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4th International Conference on Signal processing and Computer Science, SPCS 2023

作者： Yue, Yuanhao Zhao, Xingcong Zhou, Wei Liu, Guangyuan School of Electronic and Information Engineering Southwest University Tiansheng Road Beibei Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Tiansheng Road Beibei Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Tiansheng Road Beibei Chongqing400715 China

ISBN: (纸本)9781510672505

In this study, we recorded the electroencephalography (EEG) of human subjects when they watched a movie trailer. Based on the willingness preference from the subjects after finished the movie trailer watching, the EEG signals were analyzed to build regression models to predict the subjects' movie preferences. We proposed a new approach to rise the prediction accuracy. Specifically, the correlated component analysis (CCA) algorithm was used to calculate the inter-subject correlation (ISC) of each time segment in the movie trailer and the ISC of each channel in the EEG. These two states were selected to locate the moment when a specific area of the brain responds most strongly to the video stimulation, as an effective indicator for predicting individual movie preferences. The experimental results show that the method of selecting effective EEG signals at the level of ISC has achieved a better prediction effect on the GBDT regression model (R2 = 0.733, RMSE = 0.681). We also found that low-frequency EEG signals in the θ and δ bands of the parietal and occipital sites are more effective in predicting movie preferences than high-frequency EEG signals. © 2023 SPIE.

关键词： Electroencephalography

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Remote Detection and Classification of Human Stress Using a Depth Sensing Technique

Remote Detection and Classification of Human Stress Using a ...

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Asian Conference on Affective Computing and intelligent Interaction (ACII Asia)

作者： Yuhao Shan Tong Chen Liansheng Yao Zhan Wu Wanhui Wen Guangyuan Liu Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781538653128

Stress plays an important role in our daily life. Long-term's psychological stress will lead to serious health as well as social problems, it is important to detect and monitor the psychological stress in its early stage. Most existing stress detection equipment are contact-type, such as wrist strap. However, in a real application, such as a working environment, a contact-free stress detection system will bring greater convenience. In this paper, we proposed a novel framework for detecting and classifying human stress based on respiratory signals measured remotely by using a Kinect sensor with a detection range of 3 meters. We test the framework on respiratory signals data set from 20 individuals under 3 different tasks (listen relax music, do exercise and do Stroop Color-word test), corresponding to relaxation, physical stress and psychological stress state. Experimental results suggest that the proposed method is a promising way for monitoring human stress and even discriminating psychological stress from the physical stress.

关键词： human computer interaction medical signal processing patient monitoring psychology medical signal processing computer human interaction Psychological Stress Patient Monitoring Psychology Stress (mechanics) physical stress strapping of wrist Stressing telemetry Sensing Techniques Social Problems

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A decentralized Nesterov gradient method for stochastic optimization over unbalanced directed networks

A decentralized Nesterov gradient method for stochastic opti...

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作者： Hu, Jinhui Xia, Dawen Cheng, Huqiang Feng, Liping Ji, Lianghao Guo, Jing Li, Huaqing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China College of Data Science and Information Engineering Guizhou Minzu University Guiyang China Department of Computer Science Xinzhou Teachers University Shanxi China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

Decentralized stochastic gradient methods play significant roles in large-scale optimization that finds many practical applications in machine learning and coordinated control. This paper studies optimization problems over unbalanced directed networks, where the mutual goal of agents in the network is to optimize a global objective function expressed as a sum of local objective functions. Each agent using only local computation and communication in the networks is assumed to get access to a stochastic first-order oracle. In order to devise a noise-tolerant decentralized algorithm with accelerated linear convergence, a decentralized Nesterov gradient algorithm with the constant step-size and parameter using stochastic gradients is proposed in this paper. The proposed algorithm employing a gradient-tracking technique is proved to converge linearly to an error ball around the optimal solution via the analysis on a linear system when the positive constant step-size and parameter are sufficiently small. We further recover the exact linear convergence for the proposed algorithm with exact gradients under the same selection conditions of the constant step-size and parameter. Some real-world data sets are used in simulations to validate the correctness of the theoretical findings and practicability of the proposed algorithm. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Multi agent systems

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A distributed stochastic optimization algorithm with gradient-tracking and distributed heavy-ball acceleration

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Frontiers of Information Technology & Electronic Engineering 2021年第11期22卷 1463-1476页

作者： Bihao SUN Jinhui HU Dawen XIA Huaqing LI Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing400715China College of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang550025China

Distributed optimization has been well developed in recent years due to its wide applications in machine learning and signal *** this paper,we focus on investigating distributed optimization to minimize a global *** objective is a sum of smooth and strongly convex local cost functions which are distributed over an undirected network of n *** contrast to existing works,we apply a distributed heavy-ball term to improve the convergence performance of the proposed *** accelerate the convergence of existing distributed stochastic first-order gradient methods,a momentum term is combined with a gradient-tracking *** is shown that the proposed algorithm has better acceleration ability than GT-SAGA without increasing the *** experiments on real-world datasets verify the effectiveness and correctness of the proposed algorithm.

