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TS-CAN+: A Improved TS-CAN Architecture for Non-Contact Heart Rate Measurement

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Li, Yihang Huang, Junjian Zhao, Jichao Wu, Dayu Zheng, Mao Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China University of Auckland Auckland New Zealand

Heart rate is one of the most crucial physiological indicators of the human body, which can reflect the health status of the cardiovascular and cerebrovascular. Therefore, heart rate monitoring in real-time is essential for the prevention and treatment of cardiovascular and cerebrovascular diseases. Remote Photoplethysmography (rPPG) is a non-contact heart rate measurement method, which addresses the limitation of traditional heart rate measurement methods that need to contact with the subjects, and has a broad application prospect in ward vital signs monitoring and remote physiological health monitoring. How to use the rPPG method to measure heart rate more accurately is still a challenging problem. In this paper, we propose the TS-CAN+ method, which is based on TS-CAN and further incorporates convolutional block attention modules and replaces the ordinary convolution of the appearance branch with the depthwise separable convolution to improve the network accuracy. To validate the performance of our model, we conduct extensive experiments on the public PURE, UBFC-rPPG, and MMPD datasets. The experimental results indicate that compared with TS-CAN, the mean absolute error of TS-CAN+ is reduced by 37.21% in the cross-dataset test on UBFC-rPPG dataset and reduced by 12.18% on MMPD dataset. IEEE

关键词： Convolution

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Event-Triggered Adaptive Finite-Time Control for Switched Cyberphysical Systems With Uncertain Deception Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 136-146页

作者： Wang, Xin Zhou, Yuhao Luo, Biao Jiang, Yuming Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Automation Central South University Changsha410083 China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7491 Norway T&M University at Qatar Doha23874 Qatar

This article is concerned with the event-triggered adaptive finite-time control problem for a class of switched cyber-physical systems (CPSs) subject to uncertain deception attacks. We deploy the finite time command filter to alleviate the possible explosion of complexity rendered by the virtual control signal’s duplicative differentiation. To stabilize the target system with a guaranteed disturbance rejection performance index, a disturbance observer is constructed by using fuzzy logic systems (FLS) with the hope of estimating the unknown external disturbance. Furthermore, the Nussbaum gain is utilized to resist the unknown control efficiency generated by uncertain deception attacks. Based on the common Lyapunov function (CLF) method, sufficient conditions are derived to ensure that all signals in the closed-loop system are uniformly bounded at a finite time. Finally, the simulation example shows the validity and efficacy of the developed control method. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Closed loop systems

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Kernel least mean square algorithm with mixed kernel

Kernel least mean square algorithm with mixed kernel

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Qitang Sun Lujuan Dang Wanli Wang Shiyuan Wang College of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

This paper presents a novel kernel least square algorithm with mixed kernel (KLMS-MK) to improve the filtering performance of kernel least mean square (KLMS). By applying the convex combination method to the kernel function in KLMS, KLMS-MK bears the advantages of both the Gaussian kernel and the Laplace kernel. In KLMS-MK, the mixed parameter for the convex combination is updated with the stochastic gradient descent. Therefore, the steady-state mean square error (MSE) and the convergence rate are improved by KLMS-MK, simultaneously. Simulation results on chaotic time series prediction and nonlinear regression validate the excellent performance of KLMS-MK from the aspects of the convergence rate and estimation accuracy.

关键词： Kernel Testing Signal processing algorithms Gaussian noise Convergence Approximation algorithms Time series analysis

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Exploring Regulation Effect of Jogging on Human Emotion Based on Pulse Signal

Exploring Regulation Effect of Jogging on Human Emotion Base...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Jianguang Yuan Guangyuan Liu Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781509035595

Until recently, claims for the psychological benefits of physical exercise have tended to precede supportive evidence. Actually, emotional effects of exercise remain confusing, both positive and negative effects being reported. Meanwhile, several studies have shown that the aerobic exercise has impact on human's mood through making subjective evaluation of the Profile of Mood States (POMS) before and after exercise. However, the conclusion via comparing with the Profile of Mood States (POMS) scores was lack of the objective basis. In order to explain the result that exercise has effects of adjustment to fear emotion from the objective point of view, therefore, this study researched exercise which can improve mood based on the subjects' physiological signal, and analyzed data using the affective computing. Issues involved in this study are discussed.

关键词： Mood Physiology Time series analysis Affective computing Fatigue Computational intelligence nonlinear circuits

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TSW-FD: A novel temporal and spatial domain weight analysis of feature difference for micro-expression spotting

TSW-FD: A novel temporal and spatial domain weight analysis ...

