2021 13th International Conference on Communication Software and Networks (ICCSN)最新文献

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Research on a Low-Complexity Multi-channel High-Precision Amplitude and Phase Calibration Algorithm 一种低复杂度多通道高精度幅位校正算法研究
2021 13th International Conference on Communication Software and Networks (ICCSN) Pub Date : 2021-06-04 DOI: 10.1109/ICCSN52437.2021.9463608
Jing Yan, Xixi Lu, X. Li
{"title":"Research on a Low-Complexity Multi-channel High-Precision Amplitude and Phase Calibration Algorithm","authors":"Jing Yan, Xixi Lu, X. Li","doi":"10.1109/ICCSN52437.2021.9463608","DOIUrl":"https://doi.org/10.1109/ICCSN52437.2021.9463608","url":null,"abstract":"In a multi-channel receiver synchronous acquisition system, the difference in the characteristics of the analog devices and synchronization time difference between multiple channels will cause the inconsistency of amplitude and phase, resulting in the performance degradation of multi-channel receivers such as radar, signal reconnaissance, and measurement and control. Therefore, this paper proposes a low-complexity and high-precision amplitude and phase calibration algorithm, which is simple to implement, requires low hardware, and runs fast. Simulation experiments show that the calibration algorithm proposed in this paper has an amplitude accuracy of ±0.015dB and a phase accuracy of ±0.009° under the condition of inconsistent amplitude and phase, and it has also achieved good results in practical engineering applications.","PeriodicalId":263568,"journal":{"name":"2021 13th International Conference on Communication Software and Networks (ICCSN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124887290","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
2-layer Parallel SVM Network Based on Aggregated Local Descriptors for Fingerprint Liveness Detection 基于聚合局部描述符的两层并行SVM网络指纹活力检测
2021 13th International Conference on Communication Software and Networks (ICCSN) Pub Date : 2021-06-04 DOI: 10.1109/ICCSN52437.2021.9463624
Wen Jian, Yujie Zhou, Hongming Liu
{"title":"2-layer Parallel SVM Network Based on Aggregated Local Descriptors for Fingerprint Liveness Detection","authors":"Wen Jian, Yujie Zhou, Hongming Liu","doi":"10.1109/ICCSN52437.2021.9463624","DOIUrl":"https://doi.org/10.1109/ICCSN52437.2021.9463624","url":null,"abstract":"Fingerprint liveness detection is an effective way to ensure the security and reliability of fingerprint recognition algorithms against spoof fingerprint attacks. Local descriptors are one of the most widely studied fingerprint liveness detection algorithms. However, the performance of simplex local descriptors or simple voting models among multiple descriptors still can-not achieve satisfactory accuracy, robustness, and applicability. This paper proposes a 2-layer parallel Support Vector Machine (SVM) network to improve the classification performance of local descriptors and achieve 95.32% accuracy on the LivDet datasets (2009, 2011, 2013, and 2015). The experimental results and theoretical analysis indicate that the proposed 2-layer parallel SVM network based on aggregated local descriptors shows better detection accuracy and model robustness against adversarial attacks compared with simplex descriptors and state-of-the-art neural network structures. Besides, the 2-layer parallel SVM network can save training time through parallel computing, and achieve extremely high accuracy and reliability through ultra-high-dimensional descriptor classification.","PeriodicalId":263568,"journal":{"name":"2021 13th International Conference on Communication Software and Networks (ICCSN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470787","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
A Preprocessing Method of Facial Expression Image under Different Illumination 不同光照下面部表情图像的预处理方法
2021 13th International Conference on Communication Software and Networks (ICCSN) Pub Date : 2021-06-04 DOI: 10.1109/ICCSN52437.2021.9463605
Yiyun Hu, Xiaoping Zeng, Zhiyong Huang, Xiong Dong
{"title":"A Preprocessing Method of Facial Expression Image under Different Illumination","authors":"Yiyun Hu, Xiaoping Zeng, Zhiyong Huang, Xiong Dong","doi":"10.1109/ICCSN52437.2021.9463605","DOIUrl":"https://doi.org/10.1109/ICCSN52437.2021.9463605","url":null,"abstract":"In this work, we propose an image processing method which combines the limited contrast adaptive histogram equalization (CLAHE) with Gamma transform to solve the illumination problem in facial expression recognition. We apply this algorithm to professional illumination datasets (Extended Yale B) and get better visual results, compared with using CLAHE and Gamma correction separately. Moreover, we use a convolution neural network (CNN) that pre-trained on FER2013 datasets to evaluate the effect of this method in facial expression recognition. We use this preprocessing algorithm to enhance the CK+ and Oulu expression datasets, and get accuracy of 89.24% and 70.24% respectively. Compared with the datasets that have not been pre-processed, it has provided an increase in classification accuracy of 7% on the Oulu datasets.","PeriodicalId":263568,"journal":{"name":"2021 13th International Conference on Communication Software and Networks (ICCSN)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954351","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
MobileNet for Differential Constellation Trace Figure 差分星座迹图的MobileNet
2021 13th International Conference on Communication Software and Networks (ICCSN) Pub Date : 2021-06-04 DOI: 10.1109/ICCSN52437.2021.9463655
X. Ran, Tianfeng Yan, Teli Cai
{"title":"MobileNet for Differential Constellation Trace Figure","authors":"X. Ran, Tianfeng Yan, Teli Cai","doi":"10.1109/ICCSN52437.2021.9463655","DOIUrl":"https://doi.org/10.1109/ICCSN52437.2021.9463655","url":null,"abstract":"Radio frequency fingerprint technology is of great significance to the security of the Internet of Things system. When the signal uses I/Q modulation, the demodulated signal can be drawn on a two-dimensional plane, that is, constellation diagram. Slight differences in the manufacture and use of transmitters can cause the constellation to shift. Differential Constellation Trace Figure (DCTF) with device characteristics can be generated by special differential processing of demodulation signal. In this paper, the DCTFs of QPSK signal are used as radio frequency fingerprints. Radio frequency fingerprints are classified using MobileNet, a lightweight network that can be used for mobile and embedded devices. In the final simulation experiment, the accuracy of MobileNet V2 and MobileNet V3 are both over 95% when SNR is low. Comparing with the accuracy of CNN, MobileNet V2 and MobileNet V3 are better choices.","PeriodicalId":263568,"journal":{"name":"2021 13th International Conference on Communication Software and Networks (ICCSN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114920711","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
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