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Security-Enhanced Data Sharing Via Efficient Sanitization for VANETs

IEEE Transactions on Mobile Computing 2025年第6期24卷 4925-4938页

作者： Zhong, Hong Zhou, Dan Cui, Jie Wang, Li Zhang, Jing Bolodurina, Irina He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Orenburg State University Faculty of Mathematics and Information Technologies Orenburg460018 Russia Wuhan University School of Cyber Science and Engineering Wuhan430072 China MatrixElements Technologies Shanghai Key Laboratory of Privacy Preserving Computation Shanghai201204 China

With the widespread deployment of vehicular ad-hoc networks (VANETs), data sharing has garnered considerable attention as a core feature of VANETs. Attribute-based proxy re-encryption (ABPRE) enables fine-grained access control and provides flexible ciphertext updates. The initially authorized vehicle generates the re-encryption key to enable ciphertext-to-ciphertext conversion in the cloud, allowing ciphertext to be shared with new recipients. However, initially authorized vehicles may not always be trustworthy and could share data with malicious receivers. In addition, the computation and communication overhead of ABPRE hinders its widespread application in VANETs. To address these issues, we propose a lightweight sanitizable scheme for edge-assisted VANETs based on ABPRE. In this scheme, the re-encryption key is verified by a sanitizer, to prevent the data from being shared with malicious data receivers. In addition, key-splitting techniques and edge computing are employed to reduce the communication and computation overhead of re-encryption. A comprehensive security analysis and performance evaluation demonstrate that the proposed scheme is efficient and practical. © 2002-2012 IEEE.

关键词： Ciphertext

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PHT-CAD: Efficient CAD Parametric Primitive Analysis with Progressive Hierarchical Tuning

arXiv

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arXiv 2025年

作者： Niu, Ke Chen, Yuwen Yu, Haiyang Chen, Zhuofan Que, Xianghui Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Computer-Aided Design (CAD) plays a pivotal role in industrial manufacturing, yet 2D Parametric Primitive Analysis (PPA) remains underexplored due to two key challenges: structural constraint reasoning and advanced semantic understanding. To tackle these challenges, we first propose an Efficient Hybrid Parametrization (EHP) for better representing 2D engineering drawings. EHP contains four types of atomic component (i.e., point, line, circle, and arc). Additionally, we propose PHT-CAD, a novel 2D PPA framework that harnesses the modality alignment and reasoning capabilities of Vision-Language Models (VLMs) for precise engineering drawing analysis. In PHT-CAD, we introduce four dedicated regression heads to predict corresponding atomic components. To train PHT-CAD, a three-stage training paradigm Progressive Hierarchical Tuning (PHT) is proposed to progressively enhance PHT-CAD’s capability to perceive individual primitives, infer structural constraints, and align annotation layers with their corresponding geometric representations. Considering that existing datasets lack complete annotation layers and real-world engineering drawings, we introduce ParaCAD, the first large-scale benchmark that explicitly integrates both the geometric and annotation layers. ParaCAD comprises over 10 million annotated drawings for training and 3,000 real-world industrial drawings with complex topological structures and physical constraints for test. Extensive experiments demonstrate the effectiveness of PHT-CAD and highlight the practical significance of ParaCAD in advancing 2D PPA research. The code and adopted datasets are available at https://***/yuwen-chen616/ PHT-CAD. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Beamforming Design for RIS-Aided ISCC in Internet of Vehicles Systems

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IEEE Internet of Things Journal 2025年第9期12卷 12153-12165页

作者： Yang, Ruihang Wang, Dezhi Zhu, Shiyin Zhu, Chen Bao, Jianrong Yang, Zhaohui Huang, Chongwen Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Xidian University State Key Laboratory of Integrated Service Networks Xi’an710071 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Science and Technology of China Hefei230026 China New H3C Technologies Company Ltd. Hangzhou310052 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China

With the development of communication technology, the Internet of Vehicles (IoV) is becoming increasingly important, enabling vehicle-to-everything communication for real-time information exchange and processing, thereby significantly enhancing traffic efficiency and safety. In this article, we consider a joint beamforming design problem in IoV, where the objective is to minimize transmission power, computation rate, and communication rate within the integrated sensing, communication, and computation (ISCC) framework. Moreover, reconfigurable intelligent surfaces (RISs) can provide additional spatial degrees of freedom to enhance the performance of ISCC systems in IoV within limited spectrum, energy resources, and complex interference management. To address the joint beamforming design problem, we present a cooperative beamforming algorithm called weight performance optimization (WPO), which explores three single-objective optimization problems in sensing, computation, and communication within the IoV context, using alternating optimization (AO) to simplify and solve these foundational elements of the WPO framework within limited resources and vehicle mobility, enhancing resource distribution while maintaining a balance between power efficiency and system performance. Numerical results demonstrate the efficiency and potential advantages of our proposed algorithms. Specifically, the results show that the sensing error of the WPO algorithm is reduced by up to 92.2% compared to existing popular algorithms, while the computation rate and communication rate are increased by more than 29.5% and 23.9%, respectively. © 2014 IEEE.

