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.
{"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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974224
Miaolan Zhou, Nuo Chen, Yang Yang, Chenlu Pan, Xiangjie Zhou
It is an intelligent face recognition access control system based on OpenMV, Arduino, RC522, Esp8266 WiFi module and Ali Cloud Internet of Things. It provides users with three ways to enter their homes, including face recognition, NFC recognition and micro program wechat lock. Face recognition uses OpenMV and Arduino to accurately recognize faces with the help of LBP eigenvalues. RF module uses RC522, with access card can achieve the purpose of entering the residence, to provide convenient access for visitors. In the part of Internet of Things and wechat Applet, Esp8266 WiFi module and Ali Cloud Internet of Things are used to build the whole Internet of Things system, so as to build we chat small program to realize the function of opening the door, realize the information exchange between users and electromagnetic lock, and ensure the non-contact unlock under the epidemic situation. Three ways to enter the residence give users free choice and provide a greater degree of convenience.
{"title":"Intelligent Access Control System","authors":"Miaolan Zhou, Nuo Chen, Yang Yang, Chenlu Pan, Xiangjie Zhou","doi":"10.1109/CCISP55629.2022.9974224","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974224","url":null,"abstract":"It is an intelligent face recognition access control system based on OpenMV, Arduino, RC522, Esp8266 WiFi module and Ali Cloud Internet of Things. It provides users with three ways to enter their homes, including face recognition, NFC recognition and micro program wechat lock. Face recognition uses OpenMV and Arduino to accurately recognize faces with the help of LBP eigenvalues. RF module uses RC522, with access card can achieve the purpose of entering the residence, to provide convenient access for visitors. In the part of Internet of Things and wechat Applet, Esp8266 WiFi module and Ali Cloud Internet of Things are used to build the whole Internet of Things system, so as to build we chat small program to realize the function of opening the door, realize the information exchange between users and electromagnetic lock, and ensure the non-contact unlock under the epidemic situation. Three ways to enter the residence give users free choice and provide a greater degree of convenience.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"14 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":"123371744","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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974289
Joel M. Gumiran, Arnel F. Fajardo, Ruji P. Medina
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.
{"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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974318
Ruoshi Zhang, Zhe Zheng, Xuewei Chen, Boning Sun
Collecting data in the near-field area of the radar using the spotlight mode based on the linear array platform, and the three-dimensional imaging algorithm of the target within the beam irradiation range is studied. The difficulty of near-field 3D imaging lies in the large amount of radar echo data, so it has high requirements on the efficiency of imaging algorithms. The application of traditional 3D back-projection algorithm is general, but the algorithm efficiency is low. Therefore, an imaging algorithm for 3D reconstruction of echo data based on the compressed sensing is proposed, Therefore, an imaging algorithm for 3D reconstruction of echo data based on the principle of compressed sensing is proposed, and it improves the elevation resolution compared with the traditional algorithm. The simulation results show that the proposed algorithm is feasible and effective, and it takes a short time to achieve 3D imaging of the target in the scene.
{"title":"Research on Algorithm of Near-field 3D SAR Imaging Based on Linear Array","authors":"Ruoshi Zhang, Zhe Zheng, Xuewei Chen, Boning Sun","doi":"10.1109/CCISP55629.2022.9974318","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974318","url":null,"abstract":"Collecting data in the near-field area of the radar using the spotlight mode based on the linear array platform, and the three-dimensional imaging algorithm of the target within the beam irradiation range is studied. The difficulty of near-field 3D imaging lies in the large amount of radar echo data, so it has high requirements on the efficiency of imaging algorithms. The application of traditional 3D back-projection algorithm is general, but the algorithm efficiency is low. Therefore, an imaging algorithm for 3D reconstruction of echo data based on the compressed sensing is proposed, Therefore, an imaging algorithm for 3D reconstruction of echo data based on the principle of compressed sensing is proposed, and it improves the elevation resolution compared with the traditional algorithm. The simulation results show that the proposed algorithm is feasible and effective, and it takes a short time to achieve 3D imaging of the target in the scene.","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":"123673822","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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974501
Weizun Wang, Jie Huang, A. Hu, Mengjia Ding, Jiabao Yu
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.