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2020 International Symposium on Automation, information and Computing, ISAIC 2020

作者： Zhang, Zhihao Mo, Fan Zhao, Ke Chen, Tong Fu, Xiaolan State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Psychology University of Chinese Academy of Sciences Beijing100049 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing400715 China Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing400715 China

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME Ⅱ and SMIC-E-HS. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Finite difference method

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Accelerated Distributed Optimization over Directed Graphs with Row and Column-Stochastic Matrices

Accelerated Distributed Optimization over Directed Graphs wi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jinhui Hu Yifan Zhu Huaqing Li Zheng Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In this paper, we study distributed optimization problem over multi-agent networks where the goal is to find the global optimal of a sum of convex functions over strongly connected and directed graphs. A novel distributed algorithm is proposed where both row and column-stochastic matrices are utilized to bypass the limits of the implementation of doubly-stochastic matrices or eigenvector estimation in related work. Besides, it has an evident expression and accelerated convergence by introducing the momentum term. Combining the Generalized Small Gain Theorem with Linear Time Invariant (LTI) system inequality, the algorithm is proved to be able to linearly converge to the exact optimal solution. Furthermore, the ranges of stepsize and momentum paramater are characterized, respectively. Finally, simulation results illustrate effectiveness of the method and correctness of theoretical analysis.

关键词： Radio frequency Convergence Optimization Acceleration Eigenvalues and eigenfunctions Convex functions Linear matrix inequalities

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An ECG Monitoring System Based on Android

An ECG Monitoring System Based on Android

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International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Ruiqi Zhang Zhengchun Hua Chen Chen Guangyuan Liu Wanhui Wen School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728165172

ISBN: (纸本)9781728165189

This study aims to design a real-time electrocardiogram (ECG) and heart rate monitoring system based on Android. The system connects Android device and a Shimmer3 ECG module, which is a wearable ECG device collecting ECG data with five electrodes. The ECG signal is amplified and filtered by internal circuit processing in Shimmer3 ECG module, and the ECG data is transmitted to an Android phone via Bluetooth. The baseline drift in ECG signal is removed through wavelet decomposition and reconstruction. Then, the processed ECG data are used to find the R wave peaks by an algorithm for automatic R peak locating and RR interval calculation. RR interval waveform is plotted and displayed on an Android phone in real time. Finally, ECG and RR intervals are saved for data playback and subsequent analysis.

关键词： Electrocardiography Bluetooth Real-time systems Smart phones Monitoring Heart rate

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Row-Stochastic Matrices Based Distributed Optimization Algorithm With Uncoordinated Step-Sizes

Row-Stochastic Matrices Based Distributed Optimization Algor...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Huaqing Li Jinmeng Wang Zheng Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

This paper investigates the distributed optimization problem over multi-agent networks, in which the target of agents is to collaboratively optimize the sum of all local objective functions. Each local objective function is uniquely known by a single agent. We concentrate on the scenario where communication among agents is portrayed as directed graphs. Based on the exact first order method, a fully distributed optimization algorithm is proposed to deal with the optimization problem. The proposed algorithm utilizes row-stochastic matrices and uncoordinated step-sizes, which exactly drives all agents to converge to the global optimization solution. Under the assumptions that the global objective function is strong convex and the local objective functions have Lipschitz continuous gradient, we show that the proposed algorithm linearly converges to the global optimization solution as long as the maximum step-size of agents does not exceed an explicitly characterized upper bound. Finally, numerical experiments are presented to demonstrate the correctness of theoretical analysis.

关键词： Distributed optimization directed graphs uncoordinated step-sizes row-stochastic matrices directed graphs Objective function Optimization algorithms Agents Global optimization Dyscoordination NETWORKING AGENT Distribute matrices

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A Novel Distributed Algorithm to Seek GNE for Aggregative Games via Primal-Dual Proximal Gradient

A Novel Distributed Algorithm to Seek GNE for Aggregative Ga...

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International Conference on Control, Automation and Robotics (ICCAR)

作者： Zhe Li Huaqing Li Liang Ran Songyang Li Lvming Fan Lifeng Zheng Jun Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

In this paper, the generalized Nash equilibria (GNE) problems over the aggregative game is concerned inspired by the widely application of non-cooperative game in many fields. In the game, each agent aims to minimize its own objective function that depends on both its private decision and the average decision information of all agents’. Furthermore, decision variables are within feasible decision sets and shares a global coupling inequality constraint. To seek the GNE, a novel distributed primal-dual proximal algorithm is proposed. Meanwhile, the proposed algorithm adopts edge-based communication model and takes uncoordinated step-sizes that avoid the conservativeness. By the theories of monotone inclusion and averaged operators, convergence of the algorithm is proved. Finally, numerical studies on electric vehicles path and destination strategy problem are conducted to clarify the efficiency of the proposed algorithm.

关键词：

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Robust Multi-kernel Generalized Maximum Correntropy Filters

Robust Multi-kernel Generalized Maximum Correntropy Filters

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IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Qiaona Sun Tao Zhang Xuewei Huang Shiyuan Wang College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

The traditional multi-kernel filtering algorithms are proposed by combining multiple kernel with the second similarity measure. Nevertheless, these multi-kernel filters suffer from both heavy computation burden and divergence for impulsive noise environments. To deal with these challenges for online applications, the multi-kernel generalized maximum correntropy (MKGMC) algorithm is developed for improving both precision and stability under the generalized maximum correntropy criterion. To alleviate the complex network structure of MKGMC, using the vector quantization strategy in MKGMC develops quantized MKGMC (QMKGMC) to reduce the computational and spatial complexity. From the viewpoints of precision and stability, the proposed algorithms outperform other typical compared algorithms on time series prediction.

关键词： Vector quantization Stability criteria Time series analysis Filtering algorithms Predictive models Prediction algorithms Robustness

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