{"title":"An automatic approach to blindly detect bandwidth edges based on the wavelet transform","authors":"R. Hatoum, A. Ghaith, G. Pujolle","doi":"10.1109/RELABIRA.2012.6235094","DOIUrl":null,"url":null,"abstract":"Edges Detection is a significant task in many fields of application. Some signal features can be reflected by their discontinuous structure. In general, a mathematical signal transform carries more features than a raw signal. Thus, for analyzing an intercepted signal in a totally blind environment, one can benefit from the information the signal spectrum offers through the Fourier Transform. After sensing the spectrum, the characteristics extraction method must be applied. In this paper, the Wavelet Transform is used, as it exhibits excellent ability to analyze a signal. Also introduced in this paper is an improved algorithm that automatically identifies bandwidth signal boundaries in a determined frequency range. The proposed algorithm is based on the local maxima of the Wavelet Transform modulus. In blind conditions and in a noisy environment, this approach grants good performance.","PeriodicalId":180400,"journal":{"name":"2012 Symposium on Broadband Networks and Fast Internet (RELABIRA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Symposium on Broadband Networks and Fast Internet (RELABIRA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RELABIRA.2012.6235094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Edges Detection is a significant task in many fields of application. Some signal features can be reflected by their discontinuous structure. In general, a mathematical signal transform carries more features than a raw signal. Thus, for analyzing an intercepted signal in a totally blind environment, one can benefit from the information the signal spectrum offers through the Fourier Transform. After sensing the spectrum, the characteristics extraction method must be applied. In this paper, the Wavelet Transform is used, as it exhibits excellent ability to analyze a signal. Also introduced in this paper is an improved algorithm that automatically identifies bandwidth signal boundaries in a determined frequency range. The proposed algorithm is based on the local maxima of the Wavelet Transform modulus. In blind conditions and in a noisy environment, this approach grants good performance.