2022 7th International Conference on Communication, Image and Signal Processing (CCISP)最新文献_第7页

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974245

Mu Qiao, Ying Lin, Meng Liu, Zhuangzhuang Li, Wenjie Zheng, Yi Yang, Xu Jiang

{"title":"Image Similarity Measurement for The Quality Control of Electricity Substation Inspection","authors":"Mu Qiao, Ying Lin, Meng Liu, Zhuangzhuang Li, Wenjie Zheng, Yi Yang, Xu Jiang","doi":"10.1109/CCISP55629.2022.9974245","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974245","url":null,"abstract":"The routine inspection of an electricity substation helps to detect faults and repair equipment in time, ensuring the substation to work safely. However, due to irregular operations, some inspectors may miss to capture images at certain spots while take multiple similar images at the same spots. In order to make control of the inspection quality, we design an algorithm to find such situation automatically. Specifically, given two images, we design a registration-based method to evaluate the affine transformation between two images, then we evaluate the averaged corner error by comparing the image transformed with respect to the estimated affine transformation to an identical transformation. Finally, we screen out the similar images that are small in the averaged corner error. These images are very likely to be taken at the same inspection spot. We conduct experiments on a dataset collected during one routine inspection of a whole substation. Experimental results show that our method is effective to screen out similar images, helping to build an automatic quality control process of the routine inspection.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115812348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

3D contour imaging based on a Millimeter wave MIMO radar 基于毫米波MIMO雷达的三维轮廓成像

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974203

Wei Li, Dandan Li, Jiahao Jiang, Yang Gao

引用次数: 1

Passive THz Radiometer Scanner Object Detection with Adaptive Filtering 被动太赫兹辐射计扫描器目标检测与自适应滤波

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974473

Bo Wen, Tzu-kao Wang

引用次数: 0

Textile Solid Waste Recognition with Multiple Material Features 多材料特征的纺织固体废物识别

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974371

Yuan Gou, Wei Dong, Lin Gan, Ling He, Wanyu Tang, Jing Zhang

引用次数: 0

Denoising Ultrasonic Echo Signals with S-Transform and Non-negative matrix factorization 基于s变换和非负矩阵分解的超声回波信号去噪

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974449

Ma Hongbao, Kang Yihua, Cai Xiang, Qiu Gongzhe, Cheng Si, Jin Xin

引用次数: 0

Detecting Adversarial Examples Using Rich Residual Models to Improve Data Security in CNN Models 利用富残差模型检测对抗样本以提高CNN模型中的数据安全性

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974244

Kaijun Wu, Bo Tian, Yougang Wen, Xue Wang

{"title":"Detecting Adversarial Examples Using Rich Residual Models to Improve Data Security in CNN Models","authors":"Kaijun Wu, Bo Tian, Yougang Wen, Xue Wang","doi":"10.1109/CCISP55629.2022.9974244","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974244","url":null,"abstract":"The convolution neural network (CNN) is vulnerable to the adversarial attack, because the attack can generate adversarial images to force the CNN to misclassify the original label of the clean image. To defend against the adversarial attack, we propose to detect the adversarial images first and then prevent feeding the adversarial image into the CNN model. In this paper, we employ a steganalysis based method based on rich residual models to detect adversarial images which are generated by the typical attacks including BIM and DEEPFOOL. The rich residual models not only reduce the influences from natural image contents, but also enhance the diversity of the feature. Two typical and complementary methods spatial rich model (SRM) and projected spatial rich model (PSRM) are used to extract the feature, where SRM finely capture the statistical changes on co-occurrence in a small neighborhood, and PSRM remedy the loss information caused by SRM. Experimental results on CIFAR-IO and ImageNet show that the proposed method obtained better performance than existing steganalysis methods for detecting adversarial images generated by BIM and DEEPFOOL attack. The research results are expected to improve the recognition ability of image adversarial samples in the convolutional neural network model, and support the data security of natural image content in image recognition.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125597244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UA V Swarm Scheduling Based on Weighted Multi-Objective Particle Swarm Algorithm 基于加权多目标粒子群算法的UA V群调度

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974352

Guilan Luo, Anqi Cao, Shuailin Wang, Yuan-Xin Zhu

引用次数: 0

Maze Routing: An Information Privacy-aware Secure Routing in Internet of Things for Smart Grid 迷宫路由:智能电网中具有信息隐私意识的物联网安全路由

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974237

Shiying Yao, Xuanyu Yang, Z. Song, Xiaolong Yang, Dengwei Duan, Haizhou Yang

{"title":"Maze Routing: An Information Privacy-aware Secure Routing in Internet of Things for Smart Grid","authors":"Shiying Yao, Xuanyu Yang, Z. Song, Xiaolong Yang, Dengwei Duan, Haizhou Yang","doi":"10.1109/CCISP55629.2022.9974237","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974237","url":null,"abstract":"To ensure the security and reliable transmission of private information in Internet of Things for Smart Grid (for short SG-IoT), we proposed a privacy-aware secure routing method (i.e., Maze Routing) based on a novel directional random routing paradigm, which consists of a direction-determined virtual global routing mode and a physical local routing mode. Firstly, for an in-transit packet at the current node, all k-hop nodes are defined as its k-hop wavefronts, and many x-hop wavefronts within k hops are further gathered as a hyper next-hop set. Then, under the direction-determined virtual global routing mode, the packet randomly selects one of elements from the next-hop set by a priority probability as its global next-hop node according to its privacy awareness and End-to-End Quality of Service (E2E QoS) requirement, so as to build a virtual global routing path with a definite direction determined by the destination of this packet. Moreover, under the physical local routing mode, a local pathlet for any two consecutive global next-hop nodes can be built in an ordinary shortest-first routing scheme, then such many pathlets are wired in sequence to constitute a path in whole from source node to destination. Under several loT application scenarios within smart grid, the simulations results show that the proposed maze routing outperforms the existing shortest-first routing method in terms of privacy protection, E2E QoS and communication overhead.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134570369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Skip-MemGANs: An Ensemble Generative Adversarial Network Based on Skip Connection and Memory Module for Wafer Defect Detection 跳跃式memgan:基于跳跃式连接和存储模块的集成生成对抗网络用于晶圆缺陷检测

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974164

Yang Li, Sanxin Jiang

引用次数: 1

Reconfigurable optical demultiplexer calling framework based on gRPC Design 基于gRPC设计的可重构光复用器调用框架

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974287

Yudong Wang, Zhou Yi

引用次数: 0