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2025 IEEE Wireless Communications and networking Conference, WCNC 2025

作者： Liu, Haoxuan Qi, Nan Li, Xiaojie Boulogeorgos, Alexandros A. A. Tsiftsis, Theodoros A. Xiao, Ming Röning, Juha Nanjing University of Aeronautics and Astronautics Nanjing China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden University of Oulu Biomimetics and Intelligent Systems Group Finland

ISBN: (纸本)9798350368369

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Discovering Complex Correlations Among Multiple IoT Devices in Smart Environments

Discovering Complex Correlations Among Multiple IoT Devices ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Andrew D'Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Air Force Research Laboratory Rome NY USA

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.

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A Resource-Efficient Content Sharing Mechanism in Large-Scale UAV Named Data networking

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IEEE/ACM Transactions on networking 2024年

作者： Jin, Chenlang Yao, Haipeng Mai, Tianle Xu, Jiaqi Zhang, Qi Yu, F. Richard Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In recent years, there has been significant attention in UAV Named Data networking (UNDN) from both industry and academia. This network paradigm adopts a 'request-reply' communication model that allows UAVs to access desired content without the need for specific information regarding the geographical location or IP address of the content producer. This IP-independent design is well-suited for dynamic UAV swarms, but it presents challenges in establishing matching policies between content consumers and producers. This is because that during the distributed decision-making process in content sharing, consumers cannot possess private information regarding producers, and producers may lack the motivation to distribute content. As a result, a revelation and incentive mechanism is needed to be formulated in the system. In this paper, a resource-efficient content-sharing mechanism is proposed to address the aforementioned challenges. First, we propose a contract-based mechanism to incentivize content producers to share content and reveal their private information at the same time. The problem of obtaining the optimal contract is discussed in both cases of information asymmetry and complete information. Then, the Gale-Shapley (GS) algorithm is adopted to make a stable many-to-one matching between content consumers and content producers. The simulation results verify the feasibility, effectiveness and energy efficiency of the proposed mechanism. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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Conflict-Based Search and Improvement Strategies for Solving a New Lexicographic Bi-Objective Multi-Agent Path Finding Problem

Conflict-Based Search and Improvement Strategies for Solving...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Li, Siyi Li, Xingyang Zhou, Mengchu Zhao, Ziyan Liu, Shixin Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University Foshan Graduate School of Innovation Foshan528311 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Macao Inst. of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology 999078 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Multi-Agent Path Finding (MAPF) is an important problem with a variety of applications. Its aim is to find collision-free paths for agents having separate start and goal positions. This work proposes a new lexicographic bi-objective MAPF considering different task types, where agents are divided into two kinds to perform critical and acritical tasks. This is common in practical intelligent warehousing scenarios where a critical/acritical-task-performing agent (called c-agent and a-agent, respectively) may represent a full-load/no-load one or the one conducting urgent/non-urgent tasks. The primary objective is to minimize the sum-of-costs of c-agents, while the secondary objective is to minimize the sum-of-costs of a-agents. Two MAPF algorithms are modified to fit and solve the concerned problem for the first time. Moreover, four improvement strategies are embedded to the proposed algorithms and proved to be effective in solving MAPF problems with different task types. © 2022 IEEE.

关键词： Multi agent systems

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Design and application of a one-stop mobile application sharing platform for steel enterprises

Design and application of a one-stop mobile application shar...

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第42届中国控制会议

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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Robust Beamforming Design and Antenna Selection for Dynamic HRIS-aided MISO systems

arXiv

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arXiv 2024年

作者： Wang, Jintao Zhou, Binggui Ma, Chengzhi Gong, Shiqi Yang, Guanghua Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

In this paper, we propose a dynamic hybrid active-passive reconfigurable intelligent surface (HRIS) to enhance multiple-input-single-output (MISO) communications, leveraging the property of dynamically placing active elements. Specifically, considering the impact of hardware impairments (HWIs), we investigate channel-aware configurations of the receive antennas at the base station (BS) and the active/passive elements at the HRIS to improve transmission reliability. To this end, we address the average mean-square-error (MSE) minimization problem for the HRIS-aided MISO system by jointly optimizing the BS receive antenna selection matrix, the reflection phase coefficients, the reflection amplitude matrix, and the mode selection matrix of the HRIS. To overcome the non-convexity and intractability of this problem, we first transform the binary and discrete variables into continuous ones, and then propose a penalty-based exact block coordinate descent (PEBCD) algorithm to alternately solve these subproblems. Numerical simulations demonstrate the significant superiority of our proposed scheme over conventional benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Pneumonia Classification using deep learning: a comparative study

Pneumonia Classification using deep learning: a comparative ...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Mohamed Lakhdar Tiar Nadjiba Terki Juan Jose Dominguez-Jimenez IL3CUB Laboratory University of Mohamed Khider Biskra Algeria Electrical Engineering Department University of Mohamed Khider Biskra Algeria Dept. Computer Languages and Systems University of Cadiz Escuela Superior de Ingenieria Cadiz Spain

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Pneumonia is an infectious disease of the lungs, caused by viruses, bacteria or fungi. Pneumonia is distinguished by acute inflammation of the lung tissue, causing the consolidation of the terminal bronchioles and alveoli. According to the WHO (the World Health Organization), this disease causes about 4 million deaths. Among the methods of diagnosing pneumonia uses a chest X-ray. This is widely used to visualize pulmonary abnormalities. This work aims at the detection and characterization of pneumonia by the development of computer-assisted diagnosis systems (DAOC). We use deep learning algorithms and chest X-ray images from a pneumonia database to examine the classification of pneumonia. To improve the accuracy, we compare several deep learning models. This research contributes to meeting the growing demand for medical personnel by addressing the global incidence of chest disorders.

关键词： Deep learning Visualization Pneumonia Lung Signal processing algorithms Medical services Signal processing

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VoiceGuard: An Effective and Practical Approach for Detecting and Blocking Unauthorized Voice Commands to Smart Speakers

VoiceGuard: An Effective and Practical Approach for Detectin...

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International Conference on Dependable systems and Networks (DSN)

作者： Xuening Xu Chenglong Fu Xiaojiang Du E. Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems UNC Charlotte Charlotte NC USA Information Directorate Air Force Research Laboratory Rome NY USA

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

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Exploring Channel Estimation and Signal Detection for ODDM-based ISAC systems

arXiv

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arXiv 2024年

作者： Wang, Dezhi Huang, Chongwen Liu, Lei Chen, Xiaoming Wang, Wei Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Electrical Engineering and Computer Science the KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication (ISAC) systems based on the orthogonal delay-Doppler multiplexing (ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler (DD) plane. To enhance the communication performance in the ODDM-based ISAC systems, we first propose a low-complexity approximation algorithm for channel estimation, which addresses the challenge of the high complexity from high resolution in the ODDM modulation, and achieves performance close to that of the maximum likelihood estimator scheme. Then, we employ the orthogonal approximate message-passing scheme to detect the symbols in the communication process based on the estimated channel information. Finally, simulation results show that the detection performance of ODDM is better than other multi-carrier modulation schemes. Specifically, the ODDM outperforms the orthogonal time frequency space scheme by 2.3 dB when the bit error ratio is 10−6 Copyright © 2024, The Authors. All rights reserved.

关键词： Signal detection

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