optimal solutions of the approximated problems converge to the globally optimal solution of the original optimisation problem. Computer numerical simulation results show that the proposed method outperforms the existing weighted L 1 and L 2 norm SSF iterative shrinkage algorithm.

This study proposes an iterative method to approximate an N‐dimensional optimisation problem with a weighted Lp norm and L2 norm objective function by a sequence of N independent one‐dimensional optimisation problems. This iterative method is inspired by the existing weighted L1 norm and L2 norm separable surrogate functional (SSF) iterative shrinkage algorithm. However, as these independent one‐dimensional optimisation problems consist of weighted Lp norm and L2 norm objective functions, these optimisation problems are non‐convex and they may have more than one locally optimal solutions. In general, it is very difficult to find their globally optimal solutions. To address this difficulty, this study proposes to partition the feasible set of each approximated problem into various regions such that the sign of the convexity of the objective function in each region remains unchanged. In this case, there is no more than one stationary point in each region. By finding the stationary point in each region, the globally optimal solution of each approximated optimisation problem can be found. Besides, this study also shows that the sequence of the globally optimal solutions of the approximated problems converge to the globally optimal solution of the original optimisation problem.

An interference-mitigated ZigBee-based advanced metering infrastructure (AMI) solution, namely IMM2ZM, has been developed for high-traffics smart metering (SM). The IMM2ZM incorporates multiradios multichannels network architecture and features an interference mitigation design by using multiobjective optimization. To evaluate the performance of the network due to interference, the channel-swapping time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are significant improvements in the performance of the application-layer transmission rate (σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70% in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ > ~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM results in typically less than 0.43 s delay for a 30-floor building under interference. This performance fulfills the latency requirement of less than 0.5 s for SMs in the USA (Magazine of Department of Energy Communications, USA, 2010). The IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel Kxcorr,ijand the convolution kernel Kconv,ij. Kxcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both Kxcorr,ijand Kconv,ijby more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

This paper proposes a mobile based big data design patent image retrieval system via a deep learning approach. The images are represented via sparse vectors by a dictionary. The joint representation and dictionary design problem is formulated as a mixed L2 and Lp optimization problem. An iterative algorithm is employed for finding a locally optimal solution. Experimental results show that the retrieval accuracy is high.

Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy, sensitivity, and specificity of 76.93%, 77.36%, and 76.5% respectively. Results have revealed that the performance of proposed classifier is better than traditional methods.

This paper proposes a novel method for detecting the Parkinson's diseases via applying the empirical mode decomposition to filtered electromyograms. First, the electromyograms are processed by different linear phase finite impulse response bandpass filters with different pairs of cutoff frequencies. Second, each filtered electromyogram is decomposed into several intrinsic mode functions. Third, both the entropies and the total numbers of the extrema of the intrinsic mode functions of each filtered electromyogram are computed and they are used as the features for detecting the Parkinson's diseases. Computer numerical simulation results show that the features are linearly separable. Hence, a simple perceptron can be employed for the detection of the Parkinson's diseases. Finally, the algorithm is implemented via a mobile application. Compared to conventional empirical mode decomposition approaches in which a predefined number of features is employed for detecting the Parkinson's diseases, our proposed method allows to use a flexible number of features for detecting the Parkinson's diseases. This is because the total number of filters to be employed is very flexible. As a result, our proposed method is more flexible than the existing methods.

This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram. Then, these discrete cosine transform coefficients are scaled down according to their frequency indices. These scaled discrete cosine transform coefficients of each electrocardiogram are employed as features for performing the discrimination. Second, the high order statistic moments of each feature of the training electrocardiograms corresponding to the same type of tachycardias are evaluated. These high order statistic moments of each feature corresponding to same type of tachycardias form a vector. Third, the high dimensional linear discriminant function is employed to minimize the intraclass separation and maximize the interclass separation of these statistic moment vectors. In particular, new vectors are formed by projecting these statistic moment vectors to the high dimensional linear discriminant function. Fourth, the principal component analysis is employed to reduce the dimension of the projected vectors. Finally, a bank of perceptrons with multi-piece domain activation functions having multi-level functional values is employed for performing the discrimination. By using this bank of perceptrons, the condition for general two class pattern recognition problems achieving the error free pattern recognition performance is guaranteed. Computer numerical simulation results show that our proposed method is robust and effective.

