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2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)最新文献

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Signal Design and Processing for Underwater Acoustic Positioning and Communication Integrated System 水声定位与通信集成系统信号设计与处理
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232105
Chengcai Lv, Binjian Shen, Chuan Tian, Shengzong Zhang, Liang Yu, Dazhen Xu
{"title":"Signal Design and Processing for Underwater Acoustic Positioning and Communication Integrated System","authors":"Chengcai Lv, Binjian Shen, Chuan Tian, Shengzong Zhang, Liang Yu, Dazhen Xu","doi":"10.1109/ICICSP50920.2020.9232105","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232105","url":null,"abstract":"Autonomous underwater vehicles (AUVs) have been used widely in underwater environments over the last few decades. In underwater environments, positioning and navigation of AUVs are challenging due to the impossibility of radio communications and global navigation satellite system. Therefore, underwater acoustic system has been the main approach adopted to solve these challenges until now. In the traditional method, the underwater acoustic positioning system (UAPS) and underwater acoustic communication system (UACS) are mostly independent, which result in the interference and the waste of energy. In this paper, we combined the UAPS and UACS together and designed the positioning signal based on spread spectrum communication technology. It could not only solve the problem mentioned above, but also improve the positioning accuracy and locate the multiple users at the same time. However, the designed signal would result in great amount of calculation with conventional serial search algorithm for the signal processing. Hence the parallel frequency search algorithm was adopted to solve this problem. Finally, the simulative experiments were implemented and performance of the two algorithms were analyzed. It is demonstrated that the frequency search algorithm could be more effective than the serial search algorithm.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131498595","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: 2
Title Page 标题页
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/icicsp50920.2020.9232067
{"title":"Title Page","authors":"","doi":"10.1109/icicsp50920.2020.9232067","DOIUrl":"https://doi.org/10.1109/icicsp50920.2020.9232067","url":null,"abstract":"","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131027375","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
Anti-Fatigue and Collision Avoidance Systems for Intelligent Vehicles with Ultrasonic and Li-Fi Sensors 采用超声波和Li-Fi传感器的智能车辆抗疲劳和防撞系统
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232054
Yujie Li
{"title":"Anti-Fatigue and Collision Avoidance Systems for Intelligent Vehicles with Ultrasonic and Li-Fi Sensors","authors":"Yujie Li","doi":"10.1109/ICICSP50920.2020.9232054","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232054","url":null,"abstract":"Intelligent vehicles can assist the drivers to improve the safety, comfort, sustainability and efficiency. Intelligent vehicles use emerging technologies in modeling, localization, motion control, and machine learning, which have become a research focus for many worldwide academy and industry institutes. However, there is still much open research challenge in technologies as well as collision detection and obstacles avoidance. In this work, we investigate how to effectively help drivers overcome the fatigue driving and avoid collisions. By leveraging the existing, Ultrasonic Sensor, Infrared (IR) Sensor and Li-Fi, Vehicle-to-Vehicle (V2V) Communication and machine learning technologies, we develop the Anti-Fatigue Decision Tree, Anti-Fatigue Detection and Enhanced Collision Avoidance Systems to accurately evaluate the current situation of the drivers and vehicles, and make timely response. Our prototype testing and analysis show that the proposed techniques are feasible and cost-effective.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328557","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: 2
Real-Time Abnormal Event Detection in the Compressed Domain of CCTV Systems by LDA Model 基于LDA模型的CCTV系统压缩域实时异常事件检测
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232052
A. Diop
{"title":"Real-Time Abnormal Event Detection in the Compressed Domain of CCTV Systems by LDA Model","authors":"A. Diop","doi":"10.1109/ICICSP50920.2020.9232052","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232052","url":null,"abstract":"For storage and displaying, huge amounts of data are produced by the CCTV (Close Circuit TeleVision) systems. The automatic detection of abnormal events in the compressed domain of these systems makes it possible to extract these events to alert and possibly store the sequences in a compressed format to optimize the capacity of storage and transfer of data. This paper describes a solution for a real-time abnormal event detection. The proposed method is based on the LDA model for classifying events in the compressed domain in CCTV systems. Experimental results, demonstrating reliable real-time extractions and storage, shows that the classification of events with the LDA model allows the extraction of abnormal events in the compressed domain at very high compression rate with an accuracy of 95% for two standardized datasets considered.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114348607","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