关键词： Array signal processing Sensors Interference Heuristic algorithms Reconfigurable intelligent surfaces Real-time systems Optimization Resource management Reliability Complexity theory

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Colocalized Raman and IR Spectroscopies via Vibrational-Encoded Fluorescence for Comprehensive Vibrational Analysis

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Journal of the American Chemical Society 2025年第19期147卷 16309-16318页

作者： Meng, Zhao-Dong Wu, Tai-Rui Zhou, Li-Ling You, En-Ming Dong, Zhi-Peng Zhang, Xia-Guang Chen, Gan-Yu Wu, De-Yin Yi, Jun Tian, Zhong-Qun School of Electronic Science and Engineering State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Fujian Key Laboratory of Ultrafast Laser Technology and Applications IKKEM Xiamen University Xiamen361005 China School of Ocean Information Engineering Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Xiamen361021 China Key Laboratory of Green Chemical Media and Reactions Ministry of Education School of Chemistry and Chemical Engineering Henan Normal University Xinxiang453007 China

Vibrational spectroscopy, including Raman scattering and infrared (IR) absorption, provides essential molecular fingerprint information, facilitating diverse applications, such as interfacial sensing, chemical analysis, and biomedical diagnostics. The complementary selection rules of Raman and IR spectroscopies offer distinct, yet mutually reinforcing, insights into molecular structure and dynamics. However, in dynamic or complex chemical environments, either technique alone is not capable of providing a complete and nuanced picture of molecular vibrations. Simultaneous detection of complementary vibrational modes within the same molecular group remains challenging due to wavelength discrepancies and sensitivity mismatches between Raman and IR spectroscopies. In this work, to address the gap between these spectroscopies, we developed an integrated approach based on vibrational-encoded fluorescence (VEF), in which the complementary vibrational information is respectively encoded into the different parts of fluorescence radiation: Stokes fluorescence carrying Raman information and anti-Stokes fluorescence reflecting IR information. This method employs a dual-resonant microsphere-on-mirror plasmonic structure to bridge the waveband gap, enabling the simultaneous detection of complete vibrational modes in the visible spectrum with ultrahigh sensitivity down to ∼100 molecules. Hyperspectral colocalization imaging demonstrates spatial correlations between the complementary vibrations. By careful calibration, the detection efficiency is improved by 8 orders of magnitude compared to unenhanced IR spectroscopy. This approach creates new opportunities for the precise identification of molecular vibrational information in complex chemical environments. © 2025 American Chemical Society.

关键词： Vibrational spectroscopy

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An Enhanced Model-Free Predictive Current control for PMSMs Incorporating Discrete Space Vector Modulation 19th

An Enhanced Model-Free Predictive Current Control for PMSMs ...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Yang, Yuhui Qin, Qing Qin, Yingmei Che, Yanqiu School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9789819613823

Model-free predictive current control (MFPCC) has excellent parameter robustness owing to unused parameters of the controller. However, the method has the current gradient update stagnation problem due to the limited availability of only 7 voltage vectors, which results in a high current/torque ripple. To address this shortcoming, an enhanced MFPCC for permanent magnet synchronous machines (PMSMs) incorporating discrete space vector modulation (DSVM) is proposed in this paper. First, the basic voltage vectors are expanded from 7 to 37 by DSVM. Then, a novel current gradient updating method is developed to effectively reduce the current/torque ripple caused by current gradient updating stagnation. Finally, a candidate vector preselection method is designed to simplify the calculation process of current gradient updating. The effectiveness of the proposed method is verified on a 2 kW PMSM driving experimental platform. The experimental results show that the THD is reduced from 25.5% to 7.8% while the dq-axes current ripple is respectively reduced from 5.1 A and 5.8 A to 2.2A and 1.5 A. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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LLaMA-Unidetector: A LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Xie, Jianlin Wang, Guanqun Zhang, Tong Sun, Yikang Chen, He Zhuang, Yin Li, Jun Beijing100081 China Peking University National Key Laboratory for Multimedia Information Processing Beijing100871 China China University of Geosciences School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430078 China

Object detection is a crucial task in computer vision for remote sensing applications. However, the reliance of traditional methods on predefined and trained object categories limits their applicability in open-world scenarios. A key challenge in open-vocabulary object detection lies in accurately identifying unseen objects. Existing approaches often focus solely on detecting object locations, struggling to recognize the categories of previously unseen targets. To address this issue, we propose a novel benchmark where models are trained on known base classes and evaluated on their performance in detecting and recognizing unseen or novel classes. To this end, we introduce llama-Unidetector, a universal framework that incorporates textual information into a closed-set detector, enabling the generalization to open-set scenarios. Our llama-Unidetector leverages a decoupled learning strategy that separates localization and recognition. In the first stage, a class-agnostic detector identifies objects, distinguishing only between foreground and background. In the second stage, the detected foreground objects are passed through TerraOV-LLM, a multimodal large language model, for recognition, utilizing the strong generalization capabilities of large language models to infer the correct categories. We propose a self-built Vision Question Answering (VQA) remote sensing dataset, TerraVQA, and conduct extensive experiments on the NWPU-VHR10, DOTA1.0, and DIOR datasets. The llama-Unidetector achieves impressive results, with a performance of 75.46% AP, 50.22% AP and 51.38% AP on the zero-shot detection benchmarks for the NWPU-VHR10, DOTA1.0 and DIOR datasets, respectively. © 1980-2012 IEEE.

