Priyanka Pawar, Supriya M. Pharande, P. Wani, A. Patki
{"title":"Implementation of Hurst parameter on DSP processor TMS320C6713 platform using wavelet analysis","authors":"Priyanka Pawar, Supriya M. Pharande, P. Wani, A. Patki","doi":"10.1109/IADCC.2015.7154833","DOIUrl":null,"url":null,"abstract":"Over the last 20 years, analysis, modeling and simulation of network traffic in different networks adopted techniques based on statistics and probability theory. We bring out the limitations of these approaches and implement alternative approach using the long range dependence and self-similarity in the network traffic focused around wavelet analysis method. Hurst (H) parameter estimates amount of self similarity is evaluated using this proposed method. The algorithm is implemented in C programming language. The synthetic self similar traffic with predefined H parameter is used as an input. Real-time implementation of the proposed algorithm using C programming language is implemented in TMS320C6713 processor.","PeriodicalId":123908,"journal":{"name":"2015 IEEE International Advance Computing Conference (IACC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Advance Computing Conference (IACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IADCC.2015.7154833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Over the last 20 years, analysis, modeling and simulation of network traffic in different networks adopted techniques based on statistics and probability theory. We bring out the limitations of these approaches and implement alternative approach using the long range dependence and self-similarity in the network traffic focused around wavelet analysis method. Hurst (H) parameter estimates amount of self similarity is evaluated using this proposed method. The algorithm is implemented in C programming language. The synthetic self similar traffic with predefined H parameter is used as an input. Real-time implementation of the proposed algorithm using C programming language is implemented in TMS320C6713 processor.