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Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 600-605页

作者： LIU Min DENG Bin TANG Ying WU Minghu WANG Juan Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System.Hubei University of Technology Wuhan 430068.China Department of Electrical and Computer Engineering.Rowan University GlassboroNJ 08028USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Smart Education Qingdao Academy of Intelligent IndustriesQingdao 266041ShandongChina

The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring ***,there is still great challenge because of the limited wireless network *** resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image ***,an adaptively learning background model is improved to discover successive similar image frames from the inputting video ***,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element ***,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or *** evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to *** to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

关键词： wireless visual sensor network(WVSN) temporal redundancy foreground background dynamic texture

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Reconfigurable intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

引用

arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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A Parallel Education Based intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Jing, Sifeng Tang, Ying Liu, Xiwei Gong, Xiaoyan Cui, Wei Liang, Joleen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed. © 2020 IEEE.

关键词： computer aided instruction

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

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Output containment control of discrete-time multi-agent systems with application to multiple ground vehicles

arXiv

引用

arXiv 2020年

作者： Wang, Shimin Jiang, Simin Zhan, Zhi Wu, Yuanqing Zhong, Renxin Yat-Sen, Sun Department of Electrical and Computer Engineering University of Alberta AB Canada Guangdong Key Laboratory of Intelligent Transportation Systems School of Intelligent Systems Engineering University Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

This paper investigates the output containment control problem for heterogeneous discrete-time multi-agent systems with multiple leaders. The objective is to develop a distributed control law such that the output of the followers can converge to the convex hull spanned by the output of the leaders exponentially. The convex hull formed by the output of the leaders and the system matrix of leaders are estimated via an adaptive distributed containment observer. Based on this observer, a distributed dynamic output feedback control protocol is first devised for heterogeneous discrete-time multi-agent systems using only neighboring relative output information. The proof is depicted by showing certain output containment errors converge to zero exponentially, which indicates the containment control objective is well achieved. A distributed dynamic state-feedback control law is deduced as a special case of the output feedback control. For the application to control multiple ground vehicles, a prescribed offset for maintaining the minimum safety gap between adjacent agents is considered. Finally, numerical simulations with application to ground vehicles validate the effectiveness and the computational feasibility of the proposed control protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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A Domain-Agnostic Approach for Characterization of Lifelong Learning systems

arXiv

引用

arXiv 2023年

作者： Baker, Megan M. New, Alexander Aguilar-Simon, Mario Al-Halah, Ziad Arnold, Sébastien M.R. Ben-Iwhiwhu, Ese Brna, Andrew P. Brooks, Ethan Brown, Ryan C. Daniels, Zachary Daram, Anurag Delattre, Fabien Dellana, Ryan Eaton, Eric Fu, Haotian Grauman, Kristen Hostetler, Jesse Iqbal, Shariq Kent, Cassandra Ketz, Nicholas Kolouri, Soheil Konidaris, George Kudithipudi, Dhireesha Learned-Miller, Erik Lee, Seungwon Littman, Michael L. Madireddy, Sandeep Mendez, Jorge A. Nguyen, Eric Q. Piatko, Christine Pilly, Praveen K. Raghavan, Aswin Rahman, Abrar Ramakrishnan, Santhosh Kumar Ratzlaff, Neale Soltoggio, Andrea Stone, Peter Sur, Indranil Tang, Zhipeng Tiwari, Saket Vedder, Kyle Wang, Felix Xu, Zifan Yanguas-Gil, Angel Yedidsion, Harel Yu, Shangqun Vallabha, Gautam K. Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd. LaurelMD20723 United States Teledyne Scientific Company Intelligent Systems Laboratory 19 T.W. Alexander Drive NC27709 United States Department of Computer Science University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States Department of Computer Science Loughborough University Loughborough United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States SRI International 201 Washington Rd PrincetonNJ United States University of Texas at San Antonio San AntonioTX United States Department of Computer Science University of Massachusetts Amherst AmherstMA United States Sandia National Laboratories AlbuquerqueNM United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Computer Science Brown University ProvidenceRI United States Information and Systems Sciences Laboratory HRL Laboratories 3011 Malibu Canyon Road MalibuCA90265 United States Department of Computer Science Vanderbilt University NashvilleTN United States Argonne National Laboratory 9700 S Cass Ave LemontIL United States