This paper proposes a novel method of killing birds and applying vaccines for suppressing the spread of avian influenza via a nonlinear switching control approach. The switching strategy is based on the population of the susceptible birds and the population of the susceptible humans. There are four switching cases. For the first three switching cases, the elimination control force and the quarantine control force are equal to either zero or one. For the last switching case, they are equal to one minus a scalar divided by the population of the susceptible birds and one minus another scalar divided by the population of the susceptible humans, respectively. The system state vectors of the avian influenza model are guaranteed to reach the desirable equilibrium point. Also, the positivity requirements on the system states as well as the constraints on both the lower bounds and the upper bounds of both the elimination control force and the quarantine control force are guaranteed to be satisfied. Computer numerical simulation results show that the proposed control strategy is very effective and efficient.

This paper proposes a novel content removal technique for enhancing the camera identification performance. Here, very low bit rate videos with the overall noise patterns having time varying statistics are considered. First, different two dimensional discrete fractional Fourier transforms with different rotational angles are applied to the overall noise pattern of each frame of each video. Second, the modulus of each element of each transformed matrix is normalized to one if the rotational angles of the transforms are not equal to the integer multiples of π. Third, the corresponding two dimensional inverse discrete fractional Fourier transform is applied to each normalized matrix and the corresponding real part is taken out for the further processing. Fourth, the absolute values of the elements in each normalized real valued matrix are bounded by a certain threshold value. Finally, the processed matrices are averaged over all the rotational angles and all the frames of the videos corresponding to the same camera. Extensive computer numerical simulation results on the correlation performances are presented. It is found that the proposed method outperforms the existing method for a wide range of rotational angles.

This paper presents a field programmable gate array (FPGA) based 2-dimentional hand gesture recognition system using Haar feature Adaboost detector, skin color segmentation, and frame subtraction background modeling. The system obtains detection results and tracking results simultaneously. In addition, the interaction instructions are interpreted by a judgment strategy. The proposed system can complete the processing of 640*480 resolution video at 60 frames per second. The design has been synthesized and mapped for the Altera Stratix IV board. The proposed architecture compensates for the low detection speed of software implementation systems for the hardware implementation utilizes the parallelism inherited in the algorithms.

For an optimisation problem with an L 1 norm objective function subject to an L 2 norm inequality constraint, this study shows that there is an approximately linear relationship between the L 1 norm objective functional values and the L 2 norm specifications. This relationship is verified through the use of random and real world industrial data. The obtained results can be employed for (i) estimating the L 1 norm objective functional value without solving the optimisation problem numerically; (ii) providing an insight for defining the L 2 norm specification in which a simple method is proposed in this study; and (iii) testing whether the obtained solutions are the globally optimal solutions or not. These advantages are demonstrated via the use of random data.

A novel unsupervised machine learning algorithm for single channel source separation is presented. The proposed method is based on nonnegative matrix factorization, which is optimized under the framework of maximum a posteriori probability and Itakura-Saito divergence. The method enables a generalized criterion for variable sparseness to be imposed onto the solution and prior information to be explicitly incorporated through the basis vectors. In addition, the method is scale invariant where both low and high energy components of a signal are treated with equal importance. The proposed algorithm is a more complete and efficient approach for matrix factorization of signals that exhibit temporal dependency of the frequency patterns. Experimental tests have been conducted and compared with other algorithms to verify the efficiency of the proposed method.

The main contribution of this paper is to propose an optimal joint design of an orthonormal real valued short time block code and a linear transceiver for multi-input multi-output (MIMO) wireless digital communication systems in next generation home. Firstly, a relaxed zero forcing condition governing the relationship between the short time block code and the linear transceiver is imposed via a Karhunen Loève transform (KLT) approach. The relaxed zero forcing condition guarantees that there is no transmission error under a noise free environment. Secondly, the linear transceiver is optimally designed via the orthogonal Procrustes approach. In particular, the transmission power gain is minimised subject to a specification on the ratio of the signal gain to the noise gain as well as to the relaxed zero forcing condition. Computer numerical simulation results show that our proposed optimal joint design of the orthonormal real valued short time block code and the linear transceiver can significantly improve the performances of MIMO wireless digital communication systems in next generation home.

