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2nd International Conference on Electronic information technology, EIT 2023

作者： Wu, Chao Wang, De-Yong Shi, Wen-Xi Fang, Jian Zhao, Xueyi Fu, Yan-Yun School of Information Science and Engineering Xinjiang University Urumqi830000 China XinJiang Lianhai INA-INT Information Technology Co. Ltd Urumqi830000 China Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Urumqi830000 China Beijing Academy of Science and Technology Beijing100035 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

A lightweight detection algorithm based on YOLOv5 is proposed to solve the problems that the target detection algorithm has many network parameters and the large size of the model is not conducive to the actual deployment. Firstly, the depth separable convolution was introduced to reconstruct the backbone network to reduce the network parameters. Then, the residual element was redesigned by ghost convolution and integrated into the Neck end to further reduce the model volume. Finally, the detection accuracy and recognition effect of the algorithm were improved by modifying the detection layer. When the recognition accuracy of the improved lightweight algorithm is reduced by 3.4%, the model volume is reduced by 75%, and the number of network parameters is reduced by 81%. The results show that the algorithm is able to maintain a high recognition accuracy, and the volume and parameters are greatly reduced. It is suitable for embedded and other system resource limited scenarios, and has a practical application prospect. © 2023 SPIE.

关键词： Convolution

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Task Offloading and Primary Node Selection in Blockchain, Digital Twin, and MEC Enabled Internet of Vehicles 100

Task Offloading and Primary Node Selection in Blockchain, Di...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Du, Jianbo Fang, Huifang Li, Shulei Du, Pengfei Kong, Ziwen Kang, Jiawen Niyato, Dusit Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an China Xidian University School of Communications Engineering Xi'an710071 China Tianyuan Ruixin Communication Technology Co. LTD. Xi'an710071 China Xihua University Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Chengdu610039 China Guangdong University of Technology School of Automation China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798331517786

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects. © 2024 IEEE.

关键词： Dynamic programming

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Artificial Intelligence-Aid OTFS Receiver: Design and Performance Evaluation

Artificial Intelligence-Aid OTFS Receiver: Design and Perfor...

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Ubiquitous communication (Ucom), International Conference on

作者： Chenxi Wang Li Ping Qian Huijie Zhu College of Information Engineering Zhejiang University of Technology Hangzhou China Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Jiaxing China

OTFS technology is attracting much attention due to its strong adaptability to high-speed mobility and high spectrum efficiency. Studies show that applying deep learning (DL) to traditional communication systems significantly improves their performance. To this end, this paper proposes an AI-aid OTFS receiver to enhance the performance of OTFS systems in high-speed mobile communication scenarios. The proposed receiver employs two networks - a channel estimation network called TFNet and a decoding network called RecovNet. In the channel estimation module, TFNet performs more accurate channel estimation based on the results of traditional channel estimation methods. In the decoding module, RecovNet decodes the signal based on the equalized signals. To validate the superiority of our proposed receiver, we conduct comparative testing with a traditional receiver under the integer Doppler channel. The experimental results demonstrate that integrating DL technology in OTFS systems effectively enhances their performance.

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New ultrashort pulsewidth measurement technology based on interference jitter and FPGA platform

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Chinese Optics Letters 2022年第3期20卷 46-51页

作者： Jin Li Yanbo Dou Lixin Wang Jinhai Zou Yu Ding Hang Wang Qiujun Ruan Zhipeng Dong Zhengqian Luo Department of Electronic Engineering Xiamen UniversityXiamen 361005China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China

Conventional ultrashort pulsewidth measurement technology is autocorrelation based on second-harmonic generation;however,nonlinear crystals and bulky components are required,which usually leads to the limited wavelength range and the difficult adjustment with free-space light ***,we proposed a compact all-fiber pulsewidth measurement technology based on the interference jitter(IJ)and field-programmable gate array(FPGA)platform,without requiring a nonlinear optical device(*** crystal/detector).Such a technology shows a wide measurement waveband from 1 to 2.15μm at least,a pulsewidth range from femtoseconds to 100 ps,and a small relative error of 0.15%-3.8%.In particular,a minimum pulse energy of 219 fj is experimentally detected with an average-power-peak-power product of 1.065×10^(-6)W^(2).The IJ-FPGA technology may offer a new route for miniaturized,user-friendly,and broadband pulsewidth measurement.