关键词： Object detection

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A Trustworthy Cloud-Edge Collaboration Framework for Scheduling Distributed Energy Resources in Distribution Networks

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IEEE Transactions on Smart Grid 2025年第3期16卷 2691-2694页

作者： Jia, Qiangang Jiao, Wenshu Chen, Sijie Yan, Zheng Sun, Haitao Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Shandong University School of Electrical Engineering Jinan250100 China Ministry of Education Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Shanghai200240 China Taiyuan University of Technology College of Electrical and Power Engineering Shanxi Taiyuan030024 China Ministry of Education Key Laboratory of Cleaner Intelligent Control on Coal & Electricity Shanxi 030024 China

Integration of distributed energy resources, such as photovoltaics, has expanded rapidly within power distribution networks in recent years. Existing management architectures face great challenges in balancing data security and communication efficiency. To address this issue, the paper proposes a cloud-edge collaborative framework that caters to managing multiple distributed energy resources. Firstly, a two-stage structural design method is introduced to determine the optimal configuration of edge nodes for variable reduction. Secondly, a credit-based data interaction scheme considering inherent uncertainty of distributed energy resources is proposed to ensure trustworthy cloud-edge collaborative optimizations. The above work is expected to facilitate the in-depth application of cloud-edge collaboration in the energy scheduling field. © 2010-2012 IEEE.

关键词： Distributed energy

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Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

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IEEE Transactions on information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

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High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks

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IEEE Internet of Things Journal 2025年

作者： Zhang, Ning Ni, Shuo Chen, Liang Wang, Tong Chen, He Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China

Neural networks have been extensively applied across various Internet of Things (IoT) applications, such as drone-and satellite-based remote sensing and autonomous driving. With the increasing resolution and amount of data captured by sensors, the demand for real-time response in IoT applications is markedly increasing. However, it is difficult for existing convolutional neural network (CNN) accelerators for IoT applications on field-programmable gate array (FPGA) platforms to achieve high throughput because of the inherent dense multiplication operations of CNNs, memory bandwidth limitations and inefficient mapping mechanisms. In this article, a high-throughput and energy-efficient all adder neural network (ANN) accelerator for IoT applications on FPGA platform is proposed to solve this problem. First, a series of hardware-oriented algorithm optimization methods are proposed to simplify the processing flow of ANN and further minimize its deployment overhead. Second, a novel hardware architecture based on the idea of near-memory computation (NMC) is proposed to eliminate off-chip memory access completely and accelerate the reconstructed ANN in the pipeline. Third, a set of quantitative analysis methods for the proposed accelerator is presented to balance throughput and energy consumption, allowing the accelerator to adapt to the varying demands of different IoT application scenarios. Extensive experimental results on the AMD-Xilinx VC709 board demonstrate that the proposed accelerator achieves state-of-the-art performance in terms of throughput, energy efficiency, and throughput efficiency. Moreover, experiments on the AMD-Xilinx KV260 board highlight the architecture's exceptional scalability and energy efficiency, enabling a balance between speed and power consumption tailored to the specific requirements of IoT application scenarios. © 2014 IEEE.

关键词： Convolutional neural networks

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Fuzzy information Quantity Measurement and Feature Selection by Macrogranular Entropy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Zhu, Zhilin Zhang, Chucai Dai, Jianhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Changsha410081 China

Feature selection is an important data preprocessing process in artificial intelligence, which aims to eliminate redundant features while retaining essential features. Measuring feature significance and relevance between features is a significant challenge. Fuzzy information entropy is an extension of Shannon entropy. It is widely used for quantifying the information of fuzzy divisions. However, it has significant limitations, notably the lack of monotonicity in fuzzy conditional entropy measure of decision uncertainty in the feature selection process. We introduce a novel measurement Macrogranular Entropy (ME) and construct some generalized forms, such as conditional macrogranular entropy, mutual macrogranular information, and joint macrogranular entropy. The conditional macrogranular entropy exhibits monotonicity when measuring decision uncertainty. Additionally, we propose two feature selection algorithms: one based on Monotonic Conditional Macrogranular Entropy (MCME), and the other based on the Degree of Symmetric Association (ADSA). The ADSA algorithm and the MCME algorithm are compared against eight other feature selection algorithms through a series of experiments. The comparison was conducted based on classification performance using SVM and NB classifiers, as well as evaluation metrics including F1-score and Recall. In terms of all four evaluation metrics, ADSA and MCME achieved the top two rankings, respectively. Specifically, on the NB and SVM classifiers, the ADSA algorithm improves the average accuracy by 12.22% and 2.88% compared to the original feature set, while MCME improves the accuracy by 10.07% and 1.01%, respectively. Experimental comparisons demonstrate that ADSA algorithm effectively removes redundant information from the dataset during feature selection. © 2025 IEEE.

关键词： Feature Selection

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