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BAST: Blockchain-Assisted Secure and Traceable Data Sharing Scheme for Vehicular Networks

IEEE Transactions on Information Forensics and Security 2025年 20卷 4664-4678页

作者： Liu, Xinzhong Cui, Jie Zhang, Jing Yin, Rongwang Zhong, Hong Wei, Lu Bolodurina, Irina He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Hefei University Department of Basic Experiment and Training Hefei230601 China Orenburg State University Faculty of Mathematics and Information Technologies Orenburg460018 Russia Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shanghai Key Laboratory of Privacy Preserving Computation MatrixElements Technologies Shanghai201204 China

In vehicular networks, caching service content on edge servers (ESs) is a widely accepted strategy for promptly responding to vehicle requests, reducing communication overhead, and improving service experience. However, implementing such an architecture requires addressing the challenges associated with ES response data reliability and communication security. In this study, to tackle the ES response data reliability issue, a blockchain-assisted threshold signature scheme for cache-based vehicular networks is proposed. The scheme utilizes a threshold mechanism to sign the data broadcast by the ES, incorporates blockchain to trace malicious signers, and avoids the shortcomings and limitations associated with idealized assumptions for the ES in existing data-sharing schemes. Moreover, considering the communication security and high-speed mobility of vehicles, using the non-interactive signatures of knowledge based on the Σ-protocol, a secure and efficient message authentication scheme for vehicles and ESs is provided. Through rigorous security proofs and comprehensive analyses, our scheme satisfies the communication security requirements of vehicular networks. By leveraging the JPBC library for performance analysis, the proposed scheme demonstrates advantages as concerns both computation and communication overheads compared to related schemes. Moreover, we implemented the proposed scheme on an Ethereum test network (i.e., Goerli) to validate its feasibility. © 2005-2012 IEEE.

关键词： Sensitive data

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DSOD-YOLO: A lightweight dual feature extraction method for small target detection

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Digital signal processing: A Review Journal 2025年 164卷

作者： Nie, Yuan Lai, Huicheng Gao, Guxue The College of Computer Science and Technology Xinjiang University Urumqi830017 China The Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830017 China

As object detection techniques advance, large-object detection has become less challenging. However, small-object detection remains a significant hurdle. DSOD-YOLO is a lightweight small-object detection network based on YOLOv8, designed to balance detection accuracy with model efficiency. To accurately detect small objects, the network employs a dual-backbone feature extraction architecture, which enhances the extraction of small-object details. This addresses the issue of detail loss in deep models. Additionally, a Channel-Scale Adaptive Module (FASD) is introduced to adaptively select feature channels and image sizes based on the required feature information. This helps mitigate the problem of sparse feature information and information loss during feature propagation for small objects. To strengthen contextual information and further improve small-object detection, a lightweight Context and Spatial Feature Calibration Network (CSFCN) is integrated. CSFCN performs context correction and spatial feature calibration through its two core modules, Context Feature Calibration (CFC) and Spatial Feature Calibration (SFC), based on pixel context similarity and channel dimensions, respectively. To reduce model complexity, the network undergoes a pruning process, achieving lightweight small-object detection. Furthermore, knowledge distillation is employed, with a large model acting as a teacher network to guide DSOD-YOLO, leading to further accuracy improvements. Experimental results demonstrate that DSOD-YOLO outperforms state-of-the-art algorithms like YOLOv9 and YOLOv10 on multiple small-object datasets. Additionally, a new small-object dataset (SmallDark) is created for low-light conditions, and the proposed method surpasses existing algorithms on this custom dataset. © 2025 Elsevier Inc.

关键词： Image enhancement

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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High Frame Rate Along-Track Swarm SAR Sub-Aperture Collaboration Imaging for Moving Target

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Jiang, Nan Chen, Jianlai Zhu, Jiahua Liang, Buge Yang, Degui Huang, Xiaotao Xing, Mengdao Central South University School of Automation Changsha410083 China National University of Defense Technology College of Meteorology and Oceanography Changsha410073 China National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Xidian University National Laboratory of Radar Signal Processing Xi’an710071 China

As a novel configuration of along-track Multistatic SAR (Multi-SAR), the high frame rate Along-Track Swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this paper investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the highresolution, high frame rate ATS-SAR Sub-Aperture Collaborative Imaging algorithm for Moving Targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness. © 1980-2012 IEEE.

