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dc.contributor.advisorMahalanobis, Abhijiten_US
dc.contributor.authorSong, Sewoong, 1954-
dc.creatorSong, Sewoong, 1954-en_US
dc.date.accessioned2013-05-16T09:42:54Z
dc.date.available2013-05-16T09:42:54Z
dc.date.issued1990en_US
dc.identifier.urihttp://hdl.handle.net/10150/291831
dc.description.abstractThe theory of structural sub-band decomposition of an FIR filters is extended to adaptive filters. It is shown that this sub-band decomposition is equivalent to the transform of input data by orthogonal matrices, of which the Walsh-Hadamard Transform (WHT) is a special case. Thus, the proposed method is a generalization of the transform domain adaptive filtering (TDAF) using WHT, which is already known to enhance the convergence speed of adaptive filters. Furthermore, our method yields one possible hardware implementation of the fast WHT. The convergence behavior of the proposed sub-band adaptive filters is simulated using the Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms. The results show the faster convergence speed of the proposed adaptive filters compared to conventional adaptive filters.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.titleStructural sub-band decomposition for adaptive filtersen_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
dc.identifier.proquest1342676en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.nameM.S.en_US
dc.identifier.bibrecord.b26592952en_US
refterms.dateFOA2018-08-30T02:52:15Z
html.description.abstractThe theory of structural sub-band decomposition of an FIR filters is extended to adaptive filters. It is shown that this sub-band decomposition is equivalent to the transform of input data by orthogonal matrices, of which the Walsh-Hadamard Transform (WHT) is a special case. Thus, the proposed method is a generalization of the transform domain adaptive filtering (TDAF) using WHT, which is already known to enhance the convergence speed of adaptive filters. Furthermore, our method yields one possible hardware implementation of the fast WHT. The convergence behavior of the proposed sub-band adaptive filters is simulated using the Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms. The results show the faster convergence speed of the proposed adaptive filters compared to conventional adaptive filters.


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