Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/184818
Title: Independent Component Analysis Techniques for Blind Source Separation
Researcher: Chandra Shekhar Rai
Guide(s): Yogesh Singh
University: Guru Gobind Singh Indraprastha University
Completed Date: 2002
Abstract: Blind Source Separation (BSS) is a technique for separating signals from unknown sources received at different sensors. It is assumed that every sensor gets a linear mixture of the unknown independent sources. One of the most important approaches for achieving source separation is Independent Component Analysis (ICA). newlineResearch work in BSS gained impetus in the early nineties. Recently, it has found many applications in the areas of speech processing, biomedical signal processing, feature extraction, telecommunications, digital image processing, financial time series analysis etc. Due to its wide-ranging applications, research interest in this field is continuously growing. newlineIn this research work, various ICA techniques have been proposed. Performance of these techniques has been evaluated with various artificially generated and real world signals. Comparison with other techniques indicates effectiveness of the proposed methods. newline
Pagination: 
URI: http://hdl.handle.net/10603/184818
Appears in Departments:University School of Information and Communication Technology

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01 tilte certificate abstract ack.pdfAttached File602.58 kBAdobe PDFView/Open
02 contents publication.pdf987.47 kBAdobe PDFView/Open
03 chapter 1.pdf4.36 MBAdobe PDFView/Open
04 chapter 2.pdf5.11 MBAdobe PDFView/Open
05 chapter 3.pdf3.34 MBAdobe PDFView/Open
06 chapter 4.pdf7.22 MBAdobe PDFView/Open
07 chapter 5.pdf2.3 MBAdobe PDFView/Open
08 chapter 6.pdf1.42 MBAdobe PDFView/Open
09 reference.pdf6.76 MBAdobe PDFView/Open


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