关键词： Distributed optimization High-performance algorithm Multi-agent system Machine-learning problem Stochastic gradient

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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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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 Novel Privacy-Preserving Data Integrity Verification by Partial Delegation

A Novel Privacy-Preserving Data Integrity Verification by Pa...

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International Conference on Information Science and Technology (ICIST)

作者： Runze Ji Nankun Mu Xiaofeng Liao The Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing P.R.China

Recently, cloud storage is widely concerned due to the properties of low cost, advanced technology and efficient business model, and has been made a rapid development. However, as a new storage model, there are known security and privacy issues in migrating data to the cloud. Therefore, how to ensure the integrity and correctness of data storage is the main research direction and development of cloud storage technology. Moreover, the main purpose of the data integrity verification mechanism is to verify that the data is completely stored in the cloud server and is not modified maliciously or deleted. Sometimes the user can't perform data integrity verification for some reason, and the verification right need to be delegate to a third party auditor. In this paper, we proposed: (1) a new data integrity verification by partial delegation(PDIPD) is proposed, which delegates the remote data possession checking to a third party auditor. Compared with the existing data integrity verification scheme, the new scheme has less computational overhead and the flexibility at the same security level. (2) Extensive security analyses show this scheme is provable secure in random oracle. Estimating the cost of basic cryptographic and analyzing the experiment show, the proposed schemes are highly efficient and practical.

关键词： Data integrity Cloud computing Servers Data models Games Public key

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Effects of Acute Moderate Intensity Exercise on Emotion Based on beta Power in EEG 2019

Effects of Acute Moderate Intensity Exercise on Emotion Base...

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Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Pengfei Gao Zhengji Long Zhangyan Xiao Fu Yang Jihai Zhang Guangyuan Liu School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Processing Chongqing China

ISBN: (纸本)9781450376273

According to previous studies, high anxiety leads to an increase in beta power, while pleasure and relaxation of emotion reduces beta power. In this experiment, the International Standard Picture Stimulation Library(IAPS) was used to induce positive and negative emotions of subjects, and EEG signals were collected when subjects were looking at pictures. Comparing the beta power of positive and negative EEG induced by before and after exercise, we found that the power of beta stimulated by positive and negative pictures before exercise is generally greater than that after exercise. It indicates that subjects were happier when facing positive stimuli than before exercise, and less negative emotions were produced when facing negative stimuli than before exercise. In short, acute moderate intensity exercise can improve people's negative emotion in the face of negative stimuli. The purpose of this study is to explore the mechanism of the effect of acute moderate intensity exercise on emotion by analyzing beta power in late positive potential (LPP).

关键词： moderate emotion exercise beta power

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Application of convolutional neural network for facial expression recognition 6

Application of convolutional neural network for facial expre...

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6th International Conference on Robotics and Artificial Intelligence, ICRAI 2020

作者： Clinton Atabansi, Chukwuemeka School of Electronic and Information Engineering Southwest University China No.2 Tiansheng Rd. Beibei District Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University China No.2 Tiansheng Rd. Beibei District Chongqing400715 China

ISBN: (纸本)9781450388597

The recognition of people's expression has been a very difficult task for computers from the time of its invention and still continues to pose a lot of challenges to the modern day generation of computers. To solve this problem, Convolutional Neural Network (CNN) is used which involves the application of preprocessing, feature extraction, training technique, and testing modules/methods to determine facial expression recognition. These methods were tested on the Oulu-CASIA VIS dataset [1]. The results obtained classified images of people's facial expressions into six (6) distinct emotional classes, viz (anger, disgust, fear, happiness, sadness and surprise) showing an average accuracy of 98.99% and thus affirming that the application of the convolutional neural network (CNN) in facial expression recognition is efficient. © 2020 ACM.

关键词： Face recognition

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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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