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arXiv 2024年

作者： Huang, Yixuan Yang, Jie Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory The Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China

Retrieving range information in three-dimensional (3D) radio imaging is particularly challenging due to the limited communication bandwidth and pilot resources. To address this issue, we consider a reconfigurable intelligent surface (RIS)-aided uplink communication scenario, generating multiple measurements through RIS phase adjustment. This study successfully realizes 3D single-frequency imaging by exploiting the near-field multi-view image correlations deduced from user mobility. We first highlight the significance of considering anisotropy in multi-view image formation by investigating radar cross-section properties and diffraction resolution limits. We then propose a novel model for joint multi-view 3D imaging that incorporates occlusion effects and anisotropic scattering. These factors lead to slow image support variation and smooth coefficient evolution, which are mathematically modeled as Markov processes. Based on this model, we employ the Expectation Maximization-Turbo-Generalized Approximate Message Passing algorithm for joint multi-view single-frequency 3D imaging with limited measurements. Simulation results reveal the superiority of joint multi-view imaging in terms of enhanced imaging ranges, accuracies, and anisotropy characterization compared to single-view imaging. Combining adjacent observations for joint multi-view imaging enables a reduction in the measurement overhead by 80%. Copyright © 2024, The Authors. All rights reserved.

关键词： Anisotropy

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An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Blockage-Resilient Integrated Sensing and communication in mmWave Networks: Multi-View Collaboration and Efficient Task Allocation

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IEEE Transactions on Mobile Computing 2025年

作者： Cui, Yue Ding, Haichuan Ma, Ying Li, Xuanheng Zhang, Haixia Fang, Yuguang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Dalian University of Technology School of Information and Communication Engineering Dalian116023 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shandong University Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Control Science and Engineering Jinan250061 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets' radar cross section (RCS), the blockage-resilient multiBS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages. © 2025 IEEE. All rights reserved.

关键词： Integer programming

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Motion Estimation and Signal Integration Method for High-Speed Target Detection With Airborne Bistatic Radar

Motion Estimation and Signal Integration Method for High-Spe...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Zerui Zhang Xiaolong Li Fan Yang Mingxing Wang Jie Gu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Electromagnetic Space Security Southwest China Research Institute of Electronic Equipment Chengdu China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

This paper addresses the motion estimation and signal integration problem for high speed target detection with airborne bistatic radar, where the range migration (RM) and Doppler frequency migration (DFM) induced by target's motion, and the motion error of the airborne bistatic radar are all taken into account. We establish the signal model for airborne bistatic radar system with high-speed target, along with motion error model which simultaneously encompasses both translational error and rotational error. Besides, we propose a motion estimation and signal integration method. In particular, the proposed method applies the generalized Radon transform (GRT) to the estimate target's equivalent motion parameters (i.e., range, velocity and acceleraiton). Subsequently, the proposed method utilizes the 3-dimensional (3-D) generalized Radon Fourier transform (GRFT) to achieve the motion error parameters estimation of airborne platform, where the signal coherent integation results could also be obtained. Numerical simulations and performance analysis verify the effectiveness of proposed approach, which can obtain better integration detection and estimation performance than the typical existed methods.

关键词： Motion estimation Atmospheric modeling Simulation Image processing Radon Estimation Object detection

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UAV-NIDD: A Dynamic Dataset for Cybersecurity and Intrusion Detection in UAV Networks

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IEEE Transactions on Network science and Engineering 2025年

作者： Hadi, Hassan Jalil Cao, Yue Khan, Muhammad Khurram Ahmad, Naveed Hu, Yulin Fu, Chao Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430073 China King Saud University Center of Excellence in Information Assurance Riyadh Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh Saudi Arabia Wuhan University School of Electronic Information China Information and Communication Technology Co. LtdChina China