关键词： Imaging Synthetic aperture radar Apertures Data acquisition Transmitters radar polarimetry radar imaging Parameter estimation High-resolution imaging Estimation

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Covert Communications in D2D Underlaying Cellular Networks With Dual Uncertainties at the Warden

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IEEE Internet of Things Journal 2025年

作者： Du, Zhiyi Chen, Weiyu Luo, Junshan Ding, Haiyang Wang, Shilian Sun, Zhengbo National University of Defense Technology College of Electronic Science and Technology Changsha410073 China National University of Defense Technology College of Information and Communication Wuhan430019 China National Key Laboratory on Blind Signal Processing Chengdu610041 China

Covert device-to-device (D2D) communication aims to hide the existence of D2D transmission behavior from the warden, which plays a pivotal role in bolstering D2D communication security. The existing research on covert D2D communication either considered the interference's assistance with perfect channel state information (CSI) available at the warden, or considered channel distribution information (CDI) at the warden without the assistance of interference. These setups facilitate the performance analyses and the parameters design, which, however, did not fully exploit the potential of covert D2D communication, since both channel uncertainty and interference uncertainty exist in practice. In this paper, two covert schemes are proposed for covert D2D communication with dual uncertainties at the warden, where the warden has imperfect CSI. The base station transmits artificial noise to assist covert communication in the first scheme. In the second scheme, the cellular user adopts variable power to confuse the warden. For both schemes, the closed-form expressions of the warden's approximate minimum detection error probability and the achievable D2D communication rate are derived. The achievable D2D communication rate is maximized by jointly optimizing the transmit power of different links with given covertness requirement. The results suggest that the proposed schemes achieve higher communication performance by considering dual uncertainties. Moreover, the scheme with variable cellular user's transmit power performs better when the covert receiver can not well handle the interference from artificial noise. © 2025 IEEE.

关键词： Mobile telecommunication systems

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A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise signal processing algorithms Clustering algorithms Linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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GCW-YOLOv8n: Lightweight Safety Helmet Wearing Detection Algorithm

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Computer Engineering and Applications 2025年第3期61卷 144-154页

作者： Xu, Zhuang Qian, Yurong Yan, Feng Key Laboratory of Software Engineering Xinjiang University Urumqi830091 China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China School of Computer Science and Technology Xinjiang University Urumqi830046 China

China is a major industrial country in the world. In various construction environments, the falling of construction materials and collisions on construction sites are the main causes of casualties. Accidents caused by head injuries often occur, and wearing safety helmets can ensure the safety of construction personnel to the greatest extent possible. In order to solve the problems of poor timeliness and low management efficiency of manual management, the existing models have strict requirements for computing power, large memory requirements, and handling of load and data transmission delay of industrial equipment, and to achieve edge computing and real-time control, a modified helmet wearing detection algorithm based on YOLOv8n is proposed. Firstly, a new GS-C2f module is proposed, which introduces GhostConv and SE (squeeze and excitation) attention mechanism, effectively reducing the computational complexity of the model and helping the network extract features effectively. Secondly, the CBAM attention mechanism is introduced in the Neck section to enhance the model focus on effective features. Finally, Wise-IoUv3 is introduced to further improve the accuracy of the model. Through experiments, compared with the original YOLOv8n model, this model achieves a 21.24% reduction in computational parameters and a 0.01 improvement in recognition accuracy, achieving satisfactory results between model accuracy and complexity. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.;science Press. All rights reserved.

