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11th International Conference on information Science and Technology, ICIST 2021

作者： Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai Che, Hangjun Open University of Hong Kong School of Science and Technology Hong Kong Hong Kong Southwest University College of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (纸本)9781665412667

As one of the passive investment strategies, index-tracking aims to replicate market indexes to reproduce market performance. It is widely used for long-term investment. Full replication and partial index-tracking are common approaches for index-tracking problems. Although full replication tracks the chosen market index perfectly, the transaction cost is relatively high in practice. Therefore, partial index-tracking is desired that can reduce the transaction cost and avoid illiquid assets. The partial index-tracking approach selects the subset of a benchmark index and applies restrictions for the numbers of stocks with cardinality constraints. The constrained problem is converted into an unconstrained problem by adding the penalty term. This paper is concerned with the sparse index-tracking problem with cardinality constraints by various metaheuristics. Various metaheuristics are used to deal with the sparse index-tracking problem, and their performances are compared. Also, various penalty values are adopted to test the performance of the compared algorithm. © 2021 IEEE.

关键词： Constrained optimization

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Fat-tree-based optical networks-on-chip with WDM: Crosstalk noise exploiting

Fat-tree-based optical networks-on-chip with WDM: Crosstalk ...

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作者： Zhang, Jingping Xie, Yiyuan Wang, Shujian Ye, Yichen Liu, Yuzhu Pan, Gaofeng Liu, Yong Zhang, Jiliang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing National Joint Engineering Laboratory of Intelligent Transmission and Control Technology College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Optoelectronic Information University of Electronic Science and Technology of Chengdu Chengdu611731 China

Wavelength division multiplexing (WDM) technology as an extension of optical networks-on-chip (ONoCs) performance, can be applied to further overcome shortages of low bandwidth and high-power consumption in multiprocessor systems-on-chip. Nevertheless, WDM-based ONoCs (ONoCs_W) can encounter performance degradation and network scalability constraints because of its power loss and intrinsic crosstalk noise, especially the non-linear four-wave mixing crosstalk noise. Here, a formal systematical exploiting for worst-case crosstalk noise and optical signal-to-noise ratio (OSNR) in arbitrary fat-tree-based ONoCs with WDM (FONoCs_W) is proposed. Meanwhile, the worst-case OSNR optical link candidates are identified. To verify the proposed analysis method, FONoCs_W employing an optical turnaround router with WDM (WOTAR) is used for case study. The study results demonstrate that crosstalk noise significantly diminishes OSNR and restricts scalability of FONoCs_W. For instance, when input optical signal power equals 0 dBm in worst case, the average OSNR is −9.47 dB, the mean power of crosstalk noise for eight wavelengths is slightly greater than that of signal when the number of processor cores is 64;when it equals to 128, the average OSNR is −34.37 dB, decreased by 24.90 dB compared with the network with 64 processor cores. © The Institution of Engineering and Technology 2018

关键词： Network-on-chip

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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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Offset or Onset Frame: A Multi-Stream Convolutional Neural Network with CapsuleNet Module for Micro-expression Recognition

Offset or Onset Frame: A Multi-Stream Convolutional Neural N...

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International Conference on intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Nian Liu Xinyu Liu Zhihao Zhang Xueming Xu Tong Chen chool of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Micro-expression is a spontaneous facial expression, which may reveal people's real emotions. The micro-expression recognition has recently attracted much attention in psychology and computer vision community. In this paper, we designed a multi-stream Convolutional Neural Network (CNN) combined with the Capsule Network(CapsNet) *** CNNCapsNet, to improve the performance of micro-expression recognition. Firstly, both vertical and horizontal optical flow are computed from the onset to the apex, and from the apex to the offset frame respectively, which is the first time that the offset frame information has been taken into account in the field of micro-expression recognition. Secondly, these four optical flow images and the grayscale image of apex frame are input into the five-stream CNN model to extract features. Finally, CapsNet completes micro-expression recognition by learning the features extracted by CNN. The method proposed in this paper are evaluated using the Leave-One-Subject-Out (LOSO) cross-validation protocol on CASME II. The results show that the offset information, which is often neglected, is more important than onset information for the recognition task. Our CNNCapsNet framework can achieve the accuracy of 64.63% for the five-class micro-expression classification.

关键词： Computer vision Protocols Psychology Feature extraction Convolutional neural networks Task analysis Optical flow

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Observer-Based Consensus of nonlinear Multi-Agent Systems With Input Delay via Delay-Dependent Event-Triggered Control

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 157-166页

作者： Wei Zhang Bingyan Gong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronics and Information Engineering Southwest University Chongqing China

The distributed control for leader-following consensus problem of nonlinear multi-agent systems with input delay is investigated in this paper. Based on observer state information, an improved distributed truncated prediction control strategy is proposed in order to address the issue of input delay. Furthermore, a delay-dependent event-triggered control protocol is designed to alleviate the use of computation resources and communication bandwidth. Unlike the existing event-triggered protocol, the protocol in the article is delay-dependent and specially designed for systems with input delay. Moreover, the Zeno behavior does not occur. It should be pointed out that, our observer-based control is significant for practical applications where the state of agents cannot be directly obtained. Utilizing the Lyapunov-Krasoviskii function theory, the sufficient conditions of the consensus under above two control methods are obtained. Lastly, we illustrate the feasibility of our results by illustrative examples.

关键词： Delays Protocols Observers Multi-agent systems linear matrix inequalities Sufficient conditions Cyber-physical 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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A simple motion deblurring method by two continuous images

A simple motion deblurring method by two continuous images

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

作者： Jihai Zhang Jianfeng Li Guangyuan Liu Tong Chen Tengteng Zhu Bo Li The Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

Restoring blurred images to clear images is a challenging problem. Most previous methods only analyzed the single image. However, for motion blurring, this method missed the key trajectory description process. Based on the essence of motion blur, our research combined the continuous frame image information in video to estimate the motion track, the SIFT algorithm was proposed for the first time to match the feature points of the target in the continuous frame image. The accurate motion blur angle and motion blur distance were obtained by combining the matching information to obtain an accurate PSF. Finally, the Wiener filtering algorithm combined with PSF was used for deblurring. Experiments showed that the proposed method can improve the image sharpness after restoration compared with the previous algorithms.

关键词： motion blur continuous frame image deblurring PSF SIFT motion blur deblurring Point spread function Sifting SHARPNESS OF IMAGE images Continuous structures continuous image

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Movie preference prediction based on EEG signal under high brain correlation 4

Movie preference prediction based on EEG signal under high b...

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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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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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An Image Denoising Method Based on Deep Residual GAN 4

An Image Denoising Method Based on Deep Residual GAN

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2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Wang, Ziyuan Wang, Lidan Duan, Shukai Li, Yunfei Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Schoolof Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing400715 China Braininspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects. © Published under licence by IOP Publishing Ltd.

关键词： Image denoising

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