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Nonfragile Observer-Based Event-Triggered Fuzzy Tracking Control for Fast-Sampling Singularly Perturbed Systems with Dual-Layer Switching Mechanism and Cyber-Attacks

SSRN

引用

SSRN 2023年

作者： Guo, Fang Luo, Mengzhuo Cheng, Jun Katib, Iyad Shi, Kaibo College of Science Guilin University of Technology Guangxi Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Applied Statistics Guilin541004 China College of Mathematics and Statistics Guangxi Normal University Guangxi Guilin541006 China Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia School of Information Science and Engineering Chengdu University Sichuan Chengdu610106 China

This paper investigates the problem of nonfragile observer-based tracking control for a class of fuzzy fast-sampling singularly perturbed systems (SPSs) with sensor saturation, which subject to event-triggered scheme and random cyber-attacks. The switching topology of the fast-sampling SPSs is dominated by a dual-layer switching mechanism, in this case, the Markov process in the underlying system is nonhomogeneous and its time-varying transition probabilities (TPs) are governed by a persistent dwell-time switching signal. First, in order to avoid asynchronous phenomenon of premise variable between the fuzzy fast-sampling SPSs and the fuzzy controller under network environments, the non-parallel distributed compensation (non-PDC) technique is applied to improve the design flexibility and reduce the conservativeness of the results. Furthermore, a novel fuzzy tracking control design scheme is proposed, in which integrating the fuzzy nonfragile observer and reference model signal into the synthetic design of the tracking controller can be expected to reduce the tracking error. Second, by constructing a well-designed time-delay and mode-dependent Lyapunov function, some sufficient conditions are derived such that the closed-loop system is mean-square exponential stable and with extended dissipative tracking performance. Finally, the effectiveness and applicability of the proposed control protocol are illustrated by two simulation examples. © 2023, The Authors. All rights reserved.

关键词： Closed loop systems

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Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Liu, Ziheng Zhu, Yiyang Shi, Enyu Xu, Bokai Yuen, Chau Niyato, Dusit Debbah, Mérouane Jin, Shi Ai, Bo Shen, Xuemin School of Electronic and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing jiaotong University Beijing100044 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University 639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Low-Resolution Video Action Recognition Approach Based on Multi-Scale Reconstruction and Multi-Modal Fusion

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Zheng, Hui Zhao, Yesheng Zhang, Bo Shang, Guoqiang Al-Shamri, Mohammad Yahya H. Aldossary, Haya Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences Hefei Institute of Technology Innovation and Engineering Hefei230094 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Non Commissioned Officer School of Space Engineering University Beijing101416 China King Khalid University School of Computer Science Abha36311 Saudi Arabia Imam Abdulrahman Bin Faisal University Department of Computer Science Jubail31961 Saudi Arabia

The challenge of low-resolution video action recognition task lies in recovering and extracting feature representations that can effectively capture action characteristics with limited semantic information. In this paper, we propose an approach to address this challenge, which primarily comprises a multi-scale reconstruction module and a multi-modal fusion module. In multi-scale reconstruction module, we introduce a frequency-adaptive reconstruction model to reconstruct lost information from multiple scales. For crucial high-frequency sub-band images, we propose a wavelet-based super-resolution generative adversarial network to recover detailed information. In multi-modal fusion module, we propose a two-stream Transformer-based network to mine spatial-temporal joint feature representations from the reconstructed video. Additionally, we utilize another Transformer model to fuse features from different modalities, capturing both consistent and complementary representations. Finally, the fused features are fed into a classifier for recognition. Experimental results show that our proposed model outperforms other models for low-quality action recognition on HMDB51 (16×12 58.70%, 14×14 62.25%,80×60 68.94%), UCF101 (14×14 76.74%, 28×28 84.15%, 80×60 92.78%), and Tiny-VIRAT (35.63%) datasets. © 1975-2011 IEEE.

