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arXiv 2022年

作者： Zhou, Yifeng Xu, Xing Liu, Shuaicheng Wang, Guoqing Lu, Huimin Shen, Heng Tao Center for Future Multimedia School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Mechanical and Control Engineering Kyushu Institute of Technology Japan

To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment. Some attempts focused on reducing the number of filters and feature channels but suffered from large performance loss, and a more practical and lightweight denoising network with fast inference speed is of high demand. To this end, a Thumbnail based Denoising Network dubbed Thunder, is proposed and implemented as a lightweight structure for fast restoration without comprising the denoising capabilities. Specifically, the Thunder model contains two newly-established modules: (1) a wavelet-based Thumbnail Subspace Encoder (TSE) which can leverage sub-bands correlation to provide an approximate thumbnail based on the low-frequent feature;(2) a Subspace Projection based Refine Module (SPR) which can restore the details for thumbnail progressively based on the subspace projection approach. Extensive experiments have been carried out on two real-world denoising benchmarks, demonstrating that the proposed Thunder outperforms the existing lightweight models and achieves competitive performance on PSNR and SSIM when compared with the complex designs. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Elusion-Based UAV-Assisted Covert Data Collection Under Uncertain Eavesdropper Mobility

Elusion-Based UAV-Assisted Covert Data Collection Under Unce...

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IEEE International Conference on Communications in China (ICCC)

作者： Renge Wang Haichuan Ding Yiqin Deng Ying Ma Jianping An Yuguang Fang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Control Science and Engineering Shandong University Jinan China School of Information and Electronics Beijing Institute of Technology Beijing China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper studies how to enable efficient covert data collection in a UAV-assisted network under the surveillance of an aerial eavesdropper. Unlike existing work, we consider a mobile eavesdropping UAV which patrols the area of interest with an unknown mobility pattern. By exploiting the legitimate UAV’s mobility, we propose an elusion-based covert data collection scheme by optimizing the trajectory of the legitimate UAV so that it always performs data collection at the locations far away from the eavesdropper to improve the covert throughput. Given the uncertain mobility of the eavesdropper, we formulate the design of the proposed data collection scheme as a reinforcement learning problem and develop a covertness-aware learning algorithm to facilitate efficient solution search. Simulation results demonstrate that, in comparison to existing proposals, our proposed scheme can effectively improve the performance of covert data collection.

关键词： Surveillance Simulation Reinforcement learning Data collection Autonomous aerial vehicles Throughput Search problems

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Safe Reinforcement Learning via Confidence-Based Filters

arXiv

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arXiv 2022年

作者： Curi, Sebastian Lederer, Armin Hirche, Sandra Krause, Andreas Learning & Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland Information-oriented Control Department of Electrical and Computer Engineering Technical University of Munich Germany

— Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach for certifying state safety constraints for nominal policies learnt via standard RL techniques, based on probabilistic dynamics models. Our approach is based on a reformulation of state constraints in terms of cost functions, reducing safety verification to a standard RL task. By exploiting the concept of hallucinating inputs, we extend this formulation to determine a "backup" policy which is safe for the unknown system with high probability. Finally, the nominal policy is minimally adjusted at every time step during a roll-out towards the backup policy, such that safe recovery can be guaranteed afterwards. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach. © 2022, CC BY.

关键词： Reinforcement learning

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Application of artificial intelligence in diagnosis of pulmonary tuberculosis

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Chinese Medical Journal 2024年第5期137卷 559-561页

作者： Jingli Du Yue Su Juan Qiao Shang Gao Enjun Dong Ruilan Wang Yanhui Nie Jing Ji Zhendong Wang Jianqin Liang Wenping Gong Tuberculosis Control Team Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Department of Computer Science and Technology College of Management Science and EngineeringCapital University of Economics and BusinessBeijing 100071China The Second Ward of Tuberculosis Department Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China

Tuberculosis(TB)is an infectious disease caused by Mycobacterium *** the diagnostic technology of pulmonary tuberculosis(PTB)has advanced,accurate and differential diagnoses of PTB are still *** recent years,the rapid development of artificial intelligence(AI)and its wide application in the medical field have provided new opportunities for diagnosing and treating TB and *** machine learning model of AI has not only helped physicians improve diagnostic accuracy,but also enabled them to make early preventive diagnoses for individuals at increased risk of ***,AI can guide physicians to formulate targeted treatment strategies for PTB patients with different conditions.

