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arXiv 2019年

作者： Li, Huaqing Zheng, Lifeng Wang, Zheng Yan, Yu Feng, Liping Guo, Jing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Department of Computer Science Xinzhou Teachers University Shanxi Xinzhou034000 China

In this paper, we study convex optimization problems where agents of a network cooperatively minimize the global objective function which consists of multiple local objective functions. The intention of this work is to solve large-scale optimization problems where the local objective function is complicated and numerous. Different from most of the existing works, the local objective function of each agent is presented as the average of finite instantaneous functions. Integrating the gradient tracking algorithm with stochastic averaging gradient technology, a distributed stochastic gradient tracking (termed as S-DIGing) algorithm is proposed. At each time instant, only one randomly selected gradient of an instantaneous function is computed and applied to approximate the local batch gradient for each agent. Based on a novel primal-dual interpretation of the S-DIGing algorithm, it is shown that the S-DIGing algorithm linearly converges to the global optimal solution when step-size do not exceed an explicit upper bound and the instantaneous functions are strongly convex with Lipschitz continuous gradients. Numerical experiments are presented to demonstrate the practicability of the S-DIGing algorithm and correctness of the theoretical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Consensus algorithm

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Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition

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Journal of Physics: Conference Series 2021年第1期1873卷

作者： Chukwuemeka C Atabansi Tong Chen Ranlei Cao Xueming Xu School of Electronic and Information Engineering Southwest University Beibei District Chongqing 400715 China Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University Beibei District Chongqing 400715 China

In this paper, we investigate a deep learning vgg-16 network architecture for facial expression recognition under active near-infrared illumination condition and background. In particular, we consider the concept of transfer learning whereby features learned from high resolution images of huge datasets can be used to train a model of relatively small dataset without loosing the generalization ability. The pre-trained vgg-16 network architecture with transfer learning technique has been trained and validated on the Oulu-CASIA NIR dataset comprising of six (6) distinct facial expressions, and average test accuracy of 98.11% was achieved. The validation on our test data using the confusion, the precision, and the recall matrix reveals that our method achieves better results in comparison with the other methods in the literature.

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Reversible Data Hiding Based on Improved Rhombus Prediction Method

Reversible Data Hiding Based on Improved Rhombus Prediction ...

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International Conference on intelligent Control and information processing (ICICIP)

作者： Simi Lu Xiaofeng Liao Nankun Mu Jiahui Wu Junqing Le Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Electronic and Information Engineering Southwest University Chongqing P.R. China Computer Science Chongqing University Chongqing P. R. China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Reversible data hiding(RDH) is a research hotspot in the field of information hiding. Among them, RDH based on histogram shift(HS) is a high performance algorithm. Accurate pixel prediction can reduce image distortion while maintaining high capacity. Therefore, this paper proposes an RDH algorithm based on the improved rhombus prediction method. Experiments show that the improved rhombus prediction method can predict pixels more accurately, and the generated prediction error histogram is more compact and clear. The proposed RDH algorithm has a higher embedding capacity and a lower distortion rate.

关键词： intelligent control information processing Airplanes

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An extragradient algorithm for fixed point problems and pseudomonotone equilibrium problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第4期82卷 89-96页

作者： Yao, Zhangsong Liou, Yeong-Cheng Zhu, Li-Jun School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China Department of Healthcare Administration and Medical Informatics Research Center of Nonlinear Analysis and Optimization Kaohsiung Medical University Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province and Health Big Data Research Institute Advanced Intelligent Perception and Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

Iterative methods for solving fixed point problems and pseudomonotone equilibrium problems have been considered. An extragradient algorithm has been presnted for finding a common element of the fixed points of pseudocontractive operators and the solutions of pseudomonotone equilibrium problems in Hilbert spaces. Weak convergence result of the suggested algorithm is proved. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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Effects of Acute Moderate Intensity Exercise on the EEGFrontal Alpha Asymmetry

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Journal of Physics: Conference Series 2020年第7期1486卷

作者： Zhangyan Xiao Zhengji Long Pengfei Gao Chaoling Qin Guangyuan Liu College of Electronic Information and Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China College of Physical Education Southwest University Chongqing China

Exercise can improve physical and psychological states efficiently. Previous studies have shown that the frontal alpha asymmetry can be used to express emotional states, while relative activity in the larger left frontal can predict positive effects. In this study, our purpose is to observe the effects of pedalling exercise (PE) on the frontal alpha asymmetry during negative stimulation. The electroence phalograph (EEG)signals obtained are processed by matlab to obtain the frontal asymmetry value. By observing and comparing the frontal alpha asymmetry between two groups, we can find that the frontal alpha asymmetry of the sports group increased significantly, while the control group have no significantly change. The study shows that the acute moderate intensity PE can improve the resistance to negative stimuli and increase positive emotions.

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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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Gait Phase Classification Based on sEMG Signals Using Long Short-Term Memory for Lower Limb Exoskeleton Robot

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IOP Conference Series: Materials Science and Engineering 2020年第1期853卷

作者： Ye Yuan Ziming Guo Can Wang Shengcai Duan Lufeng Zhang Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University.

In this work, we present a Long Short-Term Memory Model (LSTMM) for gait phase classification based on sEMG signals to control the lower limb exoskeleton robot which can recognize 2 phases (Stand and Swing) of leg phases between the foot and ground. This model only needs four sEMG signals to control the lower limb exoskeleton robot helping the hemiplegia patient walking. Compared with the existing methods, the proposed model not only avoids the complex sensor systems but also enhances the accuracy of gait phase classification. The experimental results first verify the availability of sEMG data acquisition system on the lower limb exoskeleton robot made by the Shenzhen Institutes of Advanced Technologies (SIAT) by quantify the system with gold standard optoelectronic system Vicon, then show that the proposed LSTMM is significantly higher on prediction accuracy and has better robustness for gait phase classification to control the lower limb exoskeleton robot with different speeds. Finally, the maximum accuracy of LSTMM on the gait phase classification is 97.89%.

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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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Kernel Least Mean Square with Tracking 36

Kernel Least Mean Square with Tracking

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第36届中国控制会议

作者： Wanli Wang Shiyuan Wang Guobing Qian Bo Yang College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

ISBN: (纸本)9781538629185

Based on the Gaussian process(GP) regression,the relationship between the input-output pair can be modeled by the sum of the Gaussian noise and an unknown latent function evaluated on an input(LFEI).The acquisition of LFEI is thus essentially *** this paper,a novel algorithm called kernel least mean square with tracking(KLMS-T) is proposed to estimate *** on the existing kernel least mean square(KLMS) that is a simple and efficient kernel adaptive filtering algorithm,the proposed KLMS-T utilizes the framework of Bayesian inference to estimate LFEI from the function-space *** show that compared with LMS and KLMS,KLMS-T achieves better filtering performance in terms of the convergence rate and the steady state mean square error.

关键词： Gaussian process latent function kernel adaptive filter mean square error

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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