{"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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974216
Ming Xu, Yuhang Wu, Hao Zhang, Lu Yuan, Yiyao Wan, Fuhui Zhou, Qihui Wu
Unmanned aerial vehicle (UAV) recognition is of crucial importance due to the blowout amount of UAVs and their threats on the public safety. Although many UAV recognition methods based on deep learning (DL) have been proposed by utilizing the radio frequency fingerprints and have achieved appreciable results, their vulnerability to adversarial attacks, especially backdoor attacks, has not been studied. In this pa-per, in order to reveal the serious threat for DL-based UAV recognition encountered with backdoor attacks, a novel robust generative adversarial network (GAN)-enabled backdoor attack scheme is proposed. Moreover, the proposed GAN-based trigger generator not only emerges exceptional attack effectiveness, but also performs well in terms of attack stealthiness and migration ability. Simulation results obtained with the real collected UAV recognition dataset demonstrate that our proposed scheme outperforms the benchmark BadNets backdoor attack.
{"title":"GAN-Enabled Robust Backdoor Attack for UAV Recognition","authors":"Ming Xu, Yuhang Wu, Hao Zhang, Lu Yuan, Yiyao Wan, Fuhui Zhou, Qihui Wu","doi":"10.1109/CCISP55629.2022.9974216","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974216","url":null,"abstract":"Unmanned aerial vehicle (UAV) recognition is of crucial importance due to the blowout amount of UAVs and their threats on the public safety. Although many UAV recognition methods based on deep learning (DL) have been proposed by utilizing the radio frequency fingerprints and have achieved appreciable results, their vulnerability to adversarial attacks, especially backdoor attacks, has not been studied. In this pa-per, in order to reveal the serious threat for DL-based UAV recognition encountered with backdoor attacks, a novel robust generative adversarial network (GAN)-enabled backdoor attack scheme is proposed. Moreover, the proposed GAN-based trigger generator not only emerges exceptional attack effectiveness, but also performs well in terms of attack stealthiness and migration ability. Simulation results obtained with the real collected UAV recognition dataset demonstrate that our proposed scheme outperforms the benchmark BadNets backdoor attack.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"44 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":"125102764","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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974378
C. Ding
Living age inference plays an important role in the study of court science related cases, especially in adolescence and early adulthood. It is more and more important to infer the age of the living body by using the imaging characteristics of human teeth and bones in judicial practice. Through the analysis and summary of the latest application of image examination technology in the estimation of living age, the analysis methods and common indicators involved in different examination methods are summarized, and the advantages, disadvantages and development trends of relevant examinations are summarized, so as to provide research ideas for the estimation of living age.
{"title":"Recent advances in the application of imaging techniques in the estimation of living age","authors":"C. Ding","doi":"10.1109/CCISP55629.2022.9974378","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974378","url":null,"abstract":"Living age inference plays an important role in the study of court science related cases, especially in adolescence and early adulthood. It is more and more important to infer the age of the living body by using the imaging characteristics of human teeth and bones in judicial practice. Through the analysis and summary of the latest application of image examination technology in the estimation of living age, the analysis methods and common indicators involved in different examination methods are summarized, and the advantages, disadvantages and development trends of relevant examinations are summarized, so as to provide research ideas for the estimation of living age.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"12 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":"116671164","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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974268
Chunyang Li, Guang-xin Wu, Gui Li
The integrated detection and jamming signal, which can realize radar detection and jamming functions at the same time, is becoming more and more important today when multifunctional integrated RF systems are of great interest. In this paper, we propose an integrated signal with Costas coding of the intra-pulse frequency of LFM signal. The integrated signal has an ideal ambiguity function of pin type and is capable of causing varying degrees of coherent jamming to the enemy's LFM signal. According to the principle of time-domain matched filtering, we deduce the false target group form of this signal at enemy. Then the jamming performance of this signal is deduced and analyzed according to the difference of Costas sequences. Finally, the theoretical derivation and analysis of the jamming effect of this signal are verified by the simulation results.
