{"title":"采用正交多项式近似的多分辨率分析","authors":"Rupendra Kumar, P. Sircar","doi":"10.5281/ZENODO.36014","DOIUrl":null,"url":null,"abstract":"Multiresolution decomposition of signals has been conventionally carried out by the wavelet representation. In this paper, the orthogonal polynomial approximation has been employed for multiresolution analysis. It is demonstrated that the proposed technique based on polynomial approximation has certain distinct advantages over the conventional method employing wavelet representation.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multiresolution analysis using orthogonal polynomial approximation\",\"authors\":\"Rupendra Kumar, P. Sircar\",\"doi\":\"10.5281/ZENODO.36014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multiresolution decomposition of signals has been conventionally carried out by the wavelet representation. In this paper, the orthogonal polynomial approximation has been employed for multiresolution analysis. It is demonstrated that the proposed technique based on polynomial approximation has certain distinct advantages over the conventional method employing wavelet representation.\",\"PeriodicalId\":282153,\"journal\":{\"name\":\"1996 8th European Signal Processing Conference (EUSIPCO 1996)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1996 8th European Signal Processing Conference (EUSIPCO 1996)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.36014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.36014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiresolution analysis using orthogonal polynomial approximation
Multiresolution decomposition of signals has been conventionally carried out by the wavelet representation. In this paper, the orthogonal polynomial approximation has been employed for multiresolution analysis. It is demonstrated that the proposed technique based on polynomial approximation has certain distinct advantages over the conventional method employing wavelet representation.
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