关键词： Office equipment

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A weighted low-rank and sparse constraint-based multi-channel radar forward-Looking imaging Method

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12973-12987页

作者： Tang, Junkui Ran, Lei Zheng, Liu Xie, Rong Liu, Yan Han, Genquan Xidian University Xi'an7100071 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China Xidian University School of Microelectronics Xi'an7100071 China

radar super-resolution imaging method with joint low-rank and sparse constraints has garnered increasing attention. However, in complex imaging scenarios, the low-rank property of the signal matrix is often not prominent, which limits the performance of directly applying low-rank constraints in super-resolution imaging. To address this issue, this paper proposes a weighted low-rank and sparse (WLRS) constraint-based multi-channel radar forward-looking super-resolution imaging method. First, the proposed method calculates weighting coefficients using the covariance matrix of radar echoes and applies weighted constraints to the signal matrix, thereby enhancing its low-rank property and significantly improving forward-looking super-resolution imaging performance. Then, in solving the optimization problem, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose variables and reduce the complexity of the solution. To further enhance computational efficiency, the symmetry of the weighting matrix and the characteristics of the dictionary matrix in sparse imaging, specifically as a partial Fourier dictionary, are leveraged. A fast matrix inversion method based on eigenvalue decomposition is proposed to mitigate the computational complexity induced by super-resolution requirements. Finally, the effectiveness and superiority of the proposed method in complex scenarios are validated through comparative experiments on simulated and measured data. © 2008-2012 IEEE.

关键词： Eigenvalues and eigenfunctions

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Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷

作者： Wang, Jiahui Xu, Qin Jiang, Bo Luo, Bin The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China The Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

The transductive Few-shot Learning (FSL) mostly employs either prototype learning or label propagation methods to generalize to new classes by using the information of all query samples. However, existing methods have several main limitations. First, the prototype methods mainly focus on support samples which fail to fully exploit the relationships of query samples. Second, existing label propagation methods are generally not effective for the class-imbalanced problem. Third, existing works usually optimize the learnable parameters during inference which significantly reduces the efficiency of existing methods. To address these limitations, this article proposes an efficient and robust method for transductive FSL problem, termed Prototype-based Soft-label Propagation (PSLP), which combines the prototype learning and label propagation together for FSL problem. In our proposed method, first, the soft-label presentation for each query sample is estimated by leveraging prototypes. Then, the soft-label propagation is conducted on the learned query-support graph and the prototype representation is rectified. Both steps are conducted progressively for boosting the performance. Moreover, to learn effective prototypes for soft-label estimation and the desirable query-support graph for soft-label propagation, we design a new joint message passing scheme to learn the sample presentation and relational graph jointly. The PSLP method is parameter-free and can be implemented very efficiently. The experiments conducted on four popular benchmarks show that our method achieves competitive results on both balanced and imbalanced settings compared to the state-of-the-art methods. The code is released at https://***/mobulan/PSLP. © 2025 Copyright held by the owner/author(s).

关键词： Few-shot learning Transductive learning Message passing Label propagation

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Small Target Insect Detection Based on Improved YOLOv8n

Small Target Insect Detection Based on Improved YOLOv8n

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jianyu Shi Yuan Jia Gang Zhou Jiajia Wang Zhenhong Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory School of Statistics Renmin University of China Beijing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Insect pests greatly affect the growth and harvest of crops, and accurate identification of insect species is very important to the agricultural industry. The insect dataset has the problem that the target is too small to locate, detect, and identify. To solve this problem, We designed a modified YOLOv8n for detecting small target insects. First, we add a small object detection head to the network structure of the YOLOv8n (You Only Look Once) model. Then, we improve C2f and Conv in YOLOv8n, construct C2f-RFAConv by adding Receptive-Field Attention Convolutional Operation (RFAConv) into C2f for the first time, and replace the original convolution with RFAConv for feature extraction. Finally, we improve Wise-IoU (WIoU) loss function to solve the problem of small object detection difficulty. When the number of parameters is reduced, the performance of our model YOLOv8n-Improved on the Yellow Sticky Traps dataset and self-built dataset is greatly improved compared with YOLOv8n, and it also has great advantages compared with other models in the YOLO series. This work plays an important role in our efforts to achieve intelligent pest management in agriculture.

关键词： YOLO Smart agriculture Accuracy Head Convolution Insects signal processing algorithms Pesticides Feature extraction Speech processing

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