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SMANet: A Model Combining SincNet, Multi-Branch Spatial - Temporal CNN, and Attention Mechanism for Motor Imagery BCI

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IEEE Transactions on Neural systems and Rehabilitation engineering 2025年 33卷 1497-1508页

作者： Wang, Danjie Wei, Qingguo Nanchang University Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction School of Information Engineering Nanchang330031 China

Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering;Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability;Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs. © 2025 IEEE.

关键词： Signal to noise ratio

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Analysis and Design of a Three-Coil Coplanar Couper WPT System with Automatic Seamless CC to CV for Charging Battery 11th

Analysis and Design of a Three-Coil Coplanar Couper WPT Syst...

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11th Frontier Academic Forum of Electrical engineering, FAFEE 2024

作者： Feng, Qunqun Li, Xi Song, Ke Chen, Bin Luo, Bin School of Information Engineering Nanchang University Nanchang330036 China Key Laboratory of Intelligent Systems and Human-Machine Interaction Jiangxi China

ISBN: (纸本)9789819788118

Load-independence constant current (CC) and constant voltage (CV) charging are the two stages of the battery charging. To simplify the complexity of the control strategy during the switching process from the CC mode to the CV mode of the wireless charging system, a low-profile three-coil inductive power transfer (IPT) system based on a switchable hybrid structure is proposed, where all coils are placed coplanar to save space and low-profile ferrite plates are strategically placed on and beneath the junctions of adjacent coils to enhance primary couplings and suppress cross couplings. Two switches are set on the primary side to reconfigure the resonant circuit to choose CC mode or CV mode, while increasing the compactness of the receiver side. The current in the primary coil remains essentially unchanged, when the load is open-circuited, short-circuited, or removed, indicating a higher level of safety in the system. Throughout the charging process, zero phase angle (ZPA) and zero voltage switching (ZVS) are maintained. Thus, a higher power transmission efficiency (PTE) is obtained. An experimental platform is built to verify the theory. The experimental and theoretical results are in good agreement, and the maximum PTE was measured as 96.73%. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Resonant circuits

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Adaptive Closed-Loop Functional Electrical Stimulation System with Visual Feedback for Enhanced Grasping in Neurological Impairments

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 678-686页

作者： Lin, Chengyu Cheng, Kong Hoi Pan, Wei Sun, Jinxin Gou, Guotao Fu, Junyun Leng, Yuquan Fu, Chenglong Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Department of Mechanical and Energy Engineering Shenzhen518055 China Guilin University of Electronic Technology Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center School of Life and Environmental Sciences Guilin541004 China

Grasping is a critical motor skill essential for daily activities, but it is often compromised in individuals with neural impairments. Functional Electrical Stimulation (FES) has emerged as a promising intervention, utilizing electrical pulses to stimulate muscles and thereby restore impaired motor functions. However, existing closed-loop FES systems depend on pre-calibrated angles or forces specific to individual objects, which limits their practicality in dynamic, real-world environments with varying object *** paper presents a novel closed-loop FES (CLFES) system with visual feedback, designed to dynamically adjust stimulation parameters based on real-time interaction states without requiring object-specific calibration. The system employs a finite state machine to manage sequential grasp-release tasks and integrates a visual perception module for slip detection and intent recognition. The system was tested with two individuals with disabilities on five common household objects. Experimental results demonstrate significant improvements, including a 42.6% increase in success rate and a 45.9% reduction in task completion time compared to tasks performed without the system. These results underscore the system’s potential to improve daily task performance for individuals with neural impairments. © 2018 IEEE.

关键词： Visual servoing

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Robust Finite-Time Containment of Networked Heterogeneous Nonlinear systems with Intermittent Measurement Only

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IEEE Transactions on Network Science and engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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Complete Gait Phases Recognition Based on Muscle Synergy Using PSO-CNN-LSTM Algorithm

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IEEE Sensors Journal 2025年第10期25卷 16775-16786页

作者： Zhang, Kewen Li, Xiaoling Chen, Xin Yu, Longjie Jin, Yinan Fan, Bingfei Du, Mingyu Bao, Guanjun Wu, Xinyu Cai, Shibo Zhejiang University of Technology College of Mechanical Engineering Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education Hangzhou China Zhejiang University of Technology College of Mechanical Engineering Hangzhou China ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship Fuyang District Hangzhou311400 China Chinese Academy of Sciences Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China

Accurately recognizing gait phases, by applying proper instrumentation and measurement, is significant in walking rehabilitation training for patients with impaired mobility. In this study, seven phases of complete stand-walk-stand cycle as well as continuous daily walking were recognized based on muscle synergy and PSO-CNN-LSTM model. Firstly, the complete stand-walk-stand walking cycle were divided into starting phase, terminal swing phase, loading response phase, mid-stance phase, terminal stance phase, initial swing phase, and stopping phase, based on the features of surface electromyography (sEMG) collected by portable sEMG acquisition system and measured motion data of lower limb. Secondly, a muscle weight matrix and an activation sequence matrix were calculated by using non-negative matrix factorization (NMF). Finally, a PSO-CNN-LSTM network was designed to recognize the complete stand-walk-stand cycle by applying above mentioned matrices as input. Fourteen subjects volunteered to perform linear walking experiments under procedure to verify the feasibility of the proposed approach by comparing results with other classifiers and features. Experimental results show that the proposed approach was capable of achieving an average recognition accuracy of up to 85.384%. This work will offer promising gait recognition method for rehabilitation robots to achieve natural and flexible human-robot interaction. © 2001-2012 IEEE.