This paper proposes a novel methodology for extracting the underlying trend and predicting the power usage through a joint singular spectrum analysis (SSA) and sparse binary programming approach. The underlying trend is approximated by the sum of a part of SSA components, in which the total number of the SSA components in the sum is minimized subject to a specification on the maximum absolute difference between the original signal and the approximated underlying trend. As the selection of the SSA components is binary, this selection problem is to minimize the L0norm of the selection vector subject to the L∞norm constraint on the difference between the original signal and the approximated underlying trend as well as the binary valued constraint on the elements of the selection vector. This problem is actually a sparse binary programming problem. To solve this problem, first the corresponding continuous valued sparse optimization problem is solved. That is, to solve the same problem without the consideration of the binary valued constraint. This problem can be approximated by a linear programming problem when the isometry condition is satisfied, and the solution of the linear programming problem can be obtained via existing simplex methods or interior point methods. By applying the binary quantization to the obtained solution of the linear programming problem, the approximated solution of the original sparse binary programming problem is obtained. Unlike previously reported techniques that require a pre-cursor model or parameter specifications, the proposed method is completely adaptive. Experiment results show that our proposed method is very effective and efficient for extracting the underlying trend and predicting the power usage.

This paper extends the definition of an exact consensus complex social network to that of a near consensus complex social network. A near consensus complex social network is a social network with nontrivial topological features and steady state values of the decision certitudes of the majority of the nodes being either higher or lower than a threshold value. By using eigen theories, the relationships among the vectors representing the steady state values of the decision certitudes of the nodes, the influence weight matrix and the set of vectors representing the initial state values of the decision certitudes of the nodes that satisfies a given near consensus specification are characterized.

In this paper, an invariant set of the weight of the perceptron trained by the perceptron training algorithm is defined and characterized. The dynamic range of the steady-state values of the weight of the perceptron can be evaluated by finding the dynamic range of the weight of the perceptron inside the largest invariant set. In addition, the necessary and sufficient condition for the forward dynamics of the weight of the perceptron to be injective, as well as the condition for the invariant set of the weight of the perceptron to be attractive, is derived.

Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, pre- and post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30%.

In this paper, the dynamics of weights of perceptrons are investigated based on the perceptron training algorithm. In particular, the condition that the system map is not injective is derived. Based on the derived condition, an invariant set that results to a bijective invariant map is characterized. Also, it is shown that some weights outside the invariant set will be moved to the invariant set. Hence, the invariant set is attracting. Computer numerical simulation results on various perceptrons with exhibiting various behaviors, such as fixed point behaviors, limit cycle behaviors and chaotic behaviors, are illustrated.

In this paper, it is found that the weights of a perceptron are bounded for all initial weights if there exists a nonempty set of initial weights that the weights of the perceptron are bounded. Hence, the boundedness condition of the weights of the perceptron is independent of the initial weights. Also, a necessary and sufficient condition for the weights of the perceptron exhibiting a limit cycle behavior is derived. The range of the number of updates for the weights of the perceptron required to reach the limit cycle is estimated. Finally, it is suggested that the perceptron exhibiting the limit cycle behavior can be employed for solving a recognition problem when downsampled sets of bounded training feature vectors are linearly separable. Numerical computer simulation results show that the perceptron exhibiting the limit cycle behavior can achieve a better recognition performance compared to a multilayer perceptron.

In this paper, a noise analysis of a modulated quantizer is performed. If input signals are oversampled, then the quantization error could be reduced by modulating both the input and the output of the quantizer. The working principle is based on the fact that convolutions of bandpass signals would spread wider in the frequency spectrum than that of lowpass signals. Hence, by filtering the high frequency components, the signal-to-noise ratio (SNR) could be increased. Numerical simulation results show that the modulated quantization scheme could achieve an average of 13.0960dB to 21.4700dB improvements on SNR over the conventional scheme, depends on the types of bandlimited input signals.

It has been established that a class of bandpass sigma delta modulators (SDMs) may exhibit state space dynamics which are represented by elliptical or fractal patterns confined within trapezoidal regions when the system matrices are marginally stable. It is found that fractal patterns may also be exhibited in the phase plane when the system matrices are strictly stable. This occurs when the sets of initial conditions corresponding to convergent or limit cycle behavior do not cover the whole phase plane. Based on the derived analytical results, some interesting results are found. If the bandpass SDM exhibits periodic output, then the period of the symbolic sequence must equal the limiting period of the state space variables. Second, if the state vector converges to some fixed points on the phase portrait, these fixed points do not depend directly on the initial conditions.

We prove that a filter bank with set of decimators {2,4,4} achieves perfect reconstruction if and only if it can be represented via a tree structure and each branch of the tree structure achieves perfect reconstruction.