SimNet: Simplified Deep Neural Networks for OFDM Channel Estimation SimNet: OFDM信道估计的简化深度神经网络
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232124
Yicheng Bao, Zeyu Tan, Haifeng Sun, Zhikang Jiang
{"title":"SimNet: Simplified Deep Neural Networks for OFDM Channel Estimation","authors":"Yicheng Bao, Zeyu Tan, Haifeng Sun, Zhikang Jiang","doi":"10.1109/ICICSP50920.2020.9232124","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232124","url":null,"abstract":"In this paper, a simplified deep neural network is proposed, which can be used for channel estimation and signal detection in OFDM system and reduce complexity. To be specific, the method of deep learning is introduced to optimize the channel estimation module of OFDM system. By building deep neural networks and training parameters at the signal-to-noise ratio of 10dB and 25dB, respectively, the channel estimation results can be optimized at a wider range of signal-to-noise ratio. In addition, the influence of training model size for channel estimation and signal detection is also researched. Compared with some other artificial intelligence aided OFDM receivers, proposed deep neural networks has shorter training time and simpler architecture. The simulation results show that by using proposed deep neural networks and training method in OFDM channel estimation, smaller mean square error and lower bit error rate can be obtained, especially in the case of clipping distortion and wide range of signal-to-noise ratio.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121977381","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: 4
A Scheme for RAID-6 Scaling Based on N-code 基于n码的RAID-6扩展方案
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232077
Yu Hu, Ping Xie, Muyuan Li, Defang Wang, Shengling Geng
{"title":"A Scheme for RAID-6 Scaling Based on N-code","authors":"Yu Hu, Ping Xie, Muyuan Li, Defang Wang, Shengling Geng","doi":"10.1109/ICICSP50920.2020.9232077","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232077","url":null,"abstract":"The arrival of the era of big data means that data has an explosive increment of tend. Storage system is important more and more. Enterprise information puts more demands on storage systems. In order to cope with the situation where the storage capacity may face insufficient storage space. The industry generally adopts the scaling the existing RAID storage system to solve the current storage capacity problem. RAID that is a widely used storage system. Because it has high data reliability. Redundant arrays of independent disks has been good expand. Expanding the original storage capacity has become an effective way. The RAID-6 storage system is used by the industry as a commonly RAID scaling storage system. Because RAID-6 has advantages of higher reliability and more convenient addition of disks. Among them, optimizing the migration process and reducing the amount of data migration are also commonly used scaling approaches. This paper proposes a scaling scheme based on N-Code code for RAID-6 storage system-SNC. Through real experimental data, it can be found that SNC scheme can reduce the amount of data migration to satisfy the requirement of minimal data migration. Compared with the Round-Robin scheme, this scheme reduces the amount of data migration of 76.52-88.94% and shortens the total data migration time of 54.5-62.4%.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122048509","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
A Novel Demodulation Network for Binary Partial Response CPM Signals 一种新的二值部分响应CPM信号解调网络
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232041
Haowei Wu, Qihao Peng, Jiaying Wang, Rui Ma, Jinglan Ou
{"title":"A Novel Demodulation Network for Binary Partial Response CPM Signals","authors":"Haowei Wu, Qihao Peng, Jiaying Wang, Rui Ma, Jinglan Ou","doi":"10.1109/ICICSP50920.2020.9232041","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232041","url":null,"abstract":"Continuous phase modulation (CPM) is a promising modulation scheme, due to its constant envelope and compact spectrum. However, the application of CPM is limited by the demodulation and the strict requirements of synchronization. A novel method based on the convolution neural network (CNN) is proposed for binary partial response CPM signals, where the structure of the neural network is designed according to the traditional demodulation processing. Specifically, the convolution kernels are applied to extract the high-dimensional features, which are different from the branch metrics calculated by the matched filters and phase rotation. And then the extracted features are mapped in the fully-connected layers, which plays the same role as the Viterbi decoder. Besides, the moving step of the convolution kernels is small, so that the extracted features can obtain more information than the branch metrics, even though there are some timing errors. Our numerical evaluations demonstrate that the performance of the proposed method approaches that of the theoretical optimal method. Moreover, the designed network is robust to normalized timing variance with no extra training.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124866313","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