关键词： Non-negative matrix factorization

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ULA-MSRTI: The Unsupervised Link Analysis Based Movement Superposition Radio Tomographic Imaging in Passive UHF RFID Scenarios

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Bobo Ma, Yongtao Zhang, Xianchao Liang, Xiuyan Tianjin University School of Microelectronics Tianjin300072 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China

Device-free localization (DFL) is a promising technique for localizing targets without any auxiliary devices. Passive ultra high frequency radio frequency identification (pUHF RFID) based radio tomographic imaging (RTI) emerges as a particularly favorable DFL approach due to its inherent advantages. However, accurately localizing multiple targets remains a challenge for it. To address the challenge, we propose a novel unsupervised link analysis based movement superposition RTI (ULA-MSRTI), including movement superposition RTI (MSRTI) model and the unsupervised link analysis (ULA) scheme. MSRTI models the influence of target movement on wireless signals at every moment. Then, we design the ULA scheme to determine received signal strength (RSS) variations of target-affected links required for MSRTI. Firstly, the scheme adopts statistical analysis to remove wireless links that are mainly affected by noise. Then, we design an adaptive selection algorithm based on density-based spatial clustering of applications with noise to eliminate wireless links affected by burst signals and self-environmental multipath. After that, the scheme matches RSS variations of remaining links with the single-target database to recover RSS variations of target-affected links at one moment. Finally, we input recovered RSS variations into MSRTI to obtain the locations of multiple targets. The experiment shows that ULA-MSRTI's performance improves by an average of over 14.6% compared to other methods, and ULA-MSRTI is the insensitive to the locations of multiple targets. © 2025 IEEE.

关键词： UHF devices

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Collaborative Cloud-Edge Computing with WPT for Air-Ground Integrated IoMT Network: A URLLC Aware CAFL-Based Approach

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IEEE Transactions on Communications 2025年

作者： Qin, Peng Wang, Yifei Zhang, Xianchao North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering Beijing102206 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing314001 China Jiaxing University Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province 314001 China

The global aging process accelerates, making geriatric disease prevention and management urgent. Combining IoMT with wearable devices for health monitoring is effective, but IoMT task offloading has challenges like weak terminal processing, poor battery life, low - latency needs, and privacy protection difficulties. To address these challenges, this paper proposes a Cloud-edge Collaborative Air-ground Integrated IoMT Network (C2AI2N) that combines ground Base Stations, air-based Unmanned Aerial Vehicles (UAVs) and cloud servers to adapt to future IoMT scenarios and enhance the system capabilities. Edge servers are able to use Wireless Power Transfer (WPT) to provide energy to wearable devices. We then consider the Ultra-Reliable Low Latency Communication (URLLC) task constraints as well as power limitations, and formulate the problem of minimizing the overall system energy consumption. Next, we employ the Lyapunov optimization method to break down the problem into three sub-problems: 1) device-side task splitting, 2) task offloading as well as charging strategy, 3) server-side resource allocation, which are addressed via Lagrange multiplier method, Clustered Asynchronous Federated Learning (CAFL) based rainbow Deep Q-Network (DQN) approach, and the coati optimization algorithm, respectively. Extensive simulations demonstrate that the proposed method outperforms other baselines. Specifically, the energy consumption of the proposed method can be reduced by over 30.37% compared to the baselines. In addition, the backlog of the URLLC virtual queue can be decreased by more than 59.27%, achieving superior URLLC satisfaction rates. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

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IEEE Wireless Communications Letters 2025年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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InterMap-NRTI: A Novel Radio Tomographic Imaging Positioning Model Based on the Intersection Map of Wireless Links

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Bobo Ma, Yongtao Zhang, Xianchao Zhang, Jing Huang, Zixuan University of Science and Technology of China School of Cyber Science and Technology Anhui Hefei230027 China Tianjin University School of Microelectronics Tianjin300072 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Science and Technology of China Science Island Branch of Graduate School Anhui Hefei230031 China Institute of Space Integrated Ground Network Anhui Hefei230088 China Zixuan Huang is with the Institute of Space Integrated Ground Network Anhui Hefei230088 China

Device-free localization (DFL) is a potential positioning technique that can localize targets without requiring any device. Radio tomographic imaging (RTI) is one of the most favorable DFL methods and has many advantages over other methods. In multi-target localization indoors, target-affected wireless signal is inevitably contaminated with non-negligible interference of noise and complex multipath. Existing RTI positioning models utilize contaminated signals for multi-target localization, resulting in low positioning performance. To decrease the effect of contaminated signals, we propose a novel RTI positioning model based on the intersection map of wireless links (InterMap-NRTI). InterMap-NRTI utilizes the statistical method to determine wireless links mainly affected by targets and the interpolation method to complement the wireless links that are interrupted due to hardware malfunction and targets' obstruction. The next step is to formulate the definitions to extract high-intersections closest to the target based on the intersection map of target-affected links. Subsequently, we establish a novel positioning model based on signal strengths of wireless links and high-intersections in each cluster determined by DBSCAN algorithm using a saddle surface model. Finally, Hooke-Jeeves method is used to search for the optimal solution of the positioning model. The experiment shows that InterMap-NRTI's accuracy and identification accuracy rate outperforms other methods by an average of more than 30% and more than 14%, respectively, and InterMap-NRTI has a good tolerance for scenario layout. © 2025 IEEE.

关键词： UHF devices

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