关键词： Image reconstruction

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Devils in the Clouds: An Evolutionary Study of Telnet Bot Loaders

arXiv

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arXiv 2022年

作者： Zhu, Yuhui Chen, Zhenxiang Yan, Qiben Wang, Shanshan Giaretta, Alberto Li, Enlong Peng, Lizhi Zhao, Chuan Conti, Mauro Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China School of Information Science and Engineering University of Jinan China Department of Computer Science and Engineering Michigan State University United States AASS MPI Lab Örebro University Sweden Department of Mathematics University of Padua Italy

One of the innovations brought by Mirai and its derived malware is the adoption of self-contained loaders for infecting IoT devices and recruiting them in botnets. Functionally decoupled from other botnet components and not embedded in the payload, loaders cannot be analysed using conventional approaches that rely on honeypots for capturing samples. Different approaches are necessary for studying the loaders evolution and defining a genealogy. To address the insufficient knowledge about loaders’ lineage in existing studies, in this paper, we propose a semantic-aware method to measure, categorize, and compare different loader servers, with the goal of highlighting their evolution, independent from the payload evolution. Leveraging behavior-based metrics, we cluster the discovered loaders and define eight families to determine the genealogy and draw a homology map. Our study shows that the source code of Mirai is evolving and spawning new botnets with new capabilities, both on the client side and the server side. In turn, shedding light on the infection loaders can help the cybersecurity community to improve detection and prevention tools. © 2022, CC BY.

关键词： Botnet

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Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 Competitions

IEEE Journal on Indoor and Seamless Positioning and Navigati...

引用

IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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Joint User Scheduling and computing Resource Allocation Optimization in Asynchronous Mobile Edge computing Networks

arXiv

引用

arXiv 2024年

作者： Cang, Yihan Chen, Ming Pan, Yijin Yang, Zhaohui Hu, Ye Sun, Haijian Chen, Mingzhe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The School of Electrical and Computer Engineering The University of Georgia AthensGA30602 United States The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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Hyperspectral Image Classification via Neighborhood Adaptive Graph Isomorphism Network

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Jian Tu, Bing Liu, Bo Li, Jun Plaza, Antonio Nanjing University of Information Science and Technology Institute of Optics and Electronics State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing 210044 China China University of Geosciences (Wuhan) Faculty of Computer Science Wuhan 430074 China University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica Cáceres 10003 Spain

Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with convolutional neural network (CNN), GCN can perform convolutions over irregular image regions and learn global dependencies among pixels in the whole image. Most existing GCN-based methods in the HSI community rely on average aggregation or weighted average aggregation strategies to aggregate neighboring node features. This process tends to obscure the differences between the nodes. However, for HSI classification tasks with obvious intra-class variability, average aggregation is a suboptimal choice. Moreover, the quality of the initial graph structure plays a crucial role in the model’s capacity to represent spectral relationships effectively. To mitigate these issues, we propose a neighborhood adaptive graph isomorphism network (NAGIN) for HSI classification to ensure that the diversified spectra representation of land-cover can be effectively captured. The neighborhood adaptive block (NAB) enhances spectral discriminability between land-cover classes via spectral reconstruction, enabling more precise removal of anomalous pixels in neighboring nodes. The graph isomorphism network (GIN) aggregates the features of neighboring nodes in an isomorphic manner to obtain multiple spectral expressions of the same type of land-cover, ensuring that the spectral features of different land-cover classes can be accurately distinguished. The Kolmogorov-Arnold network (KAN) leverages its ability to learn adaptive activation functions to better extract and refine the spectral features aggregated by GIN. Experimental results demonstrate that NAB can effectively improve the quality of the graph structure, the GIN aggregation method is competitive in HSI classification, and the proposed NAGIN outperforms the state-of-the-art methods on several public HSI datasets. © 1980-2012 IEEE.