Piecewise Fitting Technology of Group Delay Difference Based on Fourier Decomposition 基于傅里叶分解的群时延差分分段拟合技术
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232113
Fengyi Wang, Pengpeng Li, Weihua Mou, Shuibin Zhong, Shaojie Ni
{"title":"Piecewise Fitting Technology of Group Delay Difference Based on Fourier Decomposition","authors":"Fengyi Wang, Pengpeng Li, Weihua Mou, Shuibin Zhong, Shaojie Ni","doi":"10.1109/ICICSP50920.2020.9232113","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232113","url":null,"abstract":"Pseudo-range measurement based on DSSS system has been widely used in satellite navigation, radar, aerospace measurement and control, deep space exploration and other fields. The group delay and amplitude characteristics of the signal channel of the high-precision navigation receiver are mainly determined by the RF front-end, and the group delay characteristics have a great influence on the high-precision navigation and positioning system. In order to explore the influence of group delay characteristics of RF front-end on pseudo-range measurement, it is necessary to fit the group delay characteristics with high precision. According to the fitting requirements, this paper proposes the piecewise fitting technology of group delay difference based on Fourier decomposition, which has high fitting accuracy, fast calculating speed, and estimation performance of receiver correlation peak distortion, which has a good fitting performance of group delay characteristics.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043979","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
24-point game based on BP neural network : A smart game designed for children 基于BP神经网络的24分游戏:专为儿童设计的智能游戏
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232048
Wu Jing, Mo Qiao, Chen Xiaowen, Yan Jihong
{"title":"24-point game based on BP neural network : A smart game designed for children","authors":"Wu Jing, Mo Qiao, Chen Xiaowen, Yan Jihong","doi":"10.1109/ICICSP50920.2020.9232048","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232048","url":null,"abstract":"This paper introduces a 24-point game based on BP (Back Propagation) neural network. This game enables two children to play together and have a contest, which will help the children to strengthen their number recognizing and mathematics ability. The training set consists of 2340 28*28 pixels black-and-white images. Hough transform and morphology are used to preprocess, then we segment the image and extract the feature. We construct a BP neural network by MATLAB neural network toolbox. Input the image processed by the front end into the network to recognize the characters of the poker card. After recognizing the 4 numbers, all the right answers to the 24-point game will be calculated and shown to the players by the system. At the same time, the system can count the time the players spend/cost and give a feedback.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129821444","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
An Optical-to-SAR Transformation Method for SAR Ship Image Augmentation 舰船SAR图像增强的光学-SAR变换方法
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232097
Ke-Jian Li, Shenshen Luan, Dong Zhou
{"title":"An Optical-to-SAR Transformation Method for SAR Ship Image Augmentation","authors":"Ke-Jian Li, Shenshen Luan, Dong Zhou","doi":"10.1109/ICICSP50920.2020.9232097","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232097","url":null,"abstract":"For ship detection in remote sensing, the labeled synthetic aperture radar (SAR) ship images are far less than optical ship images, which limited the development of SAR ship detection and classification. In this paper, an optical-to-SAR transformation method is proposed to transforming the well labeled optical ship images to SAR ship images. Based on electromagnetic theory, the backscattering and reflection mechanism is analyzed and modeled into the transformation. A background iteration method based on probability theory is also proposed to learn the distribution of SAR ship images. The result shows that the proposed method can transform the optical ship images to SAR ship images with the confidence coefficient of 84.643%.","PeriodicalId":117760,"journal":{"name":"2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748218","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
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