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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Acceleration method for target HRRP generation based on DDPM

Acceleration method for target HRRP generation based on DDPM

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xitai Shen Qiang Zhou Bingqian Yu Xin Zhang Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity.

关键词： Training Target recognition Noise reduction radar radar scattering Data collection Feature extraction Sampling methods Diffusion models Vectors

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High-Resolution Synthetic Aperture Imaging Algorithm for Terahertz 3

High-Resolution Synthetic Aperture Imaging Algorithm for Ter...

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3rd International Conference on Defence Technology, ICDT 2022

作者： Wang, Guanwen Liu, Zhaoyang School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The reconstruction and recovery of targets in the terahertz band has always been a direction for the improvement of synthetic aperture radar imaging algorithms. In order to improve the imaging resolution of the target, the scattering principle of electromagnetic waves to the target is analyzed. Through model establishment and formula derivation, the propagation process between the electromagnetic wave and the target is obtained, and the simulation and experiment verify that the corresponding algorithm can greatly improve the accuracy of target recovery. © Published under licence by IOP Publishing Ltd.

关键词： Electromagnetic waves

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Multi-Terrain radar HRRP Target Detection Using Contrastive Learning

Multi-Terrain Radar HRRP Target Detection Using Contrastive ...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xinyang Wang Mingchen Yuan Jiaqi Liu Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As radar resolution improves, high-resolution radars have significantly enhanced target detection performance through the generation of high-resolution range profiles (HRRPs). However, when the range resolution is much smaller than the target, the target’s energy is dispersed across multiple range cells, leading to poor detection performance with traditional energy-based detection algorithms. Furthermore, existing detection algorithms are highly sensitive to clutter characteristics and exhibit poor adaptability to varying scenarios. To address these issues, this paper proposes a Constant False Alarm Rate (CFAR) HRRP detection algorithm based on contrastive learning for multiple terrains. The algorithm improves the contrastive learning loss function to achieve well-clustered representations of clutter samples during training. Test samples are evaluated by calculating the Mahalanobis distance between them and the training samples, and compared against detection thresholds to accomplish target detection. Testing results show that the proposed algorithm achieves superior detection performance across multiple terrains. Furthermore, to reach a 90% detection probability, the proposed method requires a signal-to-clutter ratio (SCR) reduction of 1 to 4 dB compared to the competing methods.

关键词： Training Energy resolution radar detection Object detection Contrastive learning radar Detection algorithms Clutter signal to noise ratio Testing

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Prototype-Guided Representation Distribution Optimization for Change Detection in High-Resolution Remote Sensing Imagery

Prototype-Guided Representation Distribution Optimization fo...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Guoqing Wang He Chen Xin Wei Junqing Shi Liang Chen Wenchao Liu National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Change detection is the process of automatically extracting change areas from bi-temporal remote sensing images. The key of deep learning-based change detection methods is to extract features that represent changes/non-changes. Most of the existing methods only enhance the representation ability of the changed regions through the model architecture design, but lack further constraints in the representation space. This paper introduces a prototype-guided representation distribution optimization method. By comparing pixel features with prototypes, confusing pixels can be determined, and these pixels are adaptively weighted during learning, so that similar representations are closer and different representations are farther apart. In addition, the representation distribution is also applied as transferred knowledge to knowledge distillation for lightweight CD model. Experimental results on two public datasets show the effectiveness of the proposed method.

关键词： Adaptation models Accuracy Space technology Optimization methods Prototypes Feature extraction Data processing Data models Remote sensing

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Global information Edge Augmentation Network for Remote Sensing Image Continuous Super-Resolution

Global Information Edge Augmentation Network for Remote Sens...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Shize Gao Yong Lei Jingya Zhang Guoqing Wang Wenchao Liu National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques.

关键词： Image edge detection Aggregates Superresolution Semantics Coherence Feature extraction Sensors Remote sensing Image reconstruction Optimization

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A lightweight object detection network based on YOLOv5 for SAR image

A lightweight object detection network based on YOLOv5 for S...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Aijia Shen Liangbo Zhao Fanyun Xu Guoqing Wang Wenchao Liu Zimeng Shen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military defense. When performing remote sensing image object detection on airborne and spaceborne platforms, the vast amount of remote sensing data processing and limited computational resources impose high real-time requirements on the object detection algorithms. This paper designs a lightweight object detection network model named YOLOv5-tiny, based on the existing deep learning network detection model YOLOv5s, and deploys it on the Jetson TX2 development board for training and testing. Experimental results show that the proposed YOLOv5-tiny model, when tested on the SAR-AIRcraft-1.0 with an input image size of 640x640, is 10 times smaller than YOLOv5s; it has a computational cost of 5.2 GFLOPs, which is 1/5 of YOLOv5s, and the processing time for a single image is reduced to half that of YOLOv5s, with only a 0.1% decrease in accuracy.

关键词： YOLO Deep learning Accuracy Computational modeling Atmospheric modeling Data processing radar polarimetry Remote sensing Load modeling Testing

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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Spatial Representation and Motion Attention Fusion Network for Few-shot Action Recognition

Spatial Representation and Motion Attention Fusion Network f...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Jiabao Wang Baogui Qi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recently advancements in few-shot action recognition technology have been notable. However, certain challenges persist. One issue is that it is difficult to effectively model motion information, which results in subpar performance on datasets that emphasize motion itself. Another issue is the neglect of the integration of global background information and local detail information, leading to a lack of attention to appearance information in the models. To address these challenges, we propose Spatial Representation and Motion Attention (SRMA) fusion network. By constructing a dual-branch structure comprising Motion Attention Enrichment Module (MAEM) and Spatial Attention Enrichment Module (SAEM), the network simultaneously captures and models motion information while also focusing on the global and detailed information of the motion space. Furthermore, we integrate the cross-entropy loss from both branches as the loss function for the entire model. Experimental results demonstrate that our method outperforms most other approaches across multiple datasets.

关键词： Training Accuracy Focusing Data processing Spatial databases Robustness Data models

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Resource Allocation for Extended Target Tracking in Isac Application

SSRN

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SSRN 2024年

作者： Jiao, Hao Yan, Junkun Pu, Wenqiang Gao, Chang Wang, Penghui Liu, Hongwei National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China Shenzhen Research Institute of Big Data Shenzhen518172 China Xidian University China

The integrated sensing and communication system has the capability to integrate sensing and communication information, thereby enhancing the performance of wireless communication systems. However, in scenarios with a large number and extended targets, improving the performance of perception becomes a challenging problem. To address these problems, a strategy for extended target resource allocation is proposed in this paper. Specifically, we analyze the probability distribution characteristics of extended target measurement, derive the Cramer–Rao lower bound of target extended shape parameter measurement, and construct a resource allocation scheme based on quality of service (QoS-RA) framework. The QoS-RA scheme can be divided into two situations: sufficient resources and insufficient resources. In the case of insufficient resources, the QoS-RA problem becomes non-convex. To deal with this problem, we design an efficient three-step solution technique that incorporates problem splitting and iterative search. Simulation results demonstrate that the proposed QoS-RA scheme can save resources, greatly increase the target capacity, and improve the resource utilization efficiency of the base station. © 2024, The Authors. All rights reserved.

关键词： Quality of service

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