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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiaxiu Wang Xinyu Liu Yingjuan Jia Tong Chen Jingru Huang Zheyuan Zhang College of Electronic and Information Engineering Southwest University Chongqing China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China Chongqing Key Laboratory of Non-Linear Circuits and Intelligent Information Processing Southwest University Chongqing China

Stress is an integral state that affects physical and mental health of individuals. Tissue Oxygen Saturation (StO2) is an emerging physiological signal and can reflect different stress states. Previous studies have simply extracted features of StO2 from specific regions of the face without exploring potential associations between them. In this paper, we analyze the facial Region of Interest (ROI) based on StO2, so as to explore the deep relationship between ROIs. Building upon this, we construct, for the first time, a StO2-based facial graph structure for stress recognition by using ROIs as nodes. In addition, we propose a new graph pooling method called FTPool which allows to measure node significance in terms of both node characteristics and graph topology. Finally, we further propose a dual-stream network (GCNet) combining GNN and CNN for the baseline-independent stress recognition. The experimental results show that the proposed GCNet can achieve state-of-the-art (SOTA) results with an accuracy of 78.57% on the original unbalanced database.

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A Methodology for Palm Vein Image Enhancement and Visualization

A Methodology for Palm Vein Image Enhancement and Visualizat...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Min Peng Chongyang Wang Tong Chen Guangyuan Liu School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University

Palm vein recognition is a developing biometric identification *** can be used in physical security and information security for selective control of access to a place or *** to the palm vein recognition,vein enhancement is generally required for a better *** this paper,we propose a vein enhancement method combining Karhunen-Loeve(KL) transform and hue-preserving color image enhancement *** results show that the proposed method is robust to shadow affect,can give better visualization results,and can significantly improve the contrast and contour details of image(the contrast ratio increases by 2.3%,edge intensity increases by 8.8%,and definition increases by 66.9%) comparing to the traditional enhancement methods.

关键词： palm vein recognition palm vein enhancement multispectral image KL transform color enhancement formatting

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Stability analysis of hybrid neural networks with impulsive time window

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International Journal of Biomathematics 2017年第1期10卷 195-208页

作者： Xin Wang Hui Wang Chuandong Li Tingwen Huang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronics and Information EngineeringSouthwest University Chongqing 400715 P. R. China Key Laboratory of Machine Perception and Children's Intelligence DevelopmentChongqing University of Education P. R. China College of Mathematics Science Chongqing Normal University Chongqing 401331 P. R. China Texas A&M University at QatarDoha P. O. Box 23874 Qatar

The urgent problem with impulsive moments cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, in this paper, a novel class of system with impulsive time window is proposed. Different from the conventional impulsive control strategies, the main characteristic of the impulsive time window is that impulse occurs in a random manner. Moreover, for the importance of the hybrid neural networks, using switching Lyapunov functions and a generalized Hanlanay inequality, some general criteria for asymptotic and exponential stability of the hybrid neural networks with impulsive time window are established. Finally, some simulations are provided to further illustrate the effectiveness of the results.

关键词： Hybrid neural networks impulsive time window stability.

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

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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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A methodology for palm vein image enhancement and visualization

A methodology for palm vein image enhancement and visualizat...

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IEEE International Conference of Online Analysis and Computing Science (ICOACS)

作者： Min Peng Chongyang Wang Tong Chen Guangyuan Liu Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781467377560

Palm vein recognition is a developing biometric identification technology. It can be used in physical security and information security for selective control of access to a place or resource. Prior to the palm vein recognition, vein enhancement is generally required for a better recognition. In this paper, we propose a vein enhancement method combining Karhunen-Loeve (KL) transform and hue-preserving color image enhancement technology. Experimental results show that the proposed method is robust to shadow affect, can give better visualization results, and can significantly improve the contrast and contour details of image (the contrast ratio increases by 2.3%, edge intensity increases by 8.8%, and definition increases by 66.9%) comparing to the traditional enhancement methods.

关键词： Veins Transforms Decision support systems Image recognition Image color analysis Instruments Visualization

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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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Face recognition from near-infrared images with convolutional neural network

Face recognition from near-infrared images with convolutiona...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Xiaotong Zhang Min Peng Tong Chen Chonqging Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University China

ISBN: (纸本)9781509028610

Owing to the vigorous development of face recognition, near-infrared (NIR) face recognition technology with light insensitivity has attracted increasing attention. However, the traditional methods for NIR face recognition feature the hand-crafted feature design. In this paper, we present a convolutional neural network (CNN) for NIR face recognition. CNN is a multiplayer feed-forward neural network which can automatically learn the features from the raw images and provide partial invariance to illumination, scale and deformation. Experimental results on PolyU-NIRFD database show that our proposed CNN architecture has higher recognition rate compared with the traditional recognition methods, such as Gabor-directional binary code (GDBC), Zernike moments and Hermite kernels (ZMHK).

关键词： Face recognition Feature extraction Neural networks Lighting Face Convolutional codes Databases

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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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A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification

A Fusion Shallow and Deep Features Network for Facial StO2 S...

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IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Haolin Xia Sirui Sun Xinyu Liu Ju Zhou Hanpu Wang Tong Chen School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data.

关键词： Dimensionality reduction Automation Anxiety disorders Feature extraction Physiology Stress

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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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