Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2-node and 3-node interactions, but incur cubic computational cost. Existing efficiency methods typically reduce this burden at the expense of expressivity. We propose Co-Sparsify, a connectivity-aware sparsification framework that eliminates provably redundant computations while preserving full 2-FWL expressive power. Our key insight is that 3-node interactions are expressively necessary only within biconnected components, namely, maximal subgraphs where every node pair lies on a cycle. Outside these components, structural relationships are fully captured via 2-node message passing and graph readouts, rendering higher-order modeling unnecessary. Co-Sparsify restricts 2-node message passing to connected components and 3-node interactions to biconnected components, eliminating redundant computation without approximation or sampling. We prove that Co-Sparsified GNNs match the expressivity of the 2-FWL test. Empirically, when applied to PPGN, Co-Sparsify matches or exceeds accuracy on synthetic substructure counting tasks and achieves state-of-the-art performance on real-world benchmarks (ZINC, QM9 and TUD). This study demonstrates that high expressivity and scalability are not mutually exclusive: principled, topology-guided sparsification enables powerful, efficient GNNs with theoretical guarantees.

Medical large Vision-Language Models (Med-LVLMs) have shown strong potential in multimodal clinical applications such as medical visual question answering and report generation. However, Med-LVLMs remain challenged by hallucinations caused by modality misalignment, where models prioritize textual knowledge over visual evidence and generate outputs that conflict with medical images. To mitigate this issue, recent studies have explored preference optimization to improve image–text alignment, achieving promising results. Despite these advances, existing preference-based methods still face two limitations in medical settings: (1) overfitting to superficial cues, and (2) pseudo convergence of the preference signal. In this paper, we propose Dynamic Evidence-Guided Preference Optimization (DEPO), a new framework that enables evidence-aware and adaptive preference learning for Med-LVLMs. DEPO introduces Multi-Modal Evidence Perturbation (MEP) to suppress non-causal textual and visual shortcuts, and Dispreferred Evidence Resampling (DER) to continuously update dispreferred responses as hallucination patterns evolve. Experiments on multiple medical VQA and report generation benchmarks demonstrate consistent improvements over existing methods, with strong robustness across datasets and architectures. All Codes and data will be released after review.

Cuffless blood pressure (BP) measurement has gained increasing attention due to the global aging population. Data-driven approaches have shown high accuracy for cuffless BP estimation. However, when deployed on wearable devices, they often suffer from being time-consuming because a complete retraining process is required if the training samples or structure need to be expanded. To address these issues, we propose a Knowledge-embedded Bi-incremental Broad Learning System (BpBLS) for cuffless BP estimation using biosignals collected from wearable devices.With a flat structure, BpBLS can be updated flexibly and quickly without retraining the entire model for incremental biosignals and/or training samples. In BpBLS, a novel Pulse Pressure Regularization (PPR) method is proposed to comprehensively capture the BP knowledge, which is then embedded into the system to enhance its accuracy. Experimental results demonstrate that BpBLS exhibits superior performance in both estimation accuracy and computational efficiency compared to the state-of-the-art approaches. For the CAS-BP dataset, the estimation error is 0.60 $\pm$ 8.44 for systolic BP (SBP) and 0.29 $\pm$ 6.45 for diastolic BP (DBP); while for the Aurora-BP dataset, the estimation error is -0.32 $\pm$ 8.46 mmHg for SBP and 0.05 $\pm$ 6.65 mmHg for DBP. More importantly, the training time for both datasets is less than ten seconds, and the computational efficiency is improved by an order of magnitude compared to traditional machine learning methods. Our work will serve as a novel flexible and lightweight framework for cuffless BP measurement.

Existing UAV vehicle-detection datasets, typically captured under static and uniform illumination, fail to adequately represent the variable lighting conditions, dense traffic, and frequent occlusions observed in real-world transportation hubs. To bridge this gap, a new dataset, UAV-HubSurveillance, is introduced to capture complex vehicle interactions across urban transportation nodes under diverse environmental scenarios. Although UAV-HubSurveillance provides rich and multidimensional interaction data, it still suffers from severe occlusions and adverse weather conditions that hinder detection and identification accuracy. To address these limitations, a novel vehicle detection framework, termed bidirectional interactive multi-scale aggregation-yolo (BIMSA-YOLO), is proposed, which integrates bidirectional feature interaction with adaptive multi-scale aggregation to enhance detection robustness. First, the bidirectional shallow fusion module (BSFM) facilitates cross-resolution information exchange through a lightweight gating strategy, preserving fine-grained details of small objects. Second, the interactive deep fusion module (IDFM) reinforces contextual coherence via attention-guided cross-level semantic fusion. Third, the multi-scale adaptive aggregation module (MSAAM) dynamically aligns and integrates multi-scale features to improve robustness against scale variation. Extensive experiments conducted on the UAV-HubSurveillance dataset demonstrate that BIMSA-YOLO significantly enhances detection performance under dynamic, occluded, and adverse-weather conditions. Specifically, the proposed model achieves an mAP0.5of 63.2%, surpassing the baseline by 5.3 percentage points. Furthermore, BIMSA-YOLO also exhibits strong generalization capabilities on VisDrone and CARPK datasets. Our code and dataset are available athttps://github.com/yikuizhai/BIMSA-YOLO

Underwater imaging plays a pivotal role in marine exploration and ecological monitoring. However, it faces significant challenges of limited transmission bandwidth and severe distortion in the aquatic environment. In this work, to achieve the target of both underwater image compression and enhancement simultaneously, an enhanced quality-aware scalable underwater image compression framework is presented, which comprises a Base Layer (BL) and an Enhancement Layer (EL). In the BL, the underwater image is represented by a controllable number of non-zero sparse coefficients for coding bits saving. Furthermore, the underwater image enhancement dictionary is derived with shared sparse coefficients to make reconstruction close to the enhanced version. In the EL, a dual-branch filter comprising rough filtering and detail refinement branches is designed to produce a pseudo-enhanced version for residual redundancy removal and to improve the quality of final reconstruction. Extensive experimental results demonstrate that the proposed scheme outperforms the state-of-the-art works under five large-scale underwater image datasets in terms of Underwater Image Quality Measure (UIQM).