关键词： pulse width measurement interference jitter FPGA platform optical device

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Ranking on Heterogeneous Manifold for Multimodal information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an710049 China Air Force Engineering University Air Traffic Control and Navigation College Xi’an710051 China Air Force Engineering University Information and Navigation College Xi’an710043 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Xidian University School of Computer Science and Technology Xi’an710126 China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

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IEEE Transactions on Cognitive communications and Networking 2024年

作者： Lu, Yao Zhu, Yutao Li, Yuqi Xu, Dongwei Lin, Yun Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Binjiang Institute of Artificial Intelligence Hangzhou310056 China Chinese Academy of Sciences Institute of Computing Technology Beijing China Harbin Engineering University College of Information and Communication Engineering Harbin China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China Peng Cheng Laboratory PCL Research Center of Networks and Communications Shenzhen518000 China Hangzhou310056 China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for Automatic Modulation Recognition (AMR). Although these AMR models yield impressive results, they often come with high computational complexity and large model sizes, which hinders their practical deployment in communication systems. To address this challenge, we propose a novel layer pruning method, PSR. Specifically, we decompose the AMR model into several consecutive blocks, each containing consecutive layers with similar semantics. Then, we identify layers that need to be preserved within each block based on their contribution. Finally, we reassemble the pruned blocks and fine-tune the compact model. Extensive experiments on five datasets demonstrate the efficiency and effectiveness of PSR over a variety of state-of-the-art baselines, including layer pruning and channel pruning methods. © 2015 IEEE.

关键词： Signal modulation

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Rethinking Graph Transformer Architecture Design for Node Classification

arXiv

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arXiv 2024年

作者： Zhou, Jiajun Chen, Xuanze Xie, Chenxuan Yu, Shanqing Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Binjiang Institute of Artificial Intelligence ZJUT Hangzhou310056 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing. However, this also imposes several limitations in node classification applications: 1) nodes are susceptible to global noise;2) self-attention computation cannot scale well to large graphs. In this work, we conduct extensive observational experiments to explore the adaptability of the GT architecture in node classification tasks and draw several conclusions: the current multi-head self-attention module in GT can be completely replaceable, while the feed-forward neural network module proves to be valuable. Based on this, we decouple the propagation (P) and transformation (T) of GNNs and explore a powerful GT architecture, named GNNFormer, which is based on the P/T combination message passing and adapted for node classification in both homophilous and heterophilous scenarios. Extensive experiments on 12 benchmark datasets demonstrate that our proposed GT architecture can effectively adapt to node classification tasks without being affected by global noise and computational efficiency limitations. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Optically transparent graphene-based cognitive metasurface for adaptive frequency manipulation

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Photonics Research 2023年第1期11卷 129-136页

作者： MINGYANG GENG XIAOLU YANG HAO CHEN XINZHI BO MENGZI LI ZHENGUO LIU WEIBING LU State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Center for Flexible RF Technology Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China

Optically transparent microwave absorbing metasurfaces have shown great potential and are needed in multiple applications environments containing optical windows owing to their ability to reduce backscattering electromagnetic(EM)signals while keeping continuous optical ***,they are also required to have adaptive EM manipulation capability to cope with complex and capricious EM *** a general approach,distributed circuit components,including positive-intrinsic-negative diodes and varactors and sensing components,are integrated with passive absorbing metasurfaces to realize adaptive control of microwave ***,these circuit elements generally require bulky electrical wires and complex control circuits to regulate the operating state,resulting in the absorbing structures being optically ***,it is a great challenge to realize self-operating absorbers while maintaining optical ***,we report an optically transparent cognitive metasurface made of patterned graphene sandwich structures and a radio frequency detector,which can achieve adaptive frequency manipulation to match incident EM *** a proof-of-principle application example,we realize a closed-loop automatic absorber system prototype of the proposed graphene metasurface with self-adaptive frequency variation,without any human *** approach may facilitate other adaptive metadevices in microwave regime with high-level recognition and manipulation and,more generally,promote the development of intelligent stealth technologies.

关键词： transparent microwave absorber

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Influence of Physical Parameters on Echo Spectrum of Underwater Target

Influence of Physical Parameters on Echo Spectrum of Underwa...

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IEEE/OES China Ocean Acoustics (COA)

作者： Yunfei Chen Dapeng Yu Bing Jia Zhenshan Wang Jintao Yong Yanjie Wang School of Information and Communication Engineering Dalian University of Technology Dalian China Science and Technology on Underwater Test and Control Laboratory Dalian China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

The elastic scattering features of the underwater target scattering wave depend on the physical parameters of the target, such as the material and structure. Intuitively evaluating the influence of the elastic scattering wave on the characteristics of the whole scattering wave signal is difficult due to the coupling and superposition of the elastic scattering wave and the geometric scattering wave of the target. Based on the different modulation characteristics of broadband echo spectrum produced by target geometry scattering and elastic scattering wave, this paper characterizes and studies the modulation features of echo spectrum and tests the influence of physical parameters on the echo spectrum of underwater target using several simple cylindrical targets with different physical parameters. Moreover, the geometric scattering wave and the elastic scattering wave of the simple target echo are extracted using the fractional Fourier transform (FrF $T$ ) domain filtering method, and the influence of physical parameters on the echo spectrum is quantitatively analyzed.

关键词： Geometry Couplings Fourier transforms Filtering Oceans Scattering Modulation Feature extraction Acoustics Broadband communication

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