2020 15th IEEE International Conference on Signal Processing (ICSP)最新文献_第10页

Cloud Processing versus Independent Processing of Independent Data Sets for Distributed Detection 云处理与分布式检测中独立数据集的独立处理

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9320933

Xingchuan Liu, Jiang Zhu, Chunyi Song, Zhiwei Xu

{"title":"Cloud Processing versus Independent Processing of Independent Data Sets for Distributed Detection","authors":"Xingchuan Liu, Jiang Zhu, Chunyi Song, Zhiwei Xu","doi":"10.1109/ICSP48669.2020.9320933","DOIUrl":"https://doi.org/10.1109/ICSP48669.2020.9320933","url":null,"abstract":"A distributed detection problem where sensors are deployed to observe a common source of interest is studied. For centralized processing, decision is made by utilizing all the data collected at the sensors, which takes more resources of transmission and computation. For independent processing, it takes less resource at the cost of some performance loss. Motivated by the recently proposed cloud radio access network (C-RAN) and cloud radar, this paper proposes the cloud processing, where each sensor directly compresses (quantizes) its data and the central processor makes decision through all the compressed data. To model the quantization effects, the additive quantization noise model (AQNM) is adopted. Then, the performances of the generalized likelihood ratio test (GLRT), independent GLRT (IGLRT) and cloud GLRT (CGLRT) through deflection coefficients are analyzed. We especially focus on the performance comparison of cloud processing and independent processing, which depends on the number of sensors M, the variances of the additive quantization noise $sigma _{text{q}}^2$ and the additive noise σ2. Numerical results are conducted to verify the analysis.","PeriodicalId":237073,"journal":{"name":"2020 15th IEEE International Conference on Signal Processing (ICSP)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180414","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

The Impact of Element Spatial Arrangement on Ultrasound Tomography: Experimental Results 元件空间排列对超声断层扫描的影响:实验结果

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9321090

Yu Yuan, Yue Zhao, Yi Shen, Yang Xiao, Yuemin M. Zhu, Jing Jin, Naizhang Feng

引用次数: 1

Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks 基于联邦学习的毫米波车辆网络主动切换

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9320974

Kaiqiang Qi, Tingting Liu, Chenyang Yang

引用次数: 9

Non-Invasive Measure of Stenosis Severity Through Spectral Analysis 通过光谱分析无创测量狭窄严重程度

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9320947

C. Byers, Alexandra M. Sinson, Colette M. Scheffers, T. Ning

引用次数: 0

Aircraft Target Classification Based on Multifractal Features of Aircraft Echoes under Suppression of Airframe Section 机身截面抑制下基于飞机回波多重分形特征的飞机目标分类

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9320973

Qiusheng Li, Junyong Hu

{"title":"Aircraft Target Classification Based on Multifractal Features of Aircraft Echoes under Suppression of Airframe Section","authors":"Qiusheng Li, Junyong Hu","doi":"10.1109/ICSP48669.2020.9320973","DOIUrl":"https://doi.org/10.1109/ICSP48669.2020.9320973","url":null,"abstract":"As a kind of complicated targets, the nonrigid vibration of aircraft, their attitude change, and the rotation of their rotating parts will induce complicated nonlinear modulation on their echoes from low-resolution radars. If one performs the multifractal analysis of measures on an aircraft echo, it may offer a refined description of the echo nonlinear modulation characteristics. However, the airframe translational components that occupy the main body of the echo and have little effect on target classification and recognition will have a very adverse effect on the extraction of such features. In the case of Doppler compensation for the echo, the notch algorithm is proposed to suppress the airframe translational section. On this basis, the multifractal measure analysis method is used to analyze and extract the echo characteristics, and the target classification experiments of various types of aircraft echoes are carried out based on the proposed multifractal features combined with support vector machine (SVM). The experimental results show that the echo multifractal features under suppression of airframe section can be used to classify different types of aircraft targets effectively, and the correct classification rate has been significantly improved compared with the features extracted without airframe section compensation.","PeriodicalId":237073,"journal":{"name":"2020 15th IEEE International Conference on Signal Processing (ICSP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116741834","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

Towards a multi-scale virtual heart model 迈向多尺度虚拟心脏模型

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9321058

Zexian Wang, F. Varray, P. Clarysse, I. Magnin

引用次数: 0

Rectangle Template Matching Based on Gradient-Chromatic Illumination-Invariant Images 基于渐变色照度不变图像的矩形模板匹配

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9321052

Guoquan Li, Lingyun Wen, Zhengwen Huang, Ruiyang Xia, Huiqian Wang

引用次数: 0

A Dual-microphone Sub-band Post-filter Using Simplified TBRR for Speech Enhancement 一种用于语音增强的简化TBRR双麦克风子带后滤波器

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9320979

Haiping Wang, Yi Zhou, Yongbao Ma, Hongqing Liu

引用次数: 0

Hierarchical Multi-Classification for Sensor-based Badminton Activity Recognition 基于传感器的羽毛球运动识别分层多分类

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9320935

Ya Wang, Jinwen Ma, Xiangcheng Li, Albert Zhong

{"title":"Hierarchical Multi-Classification for Sensor-based Badminton Activity Recognition","authors":"Ya Wang, Jinwen Ma, Xiangcheng Li, Albert Zhong","doi":"10.1109/ICSP48669.2020.9320935","DOIUrl":"https://doi.org/10.1109/ICSP48669.2020.9320935","url":null,"abstract":"Fast development of sensor technology makes sensor equipments more and more smart and wearable. It further boost the need of sensor-based human activity recognition. Due to the lack of large-scale labeled datasets in practical AI applications, it is important to utilize prior information of the categories in sensor-based human activity recognition. In this paper, we propose a Hierarchical Multi-Classificaion (HMC) framework for sensor-based badminton activity recognition with the help of the prior information of badminton activity categories. Specifically, the multi-class sensor-based badminton activity recognition task is performed in two steps: (1). Any input data for a badminton activity are classified into one of the major classes which are based on their characteristic features; (2). They are further classified into one of the specific categories of badminton activity as required. It is demonstrated by the experimental results on BSS-V2 dataset that our proposed method can get up to 83.9% badminton activity recognition accuracy which is 1.7% better than previous state-of-the-art models.","PeriodicalId":237073,"journal":{"name":"2020 15th IEEE International Conference on Signal Processing (ICSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114358635","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: 5

Biological Sample Imaging by Ptychography with Laterally 1-D Scanning 生物样本横向1-D扫描的平面摄影成像

2020 15th IEEE International Conference on Signal Processing (ICSP) Pub Date : 2020-12-06 DOI: 10.1109/ICSP48669.2020.9321074

Xiu Wen, Jiubin Tan, Zhengjun Liu

引用次数: 0