Emotion recognition has broad applications in fields such as healthcare and human-computer interaction. As effective biomarkers reflecting emotional states, physiological signals have attracted significant attention. However, cross-subject emotion recognition based on physiological signals, especially multimodal signals, faces three major challenges: sample heterogeneity, modality heterogeneity, and subject heterogeneity. To address these issues, we propose a Multilevel Augmented Dual Contrastive Learning Network (MADCL-Net). MADCL-Net mitigates sample-level differences through hierarchical data augmentation and reduces modality and subject discrepancies by introducing dual contrastive learning at both the modality and subject levels. Two novel contrastive loss functions are designed for this purpose. An attention module is further integrated to enhance the complementarity among different modalities. Extensive experiments on the DEAP and DREAMER datasets demonstrate the superiority of our approach. Specifically, MADCL-Net outperforms the second-best state-of-the-art model by 1.53% in the arousal dimension on the DEAP dataset and by 2.65% in the valence dimension on the DREAMER dataset, while achieving the second-best performance in the other dimension on both datasets.

Hypertension is a leading controllable risk factor for cardiovascular disease and premature death, with blood pressure (BP) variability potentially posing greater risks than sustained hypertension. However, intermittent single-point measurements cannot capture dynamic BP changes, and although invasive arterial catheterization is the gold standard for continuous waveforms, its high complication risk limits clinical use. Consequently, non-invasive continuous BP monitoring has emerged as a promising alternative but still faces two major limitations: (1) Separate modeling of waveform prediction and SBP/DBP estimation reduces supervision and physiological consistency; (2) Point-wise loss functions overlook the periodic structure of blood pressure waveforms. To address these challenges, we propose WaveMorph, a framework for high-fidelity long-term continuous BP waveform prediction with three key contributions: (1) We develop BP-Guided de-normalization to couple waveform prediction with SBP and DBP, strengthening supervision and improving physiological consistency; (2) We introduce a patchlevel morphology loss that preserves local periodic structure and morphological fidelity in predicted waveforms; (3) achieving state-of-the-art performance. The proposed method was evaluated on the MIMIC-III dataset. Results show that our model outperforms state-of-the-art methods in waveform reconstruction and SBP/DBP estimation, achieving mean absolute errors (MAE) of 3.52 mmHg (waveform), 4.78 mmHg (SBP), and 2.42 mmHg (DBP).

Electroencephalogram (EEG) can record the elec-trical activity of large neural populations and has been widely applied to emotion recognition. Many existing methods leverage EEG signals from tens or even hundreds of electrodes (referred to as full-channel EEG) to develop emotion recognition algorithms, achieving promising results. In recent years, portable and miniature EEG devices equipped with only a few electrodes (referred to as few-channel EEG) have begun to emerge. However, emotion recognition from few-channel EEG data remains challenging due to the sparsity and limited information in the signals. Moreover, full-channel algorithms cannot be directly applied to few-channel EEG signals due to differences in channel configuration and signal characteristics. To address these challenges, this paper pro-poses a reciprocal knowledge distillation based network, named RKD-Net, for few-channel EEG emotion recognition. In RKD-Net, knowledge is transferred from a teacher network trained on full-channel EEG signals to a student network based on few-channel EEG inputs, and the teacher is continuously updated based on the student's feedback and predictions, thus forming a reciprocal interaction. Furthermore, to capture complementary emotional features across both local and global temporal dynam-ics, the hybrid time-frequency EEG representations are fused with wavelet-based time-frequency diagrams at multiple scales. Extensive experiments on two public EEG datasets demonstrate the superiority of RKD-Net in few-channel emotion recognition.

Continuous blood pressure (BP) monitoring is essential for timely diagnosis and intervention in critical care settings. However, BP varies significantly across individuals, this inter-patient variability motivates the development of personalized models tailored to each patient’s physiology. In this work, we propose a personalized BP forecasting model mainly using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. This time-series model incorporates 2D representation learning to capture complex physiological relationships. Experiments are conducted on datasets collected from three diverse scenarios with BP measurements from 60 subjects total. Results demonstrate that the model achieves accurate and robust BP forecasts across scenarios within the Association for the Advancement of Medical Instrumentation (AAMI) standard criteria. This reliable early detection of abnormal fluctuations in BP is crucial for at-risk patients undergoing surgery or intensive care. The proposed model provides a valuable addition for continuous BP tracking to reduce mortality and improve prognosis.

Human activity recognition using wearable sensors has become a critical task in ubiquitous computing, with applications in healthcare, fitness monitoring, and smart environments. However, sensor-based HAR often involves high-dimensional, multi-channel time series data, where redundant or irrelevant features may degrade both classification performance and model interpretability. In this study, we propose a lightweight feature screening framework guided by a multi-head attention mechanism to address this challenge. The model first applies channel-wise linear transformations to extract localized representations from each sensor axis, and then employs a multi-head attention module to dynamically assess the importance of each feature across all channels. This design enables the model to emphasize the most informative components while suppressing noise and redundancy. Experiments on the KU-HAR dataset demonstrate that the proposed method achieves 96.0% classification accuracy using only 60 selected features. In addition, the selected features provide valuable references for future research in feature selection, model simplification and multimodal sensor fusion.

Despite the theoretical expressiveness of 2-dimensional Folklore Weisfeiler-Lehman (2-FWL) Graph Neural Networks (GNNs), a significant gap persists between their theoretical capacity and their practical performance. To bridge this gap, we identify a critical limitation in current Graph Structural Encodings (GSEs): insufficient sensitivity to subtle structural variations, particularly in local connectivity, spectral features, and distance-based patterns. We show that widely used GSEs-such as Relative Random Walk Probability (RRWP) and monomial-based methods-lack full sensitivity across spectral frequency bands and long-range distances. Moreover, they fail to capture fine-grained local connectivity, which is essential for identifying cut nodes, biconnected components, and other higher-order structures that 2-FWL GNNs theoretically encode. To address these limitations, we propose CSDGSE (Connectivity, Spectral, and Distance Graph Structural Encoding), a novel GSE framework that jointly enhances sensitivity to: (1) exact local connectivity via hierarchical graph decomposition(2) full-frequency spectral features using expressive graph polynomials (e.g., Chebyshev), and (3) full-range distance interactions. A key innovation is our scalable divide-and-conquer algorithm for computing exact local connectivity across all node pairs, enabling efficient integration into modern GSEs. Extensive experiments show that CSDGSE outperforms existing GSEs in capturing complex structural patterns, achieving state-of-the-art results on molecular property prediction benchmarks like ZINC. Our work sets a new standard for GSEs by aligning theoretical expressiveness with practical effectiveness through enhanced structural sensitivity.

