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

A Spectrum Sensing Algorithm to Improve Detection Rates in Cognitive Radio Systems 一种提高认知无线电系统检出率的频谱感知算法

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232053

Jiahe Guo, Yi-zheng Guo, Chenjie Zhang, Xuemei Bai

{"title":"A Spectrum Sensing Algorithm to Improve Detection Rates in Cognitive Radio Systems","authors":"Jiahe Guo, Yi-zheng Guo, Chenjie Zhang, Xuemei Bai","doi":"10.1109/ICICSP50920.2020.9232053","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232053","url":null,"abstract":"To solve the problem of low detection rate of primary user signal under low signal-to-noise ratio in wireless channel environment, a spectrum sensing method of cognitive radio network based on quantum particle swarm optimization extreme learning machine (ELM) algorithm is proposed. According to the characteristics of extreme learning machine algorithm, quantum particle swarm optimization (QPSO) is employed to optimize parameters of extreme learning machine, and QPSO-ELM model with structural risk idea is constructed. The model which reduces the empirical risk of the algorithm improves the generalization ability of the model and improves the spectrum sensing performance of the algorithm. Simulation experiments show that compared with the three machine learning algorithms of artificial neural network (ANN), support vector machine (SVM) and extreme learning machine (ELM), the spectrum sensing performance of the algorithm is improved by 16%, 28% and 9% respectively when the signal-to-noise ratio is -15dB. And the simulation experiments proves that the algorithm proposed in this paper has higher performance under the condition of low signal-to-noise ratio and can effectively realize the spectrum sensing of the main user signal.","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":"133326378","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

Rain Attenuation Analysis of Ka Band Ship-borne Satellite Communication Station In Indian Ocean and Pacific Ocean 印度洋和太平洋Ka波段船载卫星通信站降雨衰减分析

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232069

Jing Wang, Linhao Wang, Mingzhong Xu

引用次数: 2

Axisymmetric extended target tracking using Gaussian process 高斯过程轴对称扩展目标跟踪

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232065

Qinlei Li, Liping Song

引用次数: 2

Robust Compressed Sensing based on Correntropy and Smoothly Clipped Absolute Deviation Penalty 基于相关熵和平滑裁剪绝对偏差惩罚的鲁棒压缩感知

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232057

Le Gao, Xifeng Li, Dongjie Bi, Xuan Xie, Yongle Xie

引用次数: 0

Geometry-based Completed Local Binary Pattern for Texture Image Classification 基于几何的完全局部二值模式纹理图像分类

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232056

Wei Wang, Qiqi Kou, Shuaishuai Zhou, Ke Luo, Lifeng Zhang

{"title":"Geometry-based Completed Local Binary Pattern for Texture Image Classification","authors":"Wei Wang, Qiqi Kou, Shuaishuai Zhou, Ke Luo, Lifeng Zhang","doi":"10.1109/ICICSP50920.2020.9232056","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232056","url":null,"abstract":"In view of the fact that the accuracy of texture image classification is easily affected by changes in illumination and rotation, based on the analysis of geometric curvatures information of the image microscopic geometric surface and the completed local binary pattern (CLBP), this paper proposed a new descriptor, named as Geometry-based Completed Local Binary Pattern (GCLBP). Inspired by the continuous rotation invariance and illumination robustness of the geometric curvature information, principal curvatures (PCs) of all pixels are first calculated and then used to represent the gradient magnitude information of the image, which are further exploited to replace the original gradient magnitude information in CLBP. To further improve the accuracy of texture classification, a cross-scale joint coding strategy is exploited to form the final GCLBP. The experimental results on two standard texture databases demonstrate that the GCLBP algorithm proposed in this paper is not only far superior to the original CLBP in terms of classification recognition accuracy and dimensionality of feature vector, but also better than most existing advanced texture classification methods.","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":"129497459","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

Simple Data Augmented Transformer End-To-End Tibetan Speech Recognition 简单数据增强变压器端到端藏文语音识别