关键词： tuberculosis diagnosis artificial

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning

Microservice Deployment for Satellite Edge AI Inference via ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhiyong Yu Hei Victor Cheng Zhanpeng Yang Xin Liu Yuning Jiang Yong Zhou Yuanming Shi School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Aarhus University Denmark Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices’ communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time.

关键词： Satellites Accuracy Heuristic algorithms Satellite broadcasting Microservice architectures Low earth orbit satellites Edge AI computer architecture Resource management Optimization

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A Real-time Hand Gesture Recognition System for Human-computer and Human-Robot Interaction

A Real-time Hand Gesture Recognition System for Human-Comput...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2022

作者： Ponzi, Valerio Iacobelli, Emanuele Napoli, Christian Starczewski, Janusz Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology al. Armii Krajowej 36 Czestochowa42-200 Poland

The proposed hand gesture recognition (HGR) system is designed to enhance human-computer interaction (HCI) and human-robot interaction (HRI), which are crucial areas of research aimed at improving the way humans interact with computer or robot systems. With the growing need for intelligent computers and robots in a range of applications, including healthcare, manufacturing, and education, both HCI and HRI have gained significant importance. In this context, the HGR system plays a vital role by enabling natural and intuitive communication between humans and technology through hand gestures. The presented system uses a single camera and efficient image processing techniques that enable real-time gesture detection. Unlike other methods, our approach employs a basic video camera, which is widely available on most computers, eliminating the need for expensive and specialized hardware. © 2022 Copyright for this paper by its authors.

关键词： Human robot interaction

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

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Sensors 2024年第10期24卷 3098页

作者： Chi, Tsun-Kuang Chen, Tsung-Yi Lin, Yu-Chen Lin, Ting-Lan Zhang, Jun-Ting Lu, Cheng-Lin Chen, Shih-Lun Li, Kuo-Chen Abu, Patricia Angela R. Department of Electronic Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information Management Chung Yuan Christian University Taoyuan320317 Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Quezon1108 Philippines

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. © 2024 by the authors.

关键词： Deep learning

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A Bayesian k-vector estimation method for electromagnetic waves in magnetized cold plasma

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URSI Radio science Bulletin 2023年第378期2021卷 77-82页

作者： Yuji Tanaka Mamoru Ota Shoya Matsuda Yoshiya Kasahara Division of Electrical Engineering Graduate School of Engineering Kyoto University Kyotodaigaku-Katsura Nishikyo-ku Kyoto Kyoto Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College 13 Hongo-machi Toyama Toyama Japan Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa University Kakuma Kanazawa Ishikawa Japan

Using the plasma wave characteristics and remote sensing technology, the k-vector direction of plasma waves can provide important information for understanding the global features of space plasma. In this study, we proposed a Bayesian k-vector estimation method in magnetized cold plasma based on the wave distribution function method. The proposed method can be applied to various types of sensors with easy visualization and calculation of the estimation accuracy. We verified the effectiveness of the proposed method through simulations.

关键词： Estimation Plasmas Electromagnetics Mathematical models Magnetic sensors Bayes methods Plasma waves

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On the link between explicit MPC and the face lattice of the lifted feasible domain

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IFAC-PapersOnLine 2022年第16期55卷 308-313页

作者： Ştefan S. Mihai Florin Stoican Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University "Politehnica" of Bucharest 313 Splaiul Independenţei Sector 6 060042 Bucharest Romania Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest.

The paper provides a geometric interpretation for the explicit solution of the quadratic cost, linear-constrained MPC (model predictive control) problem. We link the face lattice of the lifted feasible domain (defined in the input and parameter space) with the critical regions which partition the parameter space and serve as polyhedral support for the piecewise affine explicit MPC solution. We provide geometric (face visibility) and algebraic (polyhedron emptiness) tests for the pruning of the candidate sets of active constraints.

关键词： Model Predictive control (MPC) Explicit MPC Face Lattice Constrained-Optimization Problem

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