Monitoring the Depth of Anesthesia (DoA) using Electroencephalography (EEG) is essential for patient safety and optimal drug administration. However, existing methods face challenges like computational inefficiency and limited generalization, hindering real-time applicability. To address these, we propose a lightweight framework based on SimGate, a parameter-free RNN gating mechanism. SimGate simplifies gating by dynamically computing hidden states based on cosine similarity and requiring only a single linear layer and an initialization vector for training, thus reducing model complexity and enabling efficient parallel computation. To improve generalization, we introduce Harmonic Mix, a frequency augmentation strategy that enhances data diversity by applying harmonic-constrained low-pass filtering and convex combinations of EEG activities. This method preserves critical frequency bands and mitigates noise interference. Experimental results on the VitalDB dataset show that our model achieves 82.70% classification accuracy (ACC) and 5.79±0.64 root mean squared error (RMSE), outperforming existing models with only 16.9K parameters and an inference speed (IS) of 0.02ms. These results demonstrate the effectiveness of our model for real-time DoA monitoring in clinical settings.

Speech emotion recognition (SER) is a critical component in enhancing communication systems and human-machine interaction, with significant potential for applications in the medical field. Although existing SER methods that combine temporal and spectral features have achieved notable advancements, they still encounter a big challenge in capturing emotional nuances, which are vital in medical diagnostics and patient care. In this study, we introduce a straightforward yet highly efficient network called TSFNet, which is the Temporal-Spectral Fusion Network via a Large-scale Pre-trained Model. This network is specifically designed to effectively process intricate emotional nuances by seamlessly integrating temporal and spectral information present in speech signals. By leveraging the capabilities of a large-scale pre-trained model, which serves as a powerful plug-and-play component for extracting and learning the temporal characteristics of speech, TSFNet enables a more accurate capture of complex emotional details crucial for medical applications. Extensive experiments are conducted on publicly available datasets, to evaluate the performance of TSFNet. Extensive experiments conducted on six public datasets demonstrate that TSFNet significantly outperforms existing baselines, achieving unweighted accuracies of 95.57% for Savee, 92.67% for Crema-D, 85.71% for IEMOCAP, 100.00% for Tess, 95.86% for Emovo, and 80.43% for Meld. It means that TSFNet has the potential in advancing medical diagnostic tools and patient monitoring systems.

In recent years, object detection models have been extensively applied across various industries, leveraging learned samples to recognize and locate objects. However, industrial environments present unique challenges, including complex backgrounds, dense object distributions, object stacking, and occlusion. To address these challenges, we propose the Global Dynamic Matching Transformer Network (GMTNet). GMTNet partitions images into blocks and employs a sliding window approach to capture information from each block and their interrelationships, mitigating background interference while acquiring global information for dense object recognition. By reweighting key-value pairs in multi-scale feature maps, GMTNet enhances global information relevance and effectively handles occlusion and overlap between objects. Furthermore, we introduce a dynamic sample matching method to tackle the issue of excessive candidate boxes in dense detection tasks. This method adaptively adjusts the number of matched positive samples according to the specific detection task, enabling the model to reduce the learning of irrelevant features and simplify post-processing. Experimental results demonstrate that GMTNet excels in dense detection tasks and outperforms current mainstream algorithms. The code will be available athttp://github.com/yikuizhai/GMTNet.

Heart Failure (HF) stands as a principal public health issue worldwide, imposing a significant burden on healthcare systems. While existing prognostic methods have achieved certain milestones in predicting the early mortality risk of HF patients, they have not fully considered the dynamic interdependencies among physiological parameters. This paper introduces a novel Multi-scale Spatiotemporal Dynamic Graph Neural Network, MSTD-GNN, which enhances the prediction capability for early mortality in HF patients by dynamically extracting spatio-temporal information of physiological parameters from ICU patient Electronic Health Records (EHRs). Our model constructs dynamic graphs to model multivariate time series data, revealing the implicit dependencies between physiological parameters and capturing the inherent dynamics of the data. We conducted experiments using the MIMIC-III and MIMIC-IV datasets. The experimental results show that, compared to existing methods, MSTD-GNN demonstrates superior performance in predicting the early mortality risk of HF patients. On the MIMIC-III and MIMIC-IV datasets, the AUC scores of MSTD-GNN reached 83.93% and 81.74%, respectively. Furthermore, through dynamic graphs, our model unveils the dynamic relationships between physiological variables across different time scales.

Sleep staging is imperative for evaluating sleep quality and diagnosing sleep disorders. Extant sleep staging methods with fusing multiple data-views of physiological signals have achieved promising results. However, they remain neglectful of the relationship among different data-views at different feature scales with view position-alignment. To address this, we propose a novel cross-view alignment network, termed cVAN, utilising scale-aware attention for sleep stages classification. Specifically, cVAN principally incorporates two sub-networks of a residual-like network which learn spectral information from time-frequency images and a transformer-like network which learns corresponding temporal information. The prime advantage of cVAN is to adaptively align the learned feature scales among the different data-views of physiological signals with a scale-aware attention by reorganizing feature maps. Extensive experiments on three public sleep datasets demonstrate that cVAN can achieve a new state-of-the-art result, which is superior to existing counterparts.

Electrocardiography (ECG) signals are often plagued by various types of noise, which substantially undermines the precision of subsequent analysis. This paper presents ECG-LLM, an innovative model designed to address the challenges posed by low-quality ECG signals. Leveraging the capabilities of Large Language Models (LLMs), ECG-LLM forecasts and imputes missing values in 12-lead ECG data. ECG-LLM adapts the auto-regressive characteristics of LLMs, utilizes textual markers for timestamp information, and employs a strategy of freezing Transformer layers while training new embedding and projection layers. We used ECG datasets from multiple sources for our experiments. Experimental results demonstrate that ECG-LLM significantly outperforms state-of-the-art time series forecasting models, achieving a Mean Squared Error (MSE) of 0.644 and a Mean Absolute Error (MAE) of 0.456 for a forecast length of 64. Additionally, after using ECG-LLM to restore the electrocardiogram, the disease recognition accuracy of the baseline model was improved. Our findings highlight the feasibility and effectiveness of applying LLMs to ECG signal processing, offering a new perspective for medical signal analysis and providing a potential new approach for signal preprocessing in healthcare. Code is available at https://github.com/dragonlfy/ECG-LLM.