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

arXiv

引用

arXiv 2022年

作者： Boucharas, Dimitrios G. Androutsos, Christos Gkois, George Tsakanikas, Vassilis D. Pezoulas, Vasileios C. Manousos, Dimitrios Skaramagkas, Vasileios Chatzaki, Chariklia Kontogiannis, Stathis Spandonidis, Christos Pantazis, Alexandros K. Tachos, Nikolaos S. Tsiknakis, Manolis Fotiadis, Dimitrios I. The Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina IoanninaGR-45110 Greece Crete HeraklionGR-70013 Greece Biomedical Informatics and eHealth Laboratory Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Crete HeraklionGR-71004 Greece The Department of Electric and Computer Engineering Hellenic Mediterranean University Crete HeraklionGR-71004 Greece PD Neurotechnology Ltd IoanninaGR-45110 Greece Prisma Electronics SA P.Faliro AthensGR-17564 Greece Crete HeraklionGR-70013 Greece Biomedical Research Institute FORTH IoanninaGR-45110 Greece The Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR-45110 Greece

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson’s disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication protocols enables the straightforward replacement of one of the core components with a relative one (e.g., a different model of insoles), transparently from the end user, without affecting the overall architecture, resulting in a framework with the flexibility to adjust its modularity. © 2022, CC BY.

关键词： computer architecture

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A Multi-objective Computation Offloading Method in Multi-cloudlet Environment 1

引用

9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Peng, Kai Zhu, Shuaiqi Zheng, Lixin Xu, Xiaolong Leung, Victor C. M. College of Engineering Huaqiao University Quanzhou China Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems Quanzhou China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

ISBN: (数字)9783030485139

ISBN: (纸本)9783030485122

Computation offloading is becoming a promising technology that can improve quality of service for mobile users in mobile edge computing. However, it becomes much difficult when there are multi-cloudlet near to the mobile users. The resources of the cloudlet are heterogeneous and finite, and thus it is challenge to choose the best cloudlet for the multi-user. The issue for multi-user in multi-cloudlet environment is well-investigated in this study. Firstly, we establish a multi-objective optimization model with respect to time consumption and energy consumption of mobile devices. Moreover, we devise a multi-objective computation offloading method based on improved fast and elitist genetic algorithm for selecting the optimal offloading strategies. Finally, compared with other methods, numerous experiments proved that our proposed method have advantages in effectiveness and efficiency. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy utilization

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Impasse Surface of Differential-Algebraic Power System Models: An Interpretation Based on Admittance Matrices

arXiv

引用

arXiv 2020年

作者： Song, Yue Hill, David J. Liu, Tao Zhang, Xinran The State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia The School of Automation Science and Electrical Engineering Beihang University Beijing China

The impasse surface is an important concept in the differential-algebraic equation (DAE) model of power systems, which is associated with short-term voltage collapse. This paper establishes a necessary condition for a system trajectory hitting the impasse surface. The condition is in terms of admittance matrices regarding the power network, generators and loads, which specifies the pattern of interaction between those system components that can induce voltage collapse. It applies to generic DAE models featuring high-order synchronous generators, static loads, induction motor loads and lossy power networks. We also identify a class of static load parameters that prevent power systems from hitting the impasse surface;this proves a conjecture made by Hiskens that has been unsolved for decades. Moreover, the obtained results lead to an early indicator of voltage collapse and a novel viewpoint that inductive compensation to the power network has a positive effect on preventing short-term voltage collapse, which are verified via numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Differential equations

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