关键词： graph convolutional network (GCN) Graph isomorphism network (GIN) hyperspectral image (HSI) classification Kolmogorov-Arnold network (KAN)

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DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

arXiv

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arXiv 2024年

作者： Du, Chenhe Lin, Xiyue Wu, Qing Tian, Xuanyu Su, Ying Luo, Zhe Zheng, Rui Chen, Yang Wei, Hongjiang Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao The School of Information Science and Technology ShanghaiTech University Shanghai China The Department of Critical Care Medicine Zhongshan Hospital Fudan University Shanghai China The Laboratory of Image Science and Technology The School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China The School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Magnon-mediated perpendicular magnetization switching by topological crystalline insulator SnTe with high spin Hall conductivity

arXiv

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arXiv 2025年

作者： Zhao, Pengnan Shi, Guoyi Lu, Wentian Yang, Lihuan Tan, Hui Ru Guo, Kaiwei Lai, Jia-Min Yu, Zhonghai Soumyanarayanan, Anjan Yuan, Zhe Wang, Fei Xu, Xiaohong Yang, Hyunsoo Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials Science Shanxi Normal University Taiyuan030006 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Singapore138634 Singapore Department of Physics National University of Singapore Singapore117551 Singapore Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Interdisciplinary Center for Theoretical Physics and Information Sciences Fudan University Shanghai200433 China

Magnons possess the ability to transport spin angular momentum in insulating magnetic materials, a characteristic that sets them apart from traditional electronics where power consumption arises from the movement of electrons. However, the practical application of magnon devices demands room temperature operation and low switching power of perpendicular magnetization. Here we demonstrate the low-power manipulation of perpendicular magnetization via magnon torques in SnTe/NiO/CoFeB devices at room temperature. Topological crystalline insulator SnTe exhibits a high spin Hall conductivity of σs ≈ 6.1 x 104 (ℏ /2e) (Ω \. m)-1, which facilitates the generation of magnon currents in an antiferromagnetic insulator NiO. The magnon currents traverse the 20-nm-thick NiO layer and subsequently exert magnon torques on the adjacent ferromagnetic layer, leading to magnetization switching. Notably, we achieve a 22-fold reduction in power consumption in SnTe/NiO/CoFeB heterostructures compared to Bi2Te3/NiO/CoFeB control samples. Our findings establish the low-power perpendicular magnetization manipulation through magnon torques, significantly expanding the range of topological materials with practical applications. © 2025, CC BY-NC-ND.

关键词： Tin alloys

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Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

arXiv

引用

arXiv 2024年

作者： Liang, Feng Zhang, Zhen Lu, Haifeng Leung, Victor C.M. Guo, Yanyi Hu, Xiping Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518000 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen518107 China Gansu Provincial Key Laboratory of Wearable Computing School of information Science and Engineering Lanzhou University Gansu730000 China The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Frontier Cross Disciplinary Research Institute Shenzhen MSU-BIT University Shenzhen518000 China The School of Mechanical and Electrical Engineering Beijing Institute of Technology Beijing10081 China

With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep learning, the large-scale scenario poses new challenges that include fault tolerance, scalability of algorithms and infrastructures, and heterogeneity in data sets, models, and resources. Due to intensive synchronization of models and sharing of data across GPUs and computing nodes during distributed training and inference processes, communication efficiency becomes the bottleneck for achieving high performance at a large scale. This article surveys the literature over the period of 2018-2023 on algorithms and technologies aimed at achieving efficient communication in large-scale distributed DL at various levels, including algorithms, frameworks, and infrastructures. Specifically, we first introduce efficient algorithms for model synchronization and communication data compression in the context of large-scale distributed training. Next, we introduce efficient strategies related to resource allocation and task scheduling for use in distributed training and inference. After that, we present the latest technologies pertaining to modern communication infrastructures used in distributed deep learning, including GPU interconnects, programmable network devices, collective communication protocols, and communication topologies. We focus our discussion of these topics on examining the impact of the communication overhead in a large-scale and heterogeneous setting. Finally, we conduct a case study on the distributed training of large language models at a large scale to illustrate how to apply these technologies in real cases. This article aims to offer researchers a comprehensive understanding of the current landscape of large-scale distributed deep learning and to reveal promising future research directions toward communication-efficient solutions in this scope.

关键词： Scalability

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