The scarcity of labeled EEG data presents a significant challenge in emotion recognition. To address this issue, we propose PAWS, a semi-supervised learning framework specifically designed to enhance EEG-based emotion recognition, particularly in scenarios where labeled data is extremely limited. PAWS leverages the power of Intermediate Mixup, which improves domain adaptation by generating more robust features through strategic augmentation. Additionally, PAWS integrates large language models (LLMs) for enhanced feature extraction, enabling the framework to capture complex patterns in EEG signals even with minimal labeled data. In experiments with 1%, 5%, 10%, 15%, and 20% labeled data, PAWS consistently outperformed existing methods, with the most significant performance gains observed in scenarios with very few labeled samples. Notably, with 20% labeled data, PAWS achieved an accuracy of 95.81% ± 0.52% on the SEED dataset and 83.41% ± 0.31% on the SEED-IV dataset. These results demonstrate PAWS’s effectiveness in leveraging limited labeled data to achieve superior emotion recognition performance. This framework not only advances the state-of-the-art in EEG-based emotion recognition but also provides a robust foundation for future research in semi-supervised learning. Code is available at https://github.com/dragonlfy/PAWS.

Existing anchor graph-based semi-supervised classification methods can not adopt partial available labels of data to produce discriminative anchor graph, which is even challenging for incomplete multi-view data. Addressing above issues, a fast label prediction based on shrunk anchor graph (FLP-SAG) is designed for semi-supervised incomplete multi-view classification, which is capable of learning discriminative anchor graph iteratively. Firstly, in each view a similarity-based anchor graph is constructed and expanded to the size of complete data to align the multiple views. Then these pre-constructed anchor graphs are fused to get a common anchor graph, which is ready to be shrunk based on the predicted labels with high confidence scores in each iteration. To speed up the classification, an efficient two-step label prediction strategy is developed without the calculation of dense matrix inverse. Experimental results on four real world datasets comparing with several recently proposed methods demonstrate the superiority of the proposed method.

The healthcare industry faces challenges due to rising treatment costs, an aging population, and limited medical resources. Remote monitoring technology offers a promising solution to these issues. This article introduces an innovative adaptive method that deploys an ultrawideband (UWB) radar-based Internet of Medical Things (IoMT) system to remotely monitor elderly individuals’ vital signs and fall events during their daily routines. The system employs edge computing for prioritizing critical tasks and a combined cloud infrastructure for further processing and storage. This approach enables monitoring and telehealth services for elderly individuals. A case study demonstrates the system’s effectiveness in accurately recognizing high-risk conditions and abnormal activities, such as sleep apnea and falls. The experimental results show that the proposed system achieved high accuracy levels, with a mean absolute error (MAE) ± standard deviation of absolute error (SDAE) of 1.23± 1.16 bpm for heart rate (HR) detection and 0.22 ± 0.27 bpm for respiratory rate (RR) detection. Moreover, the system demonstrated a recognition accuracy of 90.60% for three types of falls (i.e., stand, bow, squat to fall), one daily activity, and No Activity Background. These findings indicate that the radar sensor provides a high degree of accuracy suitable for various remote monitoring applications, thus enhancing the safety and well-being of elderly individuals in their homes.

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models. This study introduces a novel two-step approach, named HybAVPnet, to predict antiviral peptides with a hybrid network architecture based on neural networks and traditional machine learning methods. We adopted a stacking-like structure to capture both the long-term dependencies and local evolution information to achieve a comprehensive and diverse prediction using the predicted labels and probabilities. Using an ensemble technique with the different kinds of features can reduce the variance without increasing the bias. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability.

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.

Multi-view clustering has attracted significant attention and application. Nonnegative matrix factorization is one popular feature of learning technology in pattern recognition. In recent years, many semi-supervised nonnegative matrix factorization algorithms were proposed by considering label information, which has achieved outstanding performance for multi-view clustering. However, most of these existing methods have either failed to consider discriminative information effectively or included too much hyper-parameters. Addressing these issues, a semi-supervised multi-view nonnegative matrix factorization with a novel fusion regularization (FRSMNMF) is developed in this article. In this work, we uniformly constrain alignment of multiple views and discriminative information among clusters with designed fusion regularization. Meanwhile, to align the multiple views effectively, two kinds of compensating matrices are used to normalize the feature scales of different views. Additionally, we preserve the geometry structure information of labeled and unlabeled samples by introducing the graph regularization simultaneously. Due to the proposed methods, two effective optimization strategies based on multiplicative update rules are designed. Experiments implemented on six real-world datasets have demonstrated the effectiveness of our FRSMNMF comparing with several state-of-the-art unsupervised and semi-supervised approaches.

Abstract According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) and conducted followed-up for approximately 1 month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) and diastolic BP (DBP) reference measurements were also obtained. Pulse transit time, traditional machine learning (TML), and deep learning (DL) models were evaluated with calibration and calibration-free strategy. TML models were developed using ridge regression, support vector machine, adaptive boosting, and random forest; while DL models using convolutional and recurrent neural networks. The best-performing calibration-based model yielded estimation errors of 1.33 ± 6.43 mmHg for DBP and 2.31 ± 9.57 mmHg for SBP in the overall population, with reduced SBP estimation errors in normotensive (1.97 ± 7.85 mmHg) and young (0.24 ± 6.61 mmHg) subpopulations. The best-performing calibration-free model had estimation errors of -0.29 ± 8.78 mmHg for DBP and -0.71 ± 13.04 mmHg for SBP. We conclude that smartwatches are effective for measuring DBP for all participants and SBP for normotensive and younger participants with calibration; performance degrades significantly for heterogeneous populations including older and hypertensive participants. The availability of cuffless BP measurement without calibration is limited in routine settings. Our study provides a large-scale benchmark for emerging investigations on cuffless BP measurement, highlighting the need to explore additional signals or principles to enhance the accuracy in large-scale heterogeneous populations.

Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data well. Addressing these issues, we propose a novel IMC algorithm using a normalizing alignment strategy (IMCNAS) based on nonnegative matrix factorization. Specifically, the columns of the basis matrices are constrained into unit vector space, which integrates the feature normalization and the optimizing process, and makes the model converge fast and stable. On the other hand, this enables the model to fit the data better and produce more reasonable factorization results. Further, we develop a novel pairwise co-regularization to align incomplete multiple views more directly, without introducing a common consensus matrix like traditional centroid-based co-regularization. Graph regularization is also incorporated in the proposed model to utilize the geometrical information of data. We implement IMCNAS with a centroid-based regularization and a pairwise co-regularization respectively, and leads to two variants, i.e., IMCNAS-1 and IMCNAS-2. Both variants are optimized with multiplicative updating rules. Extensive experiments conducted on various real-world datasets comparing several state-of-the-art IMC methods verified the effectiveness of the proposed methods. The source code is available at:https://github.com/GuoshengCui/IMCNAS.

