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Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment

Informatics in Medicine Unlocked 2020年 18卷

作者： Mirbagheri, Mahya Hakimi, Naser Ebrahimzadeh, Elias Setarehdan, S. Kamaledin Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Seaman Family MR Research Centre University of Calgary Calgary Alberta Canada Department of Clinical Neurosciences University of Calgary Calgary Alberta Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary Alberta Canada

Background: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique for studying brain hemodynamics. Since brain hemodynamics also involves components from the heart rate (HR), it is possible to extract the HR signal from the fNIRS (EHR). By removing global systemic changes from the EHR, we hypothesized that the EHR includes physiological information beyond HR, which is the functional response of the brain. Method: To evaluate this hypothesis, the mental states of 10 healthy subjects were assessed under a stressful condition induced using the Montreal Imaging Stress Task (MIST). EHR and Reference HR (RHR) signals were then extracted from fNIRS and ECG, respectively. Next, a method based on Independent Component Analysis (ICA) was applied in order to discriminate the independent sources in the EHR signal. Thereafter, the least correlated components with RHR were reconstructed, which is the so-called target signal of EHR¯. Results: Similarity measurement of EHR¯ with fNIRS signals revealed a significant correlation between them. To show the effectiveness of the existing information in EHR¯ signals, they were employed for mental stress assessment by using the SVM classifier, leading to an accuracy of 97.3 ± 0.9%, which was 13%, 30.8% and 1.8% greater than those of the fNIRS, RHR, and EHR signals, respectively. Conclusions: We showed not only that the EHR¯ signal is a brain-related response, but also that it contains useful information for mental stress level measurement. Moreover, it is concluded that the EHR signal includes information from both brain and heart, which is hence useful for applications in which brain and heart functions are altered. © 2019

关键词： Brain hemodynamic response Functional near infrared spectroscopy Heart rate Independent component analysis Stress assessment

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Performance–guaranteed consensus control inspired by the mammalian limbic system for a class of nonlinear multi-agents ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9496-9501页

作者： Ignacio Rubio Scola Luis Rodolfo Garcia Carrillo João P. Hespanha Rogelio Lozano CIFASIS (CONICET-UNR) and Department of Mathematics National University of Rosario Rosario 2000 Argentina. Unmanned Systems Laboratory. School of Engineering and Computer Sciences Texas A&M University - Corpus Christi TexasUSA. 78412-5797. Center for Control Dynamical Systems and ComputationUniversity of California Santa Barbara CA 93106 USA. Heuristique et Diagnostic des Systemes Complexes Universit é de Technologie de Compiègne UMR CNRS 7253 60200 CompiegneFrance

computational models of emotional learning observed in the mammalian brain have inspired diverse self-learning control approaches. These architectures are promising in terms of their fast learning ability and low computational cost. In this paper, the objective is to establish performance–guaranteed emotional learning–inspired control (ELIC) strategies for autonomous multi–agent systems (MAS), where each agent incorporates an ELIC structure to support the consensus controller. The objective of each ELIC structure is to identify and compensate model differences between the theoretical assumptions taken into account when tuning the consensus protocol, and the real conditions encountered in the real system to be stabilized. Stability of the closed-loop MAS is demonstrated using a Lyapunov analysis. Simulation results based on the consensus task of a group of inverted pendulums demonstrate the effectiveness of the proposed ELIC for stabilization of nonlinear MAS.

关键词： Multi-agents systems Biologically-inspired control Robust control Distributed control Nonlinear control

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Publisher’s Note: “Evidences of thermoelectrically driven unidirectional magnetoresistance from a single Weyl ferromagnet Co2MnGa” [APL Mater. 11, 121109 (2023)]

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APL Materials 2024年第1期12卷

作者： Bin Rong Lizhu Ren Yizhe Liu Bo Sun Jiaxin Chen Kie Leong Teo Liang Liu Yumeng Yang b) School of Information Science and Technology ShanghaiTech University Shanghai 201210 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen 518055 China Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education) Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Hefei National Laboratory Hefei 230088 China Shanghai Research Center for Quantum Sciences 99 Xiupu Road Shanghai 201315 China

This article was published online on December 15, 2023, with errors in the author and affiliation lists; several authors were linked to incorrect affiliations.