{"title":"A Shared Waveform Design for Integrated Detection and Jamming Signal Based on LFM-Costas Intra-pulse Frequency Stepping","authors":"Chunyang Li, Guang-xin Wu, Gui Li","doi":"10.1109/CCISP55629.2022.9974268","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974268","url":null,"abstract":"The integrated detection and jamming signal, which can realize radar detection and jamming functions at the same time, is becoming more and more important today when multifunctional integrated RF systems are of great interest. In this paper, we propose an integrated signal with Costas coding of the intra-pulse frequency of LFM signal. The integrated signal has an ideal ambiguity function of pin type and is capable of causing varying degrees of coherent jamming to the enemy's LFM signal. According to the principle of time-domain matched filtering, we deduce the false target group form of this signal at enemy. Then the jamming performance of this signal is deduced and analyzed according to the difference of Costas sequences. Finally, the theoretical derivation and analysis of the jamming effect of this signal are verified by the simulation results.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"58 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":"121831214","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}
Analysis of muscle synergy during the execution of different motions can provide a physiological basis for the rehabilitation assessment of stroke patients. Mechanomyography (MMG) signal is a kind of low-frequency signal produced during muscle vibration, has been widely applied to pattern recognition and muscle fatigue estimation. In this paper, muscle synergy was extracted from 5 channels of MMG signals recorded from 8 healthy subjects in the lower limbs using the non-negative matrix factorization (NNMF) algorithm. In addition, the similarities of muscle activation patterns of 4 different motions were analyzed, and a suitable activation threshold was selected by comparing synergistic and non-synergistic muscles through the coherence analysis method. This study provides a scientific basis for studying muscle synergy based on MMG signals.
{"title":"Muscle synergy analysis of lower limb based on Mechanomyography","authors":"Hanyang Zhang, Gangsheng Cao, Tongtong Zhao, Chunming Xia","doi":"10.1109/CCISP55629.2022.9974422","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974422","url":null,"abstract":"Analysis of muscle synergy during the execution of different motions can provide a physiological basis for the rehabilitation assessment of stroke patients. Mechanomyography (MMG) signal is a kind of low-frequency signal produced during muscle vibration, has been widely applied to pattern recognition and muscle fatigue estimation. In this paper, muscle synergy was extracted from 5 channels of MMG signals recorded from 8 healthy subjects in the lower limbs using the non-negative matrix factorization (NNMF) algorithm. In addition, the similarities of muscle activation patterns of 4 different motions were analyzed, and a suitable activation threshold was selected by comparing synergistic and non-synergistic muscles through the coherence analysis method. This study provides a scientific basis for studying muscle synergy based on MMG signals.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"36 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":"133375616","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}
Pub Date : 2022-11-01DOI: 10.1109/CCISP55629.2022.9974290
Lingru Pei, Cheng-zhu Du, Chengxin Shi, HuanChen Peng
A dual-band MIMO antenna is proposed on the basis of artificial magnetic conductors (AMC). The MIMO antenna consists of two water lily shaped printed monopole antennas and an SRR isolation structure, operating at the ISM bands of 2.45GHz and 5.8GHz.A simple, compact double circle-based artificial magnetic conductor (AMC) reflector is introduced to decrease radiation exposure to people as well as increase forward gain. The antenna and the 4x4 AMC array are both printed on flexible substrate Rogers RO3003, thus the antenna system can follow the contours of the human body. According to the simulated results, the proposed antenna exhibits peak gains of 8.03 dBi and 8.43 dBi at 2.45GHz and 5.8GHz respectively. The SAR value of body tissue can be reduced by around 97% while the front-to-back ratio (FBR) is over 24.5dB. Its improved radiation characteristics compared to conventional monopole antennas make it a good candidate for WBAN and ISM applications.
{"title":"A gain enhanced low SAR dual-band MIMO antenna integrated with AMC for wearable ISM applications","authors":"Lingru Pei, Cheng-zhu Du, Chengxin Shi, HuanChen Peng","doi":"10.1109/CCISP55629.2022.9974290","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974290","url":null,"abstract":"A dual-band MIMO antenna is proposed on the basis of artificial magnetic conductors (AMC). The MIMO antenna consists of two water lily shaped printed monopole antennas and an SRR isolation structure, operating at the ISM bands of 2.45GHz and 5.8GHz.A simple, compact double circle-based artificial magnetic conductor (AMC) reflector is introduced to decrease radiation exposure to people as well as increase forward gain. The antenna and the 4x4 AMC array are both printed on flexible substrate Rogers RO3003, thus the antenna system can follow the contours of the human body. According to the simulated results, the proposed antenna exhibits peak gains of 8.03 dBi and 8.43 dBi at 2.45GHz and 5.8GHz respectively. The SAR value of body tissue can be reduced by around 97% while the front-to-back ratio (FBR) is over 24.5dB. Its improved radiation characteristics compared to conventional monopole antennas make it a good candidate for WBAN and ISM applications.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"87 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":"130314925","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：