Graph Neural Networks (GNNs) resemble the Weisfeiler-Lehman (1-WL) test, which iteratively update the representation of each node by aggregating information from WL-tree. However, despite the computational superiority of the iterative aggregation scheme, it introduces redundant message flows to encode nodes. We found that the redundancy in message passing prevented conventional GNNs from propagating the information of long-length paths and learning graph similarities. In order to address this issue, we proposed Redundancy-Free Graph Neural Network (RFGNN), in which the information of each path (of limited length) in the original graph is propagated along a single message flow. Our rigorous theoretical analysis demonstrates the following advantages of RFGNN: (1) RFGNN is strictly more powerful than 1-WL; (2) RFGNN efficiently propagate structural information in original graphs, avoiding the over-squashing issue; and (3) RFGNN could capture subgraphs at multiple levels of granularity, and are more likely to encode graphs with closer graph edit distances into more similar representations. The experimental evaluation of graph-level prediction benchmarks confirmed our theoretical assertions, and the performance of the RFGNN can achieve the best results in most datasets.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

The coronavirus disease 2019 (COVID-19) pandemic is spreading worldwide. Considering the limited clinicians and resources and the evidence that computed tomography (CT) analysis can achieve comparable sensitivity, specificity, and accuracy with reverse-transcription polymerase chain reaction, the automatic segmentation of lung infection from CT scans supplies a rapid and effective strategy for COVID-19 diagnosis, treatment, and follow-up. It is challenging because the infection appearance has high intraclass variation and interclass indistinction in CT slices. Therefore, a new context-aware neural network is proposed for lung infection segmentation. Specifically, the autofocus and panorama modules are designed for extracting fine details and semantic knowledge and capturing the long-range dependencies of the context from both peer level and cross level. Also, a novel structure consistency rectification is proposed for calibration by depicting the structural relationship between foreground and background. Experimental results on multiclass and single-class COVID-19 CT images demonstrate the effectiveness of our work. In particular, our method obtains the mean intersection over union (mIoU) score of 64.8%, 65.2%, and 73.8% on three benchmark datasets for COVID-19 infection segmentation.

Sleep apnea (SA) is a common sleep disorder that occurs during sleep and its symptom is the reduction or disappearance of respiratory airflow caused by upper airway collapse. The SA would cause a variety of diseases like diabetes, chronic kidney disease, depression, cardiovascular diseases, or even sudden death. Early detecting SA and intervention can help individuals to prevent malignant events induced by SA. In this study, we propose a multi-scaled fusion network named SEMSCNN for SA detection based on single-lead ECG signals acquired from wearable devices. The proposed SE-MSCNN mainly has two modules: multi-scaled convolutional neural network (CNN) module and channel-wise attention module. To utilize adjacent ECG segments information to facilitate the SA detection performance, the multi-scaled CNN module consists of three streams of shallow neural networks with segments with various length as inputs to extract different scaled features. To overcome the problem of feature information local concentration for feature fusion with concatenation, a channel-wise attention module with squeeze-to-excitation block is employed to fuse the different scaled features adaptively. Experiment results on PhysioNet Apnea-ECG dataset show that the proposed SE-MSCNN can achieve the best per-segment accuracy of 90.64 % and the best per-recording accuracy of 100 %, which is superior to state-of-the-art SA detection methods with a big margin. The SE-MSCNN with merits of quick response and lightweight parameters can be potentially embedded to a wearable device to provide a SA detection service for individuals in home sleep test.

The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.

We developed a ballistocardiography (BCG)-based Internet-of-Medical-Things (IoMT) system for remote monitoring of cardiopulmonary health. The system composes of BCG sensor, edge node, and cloud platform. To improve computational efficiency and system stability, the system adopted collaborative computing between edge nodes and cloud platforms. Edge nodes undertake signal processing tasks, namely approximate entropy for signal quality assessment, a lifting wavelet scheme for separating the BCG and respiration signal, and the lightweight BCG and respiration signal peaks detection. Heart rate variability (HRV), respiratory rate variability (RRV) analysis and other intelligent computing are performed on cloud platform. In experiments with 25 participants, the proposed method achieved a mean absolute error (MAE)±standard deviation of absolute error (SDAE) of 9.6±8.2 ms for heartbeat intervals detection, and a MAE±SDAE of 22.4±31.1 ms for respiration intervals detection. To study the recovery of cardiopulmonary function in patients with coronavirus disease 2019 (COVID-19), this study recruited 186 discharged patients with COVID-19 and 186 control volunteers. The results indicate that the recovery performance of the respiratory rhythm is better than the heart rhythm among discharged patients with COVID-19. This reminds the patients to be aware of the risk of cardiovascular disease after recovering from COVID-19. Therefore, our remote monitoring system has the ability to play a major role in the follow up and management of discharged patients with COVID-19.

With the fast development of wearable IoT devices, their applications are becoming more and more pervasive, ranging from social networking, payment, and navigation to health and activity monitoring. The security of the communication between these devices is essential to protect the transmitted sensitive information from tampering and eavesdropping. With the integration of accelerometers into wearable IoT devices, the gait-based biometric cryptography technology has emerged as a data securing tool for wearables. This article proposes a lightweight noise-based group key generation method, which utilizes the noise signals imposed on the raw acceleration signals to generate an M-bit key with high randomness and bit generation rate. Moreover, a signed sliding window coding (SSWC)-based common feature extraction method was designed to extract the common feature for sharing the generated M-bit key among devices worn on different body parts. Finally, a fuzzy vault-based group key distribution system was implemented and evaluated using a public data set. The performed comprehensive analysis of the proposed key generation and distribution method proved that the binary keys generated via the introduced noise-based procedure have high entropy and can pass both the NIST and Dieharder statistical tests with high efficiency. The experimental results obtained prove the robustness of the proposed SSWC-based common feature extraction method in terms of the similarity and discriminability of intra- and inter-class features, respectively.

Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.

The ongoing coronavirus disease 2019 (COVID-19) pandemic spread throughout China and worldwide since it was reported in Wuhan city, China in December 2019. 4 589 526 confirmed cases have been caused by the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), by May 18, 2020. At the early stage of the pandemic, the large-scale mobility of humans accelerated the spread of the pandemic. Rapidly and accurately tracking the population inflow from Wuhan and other cities in Hubei province is especially critical to assess the potential for sustained pandemic transmission in new areas. In this study, we first analyze the impact of related multisource urban data (such as local temperature, relative humidity, air quality, and inflow rate from Hubei province) on daily new confirmed cases at the early stage of the local pandemic transmission. The results show that the early trend of COVID-19 can be explained well by human mobility from Hubei province around the Chinese Lunar New Year. Different from the commonly-used pandemic models based on transmission dynamics, we propose a simple but effective short-term prediction model for COVID-19 cases, considering the human mobility from Hubei province to the target cities. The performance of our proposed model is validated by several major cities in Guangdong province. For cities like Shenzhen and Guangzhou with frequent population flow per day, the values of [Formula: see text] of daily prediction achieve 0.988 and 0.985. The proposed model has provided a reference for decision support of pandemic prevention and control in Shenzhen.