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Finite-sample analysis of decentralized temporal-difference learning with linear function Approximation

arXiv

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arXiv 2019年

作者： Sun, Jun Wang, Gang Giannakis, Georgios B. Yang, Qinmin Yang, Zaiyue College of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an ‘additional’ projection step to control the ‘gradient’ bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type—in the sense that they hold under the most practical assumptions —which is made possible by means of a novel multi-step Lyapunov analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Sampling

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Robust on-line ADP-based solution of a class of hierarchical nonlinear differential game

arXiv

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arXiv 2019年

作者： Satouri, Mohammad Reza Kebriaei, Hamed Razminia, Abolhassan Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran P.O. Box 4395-515 Tehran Iran Research Lab. Department of Electrical Engineering School of Engineering Persian Gulf University P.O. Box 75169 Boushehr Iran School of Electrical and Computer Engineering College of Engineering University of Tehran P.O. Box 4395-515 Tehran Iran Control & Intelligent Processing Center of Excellence

In this paper a hierarchical one-leader-multi-followers game for a class of continuous-time nonlinear systems with disturbance is investigated by a novel policy iteration reinforcement learning technique in which, the game model consists both of the zero-sum and nonzero-sum games, simultaneously. An adaptive dynamic programming (ADP) method is developed to achieve optimal control strategy under worst case of disturbance. This algorithm reduces the number of neural networks which are used for estimation for about thirty percent. Proposed algorithm uses neural networks to estimate value functions, control policies and disturbances. Convergence analysis of the estimations is investigated using Lyapunov theory and exploiting properties of the Nemytskii operator. Finally the simulation results will show effectiveness of developed ADP *** Codes 91A65, 49N70, 68T05, Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear systems

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Statistical Approach to Detection of Attacks for Stochastic Cyber-Physical Systems

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IFAC-PapersOnLine 2018年第25期51卷 178-183页

作者： Marelli, Damián Sui, Tianju Fu, Minyue School of Automation Guandong University of Technology China French-Argentinean International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina Department of Control Science and Engineering Dalian University of Technology China School of Electrical Engineering and Computer Science University of Newcastle Australia

We study the problem of detecting an attack on a stochastic cyber-physical system. We start by proposing a detection criterion based on checking the statistics of the Kalman prediction error. To show the importance of the proposed criterion, we study certain attacking schemes which can be effectively detected by this criterion, but not by checking simpler conditions based on commonly used statistics. We then use the proposed criterion to derive a detection algorithm. This algorithm is based on a statistical confidence test, giving a confidence level for detecting an attack. We present simulation results to illustrate the performance of this algorithm. © 2018

关键词： computer crime Cyber Physical System Embedded systems Error statistics Linear systems Statistics Stochastic systems Testing Attack detection Detection algorithm Detection of attacks Integrity attacks Statistical approach Statistical confidence Statistical hypothesis testing System security

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Translational Model Identification and Robust control for the Parrot Mambo UAS Multicopter

Translational Model Identification and Robust Control for th...

引用

IEEE Globecom Workshops

作者： Ignacio Rubio Scola Gabriel Alexis Guijarro Reyes Luis Rodolfo Garcia Carrillo Joao Hespanha Junfei Xie FCEIA-UNR Rosario Argentina Texas A&M University Corpus Christi TX USA Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA Department of Electrical and Computer Engineering San Diego State University San Diego CA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Two main problems are addressed in this paper. The first one is the model identification for a commercial unmanned aircraft system (UAS): the Parrot Mambo multicopter. The second one aims at synthesizing a robust controller for guaranteeing the stability of the X and Y translational dynamics of this UAS. To accomplish these goals, we first collect input-output data from a set of real-time flight experiments. Next, by applying an extended least square (ELS) algorithm to the data, a group of dynamic models are identified. Due to uncertainties, the obtained models are similar in nature but exhibit parametrical variations. For this reason, from the set of identified models, a unique nominal (i.e., average) parameter-dependent linear model is built, which also takes into account the minimum and maximum values defining the model parametrical variations. Finally, a static linear controller is synthesized for the dynamics of interest, guaranteeing global stability for every model. The identification results and the performance of the closed- loop controller are validated in a set of numerical simulations, demonstrating the effectiveness of the proposed modeling and control approaches.

关键词： Mathematical model Robust control Numerical models computational modeling Real-time systems Uncertainty Estimation

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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

引用

Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

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The vulnerability of cyber-physical system under stealthy attacks

arXiv

引用

arXiv 2020年

作者： Sui, Tianju Mo, Yilin Marelli, Damián Sun, Ximing Fu, Minyue School of Control Science and Engineering Dalian University of Technology Dalian China Department of Automation Tsinghua University Beijing China School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia School of Automation Guandong University of Technology China

In this paper, we study the impact of stealthy attacks on the Cyber-Physical System (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp. strictly stealthy) if it produces bounded changes (resp. no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp. strictly vulnerable) if it can be destabilized by a stealthy attack (resp. strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results. Copyright © 2020, The Authors. All rights reserved.

关键词： Embedded systems

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