2022 7th International Conference on Communication, Image and Signal Processing (CCISP)最新文献_第8页

A-CGAN based transformation from ISAR to optical image 基于A-CGAN的ISAR图像到光学图像的转换

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974485

Qinwen Tan, Xiangyuan Li, Zhen Liu, Shuowei Liu, Qinmu Shen

{"title":"A-CGAN based transformation from ISAR to optical image","authors":"Qinwen Tan, Xiangyuan Li, Zhen Liu, Shuowei Liu, Qinmu Shen","doi":"10.1109/CCISP55629.2022.9974485","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974485","url":null,"abstract":"Inverse synthetic aperture radar (ISAR) image offers geometric and structural characteristics information of target objects. Thus, It is an important research topic to recognize radar targets based on ISAR images. ISAR imaging offers the advantages of all-day, all-weather, and ultra-long- distance imaging; however, ISAR image quality is affected by attitude angle, defocusing noise, resolution and other factors, resulting in inferior recognition performance. In contrast, optical images require more stringent imaging conditions, but they provide more feature diversity, resulting in a better recognition effect. Combining the advantages of ISAR images and optical images, the transformation from target ISAR images to optical images greatly improves the target recognition performance. In this study, an ISAR-to-optical image generation method was developed. Combined with two attention mechanisms and the SSIM loss function, a conditional generative adversarial network was constructed to transform ISAR images into optical images so that the generative model can realistically restore the details of the target images. In addition, a comparative test was conducted on a simulated aircraft target, and the performance of the proposed architecture was evaluated in terms of visual effects and quantitative indicators. The results show that the proposed method yields better generation effect. Furthermore, the target recognition case shows that the recognition rate obtained using the generated optical images is considerably higher than that obtained using the original ISAR images, further verifying the effectiveness of the generated image for target recognition.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"214 4 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":"121202685","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

Intelligent Access Control System 智能门禁系统

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974224

Miaolan Zhou, Nuo Chen, Yang Yang, Chenlu Pan, Xiangjie Zhou

引用次数: 0

Enhanced Threshold-based Segmentation for Maize Plantation 基于阈值的玉米种植区分割方法的改进

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974289

Joel M. Gumiran, Arnel F. Fajardo, Ruji P. Medina

{"title":"Enhanced Threshold-based Segmentation for Maize Plantation","authors":"Joel M. Gumiran, Arnel F. Fajardo, Ruji P. Medina","doi":"10.1109/CCISP55629.2022.9974289","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974289","url":null,"abstract":"Phenotyping, mainly plant’ health monitoring, is labor-and time-intensive, particularly for large-scale operations like maize plantations. Therefore, this research used a drone equipped with an RGB image to photograph the whole plantation quickly. On the other hand, RGB photographs do not categorize plants and weeds due to high brightness, shadows, and overlapped foliage. Therefore, several segmentation algorithms are used to solve various challenges. For instance, threshold-based segmentation can only accept progressive illumination, which is crucial for outdoor lighting, simplicity, and distinguishing objects with identical hues. For this kind of segmentation, however, intense light requires modification. Consequently, threshold-based segmentation was improved to normalize the disturbances above while rapidly separating leaves from weeds. In this manner, the Enhanced threshold-based segmentation had applied to RGB images of maize plantations like cornfields with distractions seen in the gathered photos with a segmentation accuracy of 92.41%. In comparison, the threshold-based segmentation had used in the same dataset without normalizing the picture's luminance, with a segmentation accuracy of 5.71%. Thus, the enhanced segmentation method improved segmentation accuracy by 86.7% compared to threshold-based segmentation, which is limited to extreme light conditions. Thus, the incorporated normalization in the segmentation process significantly increases the segmentation accuracy.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"35 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":"123471235","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

Research on Algorithm of Near-field 3D SAR Imaging Based on Linear Array 基于线阵的近场三维SAR成像算法研究

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974318

Ruoshi Zhang, Zhe Zheng, Xuewei Chen, Boning Sun

引用次数: 0

Identification Technology of RKE System Using Multi-dimension RF Fingerprints 基于多维射频指纹的RKE系统识别技术

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974501

Weizun Wang, Jie Huang, A. Hu, Mengjia Ding, Jiabao Yu

{"title":"Identification Technology of RKE System Using Multi-dimension RF Fingerprints","authors":"Weizun Wang, Jie Huang, A. Hu, Mengjia Ding, Jiabao Yu","doi":"10.1109/CCISP55629.2022.9974501","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974501","url":null,"abstract":"Recently, remote keyless entry system (RKE system) has been gradually replacing traditional way to unlock car doors for convenience. However, it has been shown that RKE system is vulnerable to cyber attacks including relay attack, amplification attack and cryptographic attack. In order to solve this dilemma, RF fingerprints method was applied to identify car key fobs in this paper. Power spectrum of preamble signal envelope was proposed to extract features while carrier frequency offset and least mean square-based adaptive filter were also used as auxiliary ones. Multi-dimension RF fingerprints were presented in this paper based on three features mentioned above to increase identification accuracy. Support vector machine(SVM) was chosen with 10-fold cross-validation to train classifier model. Corresponding to current research on keyless entry car theft, the classification results in this paper show that signals from various key fobs can be classified with 99.3% accuracy when using Rf fingerprints extracted from multiple features, with false acceptance rate (FAR) of 0.7% and false rejection rate (FRR) of 0.7% under Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) classifier.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"85 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":"123391187","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

GAN-Enabled Robust Backdoor Attack for UAV Recognition 基于gan的无人机识别鲁棒后门攻击

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974216

Ming Xu, Yuhang Wu, Hao Zhang, Lu Yuan, Yiyao Wan, Fuhui Zhou, Qihui Wu

引用次数: 0

Recent advances in the application of imaging techniques in the estimation of living age 影像技术在估计生活年龄方面的最新进展

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974378

C. Ding

引用次数: 0

A Shared Waveform Design for Integrated Detection and Jamming Signal Based on LFM-Costas Intra-pulse Frequency Stepping 基于LFM-Costas脉冲内频率步进的集成检测与干扰信号共享波形设计

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974268

Chunyang Li, Guang-xin Wu, Gui Li

引用次数: 0

Muscle synergy analysis of lower limb based on Mechanomyography 基于肌力图的下肢肌肉协同分析

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974422

Hanyang Zhang, Gangsheng Cao, Tongtong Zhao, Chunming Xia

引用次数: 0

A gain enhanced low SAR dual-band MIMO antenna integrated with AMC for wearable ISM applications 增益增强低SAR双频MIMO天线集成AMC可穿戴ISM应用

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974290

Lingru Pei, Cheng-zhu Du, Chengxin Shi, HuanChen Peng

引用次数: 0