Carotid artery lesions could be the pathology of subclinical atherosclerosis and hence lead to the onset of stroke. Early detection of abnormal carotid artery might help to better identify individuals susceptible to stroke. Considering the carotid artery ultrasonography is time-consuming and costly, the object of this paper is to establish a model to detect the status of carotid artery for preliminary screening of stroke, according to the simple physiological examination and survey information. However, most of the previous studies were based on the linear regression or the traditional machine learning methods, those suffer from two limitations. One is the limited labeled samples, and the other one is the missing data. To address these issues, we firstly propose a semi-supervised approach based on a modified variational autoencoder (VAE) to identify the abnormal carotid arteries. In this paper, a mixture of mean and K th nearest neighbours (MKNN) and a modified VAE were used for missing data imputation. The experimental results demonstrate that the proposed method can not only handle the missing values, but also outperform four widely used supervised approaches. Therefore, we can conclude that this semi-supervised model is a promising way to identify the abnormal carotid arteries.

Recently, surface electromyogram (sEMG) has a trend with an increasing number of electrodes to compose a 2-dimension (D) electrode array, which is called high density sEMG (HD-sEMG). However, gesture recognition algorithm with HD-sEMG is still a challenge especially in real time recognition application. This paper researched several spatial attention modules and embedded them to the input layer of neural network. In this way, we can re-weight the input channel to get a better accuracy, robustness and interpretability. By utilizing the Group Convolution Neural Network (CNN), the gesture classification accuracy is improved by 4.44% and 2.71% in CapgMyo and CSL-HDEMG dataset respectively. This method is so efficient that it achieves only with 128 parameters, barely increasing the computational overhead. Meanwhile, we compared the performance in 1-D, 2-D and 3-D CNN, and found that our 1-D group CNN has great advantages in total computational overhead without the loss of accuracy. It provides a practical solution for real time gesture recognition application.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

The papers in this special section focuses on artificial intelligent-driven informatics, sensing, imaging and big data analytics in dealing with the COVID-19 pandemic.

Ambulatory blood pressure (BP) provides valuable information for cardiovascular risk assessment. The present cuff-based devices are intrusive for long-term BP monitoring, whereas cuff-less BP measurement methods based on pulse transit time or multi-parameter are inferior in robustness and reliability by using electrocardiogram (ECG) and photoplethysmogram signals. This study examined a multi-sensor fusion-based platform and algorithm for systolic BP (SBP), mean arterial pressure (MAP), and diastolic BP (DBP) estimation. The proposed multi-sensor platform was comprised of one ECG sensor and two pulse pressure wave sensors for simultaneous signal collection. After extracting 35 features from the collected signals, a weakly supervised feature selection method was proposed for dimension reduction because the reference oscillometric technique-based BP are intermittent and can be redeemed as coarse-grained labels. BP models were then established using a multi-instance regression algorithm. A total of 85 participants including 17 hypertensive and 12 hypotensive patients were enrolled. Experimental results showed that the proposed approach exhibited good accuracy for diverse population with an estimation error of 1.62 ± 7.76 mmHg for SBP, 1.53 ± 6.03 mmHg for MAP, and 1.49 ± 5.52 for DBP, which complied with the association for the advancement of medical instrumentation standards in BP estimation. Moreover, the estimation accuracy is with random daily fluctuations rather than long-term degradation through a maximum two-month follow-up period indicated good robustness performance. These results suggest that the proposed approach is with high reliability and robustness and thus provides a novel insight for cuff-less BP measurement.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from raw ECG signals for fine-grained diseases classification is still a challenging problem today due to variable abnormal rhythms and noise distribution. For ECG analysis, the previous research works depend mostly on heartbeat or single scale signal segments, which ignores underlying complementary information of different scales. In this paper, we formulate a novel end-to-end Deep Multi-Scale Fusion convolutional neural network (DMSFNet) architecture for multi-class arrhythmia detection. Our proposed approach can effectively capture abnormal patterns of diseases and suppress noise interference by multi-scale feature extraction and cross-scale information complementarity of ECG signals. The proposed method implements feature extraction for signal segments with different sizes by integrating multiple convolution kernels with different receptive fields. Meanwhile, joint optimization strategy with multiple losses of different scales is designed, which not only learns scale-specific features, but also realizes cumulatively multi-scale complementary feature learning during the learning process. In our work, we demonstrate our DMSFNet on two open datasets (CPSC_2018 and PhysioNet/CinC_2017) and deliver the state-of-art performance on them. Among them, CPSC_2018 is a 12-lead ECG dataset and CinC_2017 is a single-lead dataset. For these two datasets, we achieve the F1 score [Formula: see text] and [Formula: see text] which are higher than previous state-of-art approaches respectively. The results demonstrate that our end-to-end DMSFNet has outstanding performance for feature extraction from a broad range of distinct arrhythmias and elegant generalization ability for effectively handling ECG signals with different leads.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. In this paper, we proposed a novel end-to-end deep learning method for multiclass arrhythmia detection with multiple stage features fusion. The network is composed of multiple convolution and attention module. Specifically, we use skip connection operation to fuse different levels of features extracted at different stages for target task processing. And the channel-wise attention modules are adopted for effectively extracting the features learned at the different stages. By combining the attention module and convolutional neural network, the discrimination power of the network for ECG classification is improved. We demonstrate the proposed method for ECG classification on an open ECG dataset and compare it with some state-of-the-art methods, which achieves an average F1-score of 81.3% in classification of 8 types of arrhythmias and sinus rhythm. The experimental results convince the efficiency of the proposed method.

