An adaptive optimization of the polynomial wavelet threshold

dc.contributor.advisorAkopian, David
dc.contributor.advisorAgaian, Sos S.
dc.contributor.authorGovinahallisathyanaryana, Sushanth
dc.contributor.committeeMemberJohn, Eugene
dc.date.accessioned2024-02-09T21:57:25Z
dc.date.available2024-02-09T21:57:25Z
dc.date.issued2010
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractIn this thesis parametrically defined polynomial thresholding operators are proposed. Prior work has shown that the optimal choice of the polynomial coefficients can be formulated as a least squares (LS) problem if the training sequences are available. An adaptive LMS approach for the optimization of wavelet coefficients is proposed and studied as an approach to reduce computational costs. This thesis presents a new class of polynomial threshold operators for denoising signals using wavelet transforms. The operators are parameterized to include classical soft- and hard-thresholding operators and have many degrees of freedom to optimally suppress undesired noise and preserve signal details. To avoid the complicated process of signal model identification for specific type of signals, an adaptive least mean squares (LMS) optimization method is proposed for the polynomial coefficients. This approach optimizes coefficients without matrix inversion and if needed allows to optimally adapt the threshold polynomials for different sub bands without relative significant computational overheads. The approach is applied to 1D, 2D and 3D signals, and the results are compared to the conventional methods. High potential of the proposed approach is demonstrated through the simulations.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent78 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781124385259
dc.identifier.urihttps://hdl.handle.net/20.500.12588/3795
dc.languageen
dc.subjectDenoising
dc.subjectLMS algorithm
dc.subjectPolynomial operator
dc.subjectWavelet thresholding
dc.subject.classificationElectrical engineering
dc.titleAn adaptive optimization of the polynomial wavelet threshold
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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