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232114

Xiaodong Yang, Wen Wang, Hongwu Yang, Jiaolong Jiang

{"title":"Simple Data Augmented Transformer End-To-End Tibetan Speech Recognition","authors":"Xiaodong Yang, Wen Wang, Hongwu Yang, Jiaolong Jiang","doi":"10.1109/ICICSP50920.2020.9232114","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232114","url":null,"abstract":"The Tibetan language belongs to the Tibetan branch of the Sino-Tibetan language family, and which is the common language of the Tibetan people in China, mainly containing the Ando, U-Tsang and Kham dialects. The Tibetan language has its own phonetic system, grammatical structure, rich vocabulary, and perfect expressive ability. This paper builds a corpus of U-Tsang Tibetan and designs corresponding textual annotations. Based on this corpus, a Transformer network-based method for Tibetan speech recognition is proposed and a Transformer end-to-end speech recognition model for Tibetan is trained. We compared the Transformer-based approach to Tibetan speech recognition with the convolutional neural network (CNN)-based model and introduced a data augmentation algorithm SpecAugment in training. The results show that under the use of 36000 Tibetan utterances corpora with a total length of 53h, transformer model and CNN model respectively obtain 29.3% and 32.6% of WER. After using the data augmentation algorithm SpecAugment, we got 25.8% and 28.1% in Transformer model and CNN model respectively.","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":"121203341","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

Fault Tolerant Model for Redundant System Based on Health Assessment 基于健康评估的冗余系统容错模型

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232043

Tao Wang, Jian Cao, Yingbiao Luo, Wenyu Sun, Ying Zhang, Yuandong Wang

引用次数: 0

An Intra Complexity Reduction Algorithm for Quality Scalable SHVC 一种高质量可扩展SHVC的内部复杂度降低算法

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232091

Yong Li, Fukang Wang

引用次数: 1

Intercomparison between the satellite and Drifter observed sea surface temperature in East/Japan Sea 东/日本海海面温度与卫星观测值的比较

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232104

Shiyao Chen, Haodi Wang, Ning Wang, Peilong Yu, Xiao Yang, Benhong Wang, Yongchui Zhang

引用次数: 1

Evaluation of multi-model current data in the East/Japan Sea 东/日本海多模式洋流资料的评价

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI: 10.1109/ICICSP50920.2020.9232090

Haodi Wang, Shiyao Chen, Ning Wang, Peilong Yu, Xiao Yang, Yang Wang, Yongchui Zhang

{"title":"Evaluation of multi-model current data in the East/Japan Sea","authors":"Haodi Wang, Shiyao Chen, Ning Wang, Peilong Yu, Xiao Yang, Yang Wang, Yongchui Zhang","doi":"10.1109/ICICSP50920.2020.9232090","DOIUrl":"https://doi.org/10.1109/ICICSP50920.2020.9232090","url":null,"abstract":"Eddy resolving numerical model data from ocean forcasting/hindcasting systems are being extensively used to resolve mesoscale variabilities such as mesoscale eddies and fronts. In this paper, quantitative evaluations of four eddy resolving ocean models including HYCOM, OFES, ECCO2 and FRA-JCOPE2 are conducted in the East/Japan Sea. Regions of main circulation systems, including the Tsushima Warm Current (TWC), East Korea Warm Current (EKWC) and Subpolar Front Current (SPFC), are especially concerned. Using the quality-controlled 6-hourly Drifter interpolation data provided by NOAA, the point and field correlation coefficients are respectively calculated to evaluate the correlation between observed data and model data. Results show that the HYCOM forecast data and OFES hindcast data offer a better stimulation of the main current system in the East/Japan Sea, compared with the ECCO2 and FRA-JCOPE2 data, whose point and field averaged coefficients are both over 0.7. The conclusion is supported by the Taylor diagram, which indicates that HYCOM data has the closest distance to Drifter data. A comparation of East Sea Real-time Observation Buoy (ESROB) and EC1 mooring data with model data is undertaken to support the evaluation result by analysing the variation trend of model and observation time series on the fixed locations.","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":"128187798","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: 3