With the rapid development of wearable Internet of Things (WIoT) devices, a significant amount of sensitive/private information collected by them poses a considerable challenge to the security of the WIoT devices. The accelerometer-based gait recognition is considered as an emerging and fast-evolving technology in security and access control fields and has achieved outstanding performance at certain fixed walking speeds. However, the gait recognition performance of the above technology deteriorates dramatically when the walking speed varies. To address this issue, both the speed-adaptive gait cycle segmentation method and individualized matching threshold generation method were proposed in this paper. Furthermore, the contrast experiments were conducted on the ZJU-GaitAcc public dataset sampled from five different body locations and the self-collected dataset sampled at various walking speeds. The experimental results indicated the average gait recognition and user authentication rates of 96.9% and 91.75%, respectively. As compared to the available state-of-the-art methods based on the fixed walking speeds and constant thresholds, the proposed method improved the gait recognition by 25.8% and user authentication by 21.5%.

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multiscaled fusion of deep convolutional neural network (MS-CNN) is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The MS-CNN employs the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales. The experimental results show that the proposed MS-CNN achieves 96.99% of classification accuracy on ECG recordings cropped/padded to 5 s. Especially, the best classification accuracy, 98.13%, is obtained on ECG recordings of 20 s. Compared with artificial neural network, shallow single-stream CNN, and VisualGeometry group network, the MS-CNN can achieve the better classification performance. Meanwhile, visualization of the learned features from the MS-CNN demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering. The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices.

In a wireless body area network (WBAN), walking movements can result in rapid channel fluctuations, which severely degrade the performance of transmission power control (TPC) schemes. On the other hand, these channel fluctuations are often periodic and are time-synchronized with the user's gait cycle, since they are all driven from the walking movements. In this paper, we propose a novel gait-cycle-driven transmission power control (G-TPC) for a WBAN. The proposed G-TPC scheme reinforces the existing TPC scheme by exploiting the periodic channel fluctuation in the walking scenario. In the proposed scheme, the user's gait cycle information acquired by an accelerometer is used as beacons for arranging the transmissions at the time points with the ideal channel state. The specific transmission power is then determined by using received signal strength indication (RSSI). An experiment was conducted to evaluate the energy efficiency and reliability of the proposed G-TPC based on a CC2420 platform. The results reveal that compared to the original RSSI/link-quality-indication-based TPC, G-TPC reduces energy consumption by 25% on the sensor node and reduce the packet loss rate by 65%.

Energy consumption is a critical issue for battery‐powered sensor nodes in wireless body area networks (WBANs). Many energy‐efficient medium access control (MAC) protocols have been proposed and investigated for energy‐constrained WBANs. These protocols assume that the battery has an ideal linear model. However, in practical application, the battery model is non‐linear. When the lithium (Li)‐ion battery experiences an idle period, the unavailable charge can be transferred to the available charge and the battery lifetime can be extended. In this study, the authors propose a battery‐friendly MAC (BFMAC) protocol based on the non‐linear electrochemical properties of the Li‐ion battery. A remaining charge factor (RCF) of the battery is introduced. On the basis of the RCF, a combined packets transmission scheme is designed to maximise the lifetime of the battery‐powered sensor nodes. The performance of the BFMAC is evaluated through simulation. The results show that the presented BFMAC and the joint packets transmission scheme prolonged the network lifetime by 25.45% over the sensor‐MAC and 33.61% over the IEEE 802.15.4 non‐beacon‐enabled MAC.

Shortest path distance retrieval is a core component in many important applications. For a decade, hub labeling (HL) techniques have been considered as a practical solution with fast query response time (e.g., 1--3 orders of magnitude faster), competitive indexing time, and slightly larger storage overhead (e.g., several times larger). These techniques enhance query throughput up to hundred thousands queries per second, which is particularly helpful in large user environment. Despite the importance of HL techniques, we are not aware of any comprehensive experimental study on HL techniques. Thus it is difficult for a practitioner to adopt HL techniques for her applications. To address the above issues, we provide a comprehensive experimental study on the state-of-the-art HL technique with analysis of their efficiency, effectiveness and applicability. From insightful summary of different HL techniques, we further develop a simple yet effective HL techniques called Significant path based Hub Pushing (SHP) which greatly improves indexing time of previous techniques while retains good query performance. We also complement extensive comparisons between HL techniques and other shortest path solutions to demonstrate robustness and efficiency of HL techniques.

Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14 features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted for beat-to-beat BP estimation. A genetic algorithm-based feature selection method was then used to select BP indicators for each subject. Multivariate linear regression and support vector regression were employed to develop the BP model. The accuracy and robustness of the proposed approach were validated for static, dynamic, and follow-up performance. Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy in static BP estimation, with a correlation coefficient and mean error of 0.852 and -0.001 ± 3.102 mmHg for systolic BP, and 0.790 and -0.004 ± 2.199 mmHg for diastolic BP. Similar performance was observed for dynamic BP estimation. The robustness results indicated that the estimation accuracy was lower by a certain degree one day after model construction but was relatively stable from one day to six months after construction. The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentially novel insights for cuffless BP estimation.

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. Instead, it follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a novel, locally adaptive technique, our method offers (i) proven optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time (i.e., time to refresh the query results) than the current state-of-the-art.

An important constraint in wireless body area network (WBAN) is to maximise the energy‐efficiency of wearable devices due to their limited size and light weight. Two experimental scenarios; ‘right wrist to right hip’ and ‘chest to right hip’ with body posture of walking are considered. It is analyzed through extensive real‐time data sets that due to large temporal variations in the wireless channel, a constant transmission power and a typical conventional transmission power control (TPC) methods are not suitable choices for WBAN. To overcome these problems a novel energy‐efficient adaptive power control (APC) algorithm is proposed that adaptively adjusts transmission power (TP) level based on the feedback from base station. The main advantages of the proposed algorithm are saving more energy with acceptable packet loss ratio (PLR) and lower complexity in implementation of desired tradeoff between energy savings and link reliability. We adapt, optimise and theoretically analyse the required parameters to enhance the system performance. The proposed algorithm sequentially achieves significant higher energy savings of 40.9%, which is demonstrated by Monte Carlo simulations in MATLAB. However, the only limitation of proposed algorithm is a slightly higher PLR in comparison to conventional TPC such as Gao's and Xiao's methods.

Atrial activity (AA) signal must first be extracted from atrial fibrillation electrocardiogram (AF ECG) before it is used to characterise AF. However, extracting AA signal is not an easy task, especially from single‐lead ECG recording. The AA signals within QRS intervals extracted by the existing single‐lead extraction methods are often heavily distorted due to the existence of large QRS residuals. In this study, the authors focus on reducing the QRS residuals in the extracted AA signals, and propose a novel signal extrapolation based method. AA signal is assumed to be band‐limited, and a dedicated extrapolation formula is derived. Based on this extrapolation formula, the AA samples within QRS interval are reconstructed by using the ones in the adjacent SQ segments. The experiments with simulated AF ECGs showed that, after using the proposed method, the normalised mean square error of the AA signal extracted by average beat subtraction method decreased by 26–50%, 15–36%, 12–40 and 42–63% for simulated AF ECGs in lead I, II, V 1 and V 6 , respectively. Experiments with real AF ECG also proved that the proposed method is able to greatly reduce the ventricular residuals of the extracted AA signal.

Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively.

The year 2016 marks the 200th birth anniversary of Carl Friedrich Wilhelm Ludwig (1816-1895). As one of the most remarkable scientists, Ludwig invented the kymograph, which for the first time enabled the recording of continuous blood pressure (BP), opening the door to the modern study of physiology. Almost a century later, intraarterial BP monitoring through an arterial line has been used clinically. Subsequently, arterial tonometry and volume clamp method were developed and applied in continuous BP measurement in a noninvasive way. In the last two decades, additional efforts have been made to transform the method of unobtrusive continuous BP monitoring without the use of a cuff. This review summarizes the key milestones in continuous BP measurement; that is, kymograph, intraarterial BP monitoring, arterial tonometry, volume clamp method, and cuffless BP technologies. Our emphasis is on recent studies of unobtrusive BP measurements as well as on challenges and future directions.

Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.

Expanding our knowledge about human mobility is essential for building efficient wireless protocols and mobile applications. Previous human mobility studies have typically been built upon empirical single-source data (e.g., cellphone or transit data), which inevitably introduces a bias against residents not contributing this type of data, e.g., call detail records cannot be obtained from the residents without cellphone activities, and transit data cannot cover the residents who walk or ride private vehicles. To address this issue, we propose and implement a novel architecture mPat to explore human mobility using multi-source data. A reference implementation of mPat was developed at an unprecedented scale upon the urban infrastructures of Shenzhen, China. The novelty and uniqueness of mPat lie in its three layers: (i) a data feed layer consisting of real-time data feeds from 24 thousand vehicles, 16 million smart cards and 10 million cellphones; (ii) a mobility abstraction layer exploring the correlation and divergence among the multi-source data to analyze and infer human mobility; and (iii) an application layer to improve urban efficiency based on the human mobility findings of the study. The evaluation shows that mPat achieves a 75% inference accuracy, and that its real-world application reduces passenger travel time by 36%.

Recently, big data has been evolved into a buzzword from academia to industry all over the world. Benchmarks are important tools for evaluating an IT system. However, benchmarking big data systems is much more challenging than ever before. First, big data systems are still in their infant stage and consequently they are not well understood. Second, big data systems are more complicated compared to previous systems such as a single node computing platform. While some researchers started to design benchmarks for big data systems, they do not consider the redundancy between their benchmarks. Moreover, they use artificial input data sets rather than real world data for their benchmarks. It is therefore unclear whether these benchmarks can be used to precisely evaluate the performance of big data systems. In this paper, we first analyze the redundancy among benchmarks from ICTBench, HiBench and typical workloads from real world applications: spatio-temporal data analysis for Shenzhen transportation system. Subsequently, we present an initial idea of a big data benchmark suite for spatio-temporal data. There are three findings in this work: (1) redundancy exists in these pioneering benchmark suites and some of them can be removed safely. (2) The workload behavior of trajectory data analysis applications is dramatically affected by their input data sets. (3) The benchmarks created for academic research cannot represent the cases of real world applications.

We present a low power electrocardiogram (ECG) acquisition system with 28mW total power dissipation in this paper, which is almost half of traditional implementation below our measurements. The majority of this power reduction is due to use a fully integrated analog front-end (AFE) with inherent all the necessary functions that needed in traditional ECG acquisition implementation. This AFE is implemented in 0.18um CMOS technology, consumes only around 0.3mW. Therefore, our acquisition system is well suited for portable medical applications.

Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized device, called TaxiBox. In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers.

Recently, a kind of lightweight and resource‐efficient biometrics‐based security solutions were proposed for the emerging body sensor network (BSN). In such security solutions, physiological characteristics that can be captured by individual sensors of BSN were proposed to generate entity identifiers (EIs) for securing keying materials by a biometric approach. In this study, the authors focus on an improved key distribution solution with the energy distribution information of physiological signals (EDPSs) ‐based EIs. Firstly, different EDPS‐based EI generation schemes are studied. Based on the existing multi‐windows Fourier transform scheme, a modified one with single‐window is proposed to improve the identification performance of the generated EIs. Then, a different method based on the discrete cosine transform of the autocorrelation sequence of physiological signals is proposed aiming for a significant increase in identification rates. The performances of time‐varying randomness and identification rates are evaluated to examine EI's feasibility in securing the transmission of keying materials. Based on the characteristics of generated EIs, the corresponding key distribution solution, that is, user‐dependent fuzzy vault, is proposed. A detailed system performance analysis in terms of half total error rate, anti‐attack ability, as well as computational complexity, is conducted to demonstrate the effectiveness of the proposed solution.

As an emerging state-of-the-art technology, cloud computing has been applied to an extensive range of real life situations. Healthcare is one of such important application fields. We developed a healthcare system, named HCloud, after comprehensive analysis of requirements of healthcare, which based on cloud platform with characteristics of loose coupling algorithms modules and powerful parallel computing capabilities to compute the detail of these indicators for the purpose of preventive healthcare service. The proposed system can support huge physiological data storage and process heterogeneous data for various health care applications such as automated electrocardiogram (ECG) analysis, providing an early warning mechanism for users with chronic disease. The architecture of the cloud platform for physiological data storage, computing, data mining, and feature selections are described. Performance evaluations based on testing has demonstrated the effectiveness and usability of the system.

The fuzzy vault scheme, which has been most widely used in biometric systems, has some weaknesses while applied in securing Body Sensor Network (BSN) communications. This is mainly because of the dynamic random characteristics of biometric identifiers independently generated by sensor nodes based on self-captured physiological signals. A modified fuzzy vault scheme is proposed to overcome this problem, aiming for a significant reduction in recognition errors. Error-correction encoding/decoding process is deployed in a way different from the existing ones at the transmitter/receiver to reduce the effect of bit differences in dynamic random patterns between biometric identifiers. An enveloping process is further deployed before the projection of real points onto the keying material constructed polynomial to protect the real points from being revealed by various potential attacks. This enveloping process can also be deployed in the traditional fuzzy vault scheme to prevent the known attacks based on statistical analysis of points in the vault. A detailed performance analysis in terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), as well as anti-attack capability was conducted to demonstrate the effectiveness of our solution.