Unmanned Aerial Vehicles (UAVs) are necessary for numerous tasks but are vulnerable to cyber threats due to their widespread use and connectivity. The lack of a comprehensive dataset necessitates the development of effective detection and mitigation solutions. Our work introduces UAV-NIDD, a new dataset that addresses the gaps in understanding and countering both cyber and physical threats in UAV networks. It includes three distinct attack scenarios: compromised UAV initiating a network-wide attack, access point compromised network-wide intrusion, and compromised Ground control Station (GCS) establishing a network-wide attack. We develop a real-time testbed for creating UAV-NIDD (Unmanned Aerial Vehicles-Network Intrusion Detection Dataset), incorporating UAV devices, data collection tools, and controllers. Our testbed facilitates cyberattack execution and data gathering under normal and attack conditions. Our dataset covers various cyber-attacks like Scanning, Reconnaissance, DoS, DDoS, GPS Jamming & Spoofing, MITM, Replay, Evil Twin, Brute-Force, and Fake Landing packet attacks. Additionally, UAV-NIDD presents a valuable resource for AI and ML solutions, strengthening UAV networks against evolving cyber threats. Moreover, we offer open access and cooperative innovation in terms of long-term updating of dataset. © 2013 IEEE.

关键词： Network intrusion

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GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network 6

GraphEye: A Novel Solution for Detecting Vulnerable Function...

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Zhou, Li Huang, Minhuan Li, Yujun Nie, Yuanping Li, Jin Liu, Yiwei School Of Information And Communication Engineering University Of Electronic Science And Technology Of China Chengdu China National Key Laboratory Of Science And Technology On Information System Security Beijing China School Of Computer Science And Engineering University Of Electronic Science And Technology Of China Chengdu China

ISBN: (纸本)9781665418157

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatically detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification problem. GraphEye is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention networks, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6%, 95.6%, 96.1%, 92.6%, and 96.1% respectively, which validate the effectiveness of the proposed solution. © 2021 IEEE.

关键词： Semantics

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

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IEEE Conference on Global communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Jianbo Du College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China School of Information Technology Carleton University Ottawa Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover’s algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms.

关键词： Quantum entanglement Computational modeling Simulation Temporal difference learning Parallel processing Global communication Integrated circuit modeling Quantum circuit

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

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IEEE Visual communications and Image Processing (VCIP)

作者： Da Ai Siyu Qin Zihe Nie Hui Yuan Ying Liu Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA- Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： Point cloud compression Three-dimensional displays Visual communication Convolution Semantic segmentation Semantics Transformers Feature extraction Data mining Testing

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A CPK-Based Identity Authentication Scheme for IoT

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Computer Systems science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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Byzantine-Robust Federated Learning Framework via a Server-Client Defense Mechanisms

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IEEE Internet of Things Journal 2025年

作者： Song, Anxiao Zhang, Tao Cheng, Ke Cao, Yang Zhu, Xinghui Shen, Yulong Xidian University School of Computer Science and Technology Xi’an China Shaanxi Key Laboratory of Network and System Security Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi’an710121 China Institute of Science Tokyo Department of Computer Science Tokyo Japan

Federated Learning (FL), a distributed machine learning (ML) framework, is susceptible to Byzantine attacks since the attacker can manipulate clients' local data or models to compromise the performance of the global model. There has been a wealth of defenses developed to mitigate the attacks by limiting the impact of malicious models. Nevertheless, the attacker can easily circumvent these approaches that rely solely on a single server-side defense, stemming from the high dimensionality of models and the variety of Byzantine attacks. Therefore, we propose Basalt, a Byzantine-robust federated learning framework with a server-client joint defense mechanism that enables multiple clients to train a global ML model under Byzantine attacks. On the client side, we design an efficient self-defense approach with model-level penalty loss that restricts local-benign divergence and decreases local-malicious correlation to prevent misclassification. On the server side, we present an efficient defense strategy based on the manifold and maximum clique, further strengthening the FL's resilience against Byzantine attacks. We provide theoretical guarantees for global model convergence in FL with Byzantine attacks. Our extensive experiments demonstrate that Basalt outperforms existing state-of-the-art works. Especially, it achieves nearly 100% accuracy for detecting malicious clients in nonindependent and nonidentically distributed (Non-IID) MNIST datasets under various Byzantine attacks. © 2014 IEEE.

关键词： Federated learning

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