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dc.contributor.advisorPeter, Adrian M.
dc.contributor.authorMijatovic, Nenad
dc.date.accessioned2019-11-05T15:07:17Z
dc.date.available2019-11-05T15:07:17Z
dc.date.created2019-05
dc.date.issued2019-05
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/11141/2982
dc.description(Ph.D.) - Florida Institute of Technology, 2019.en_US
dc.description.abstractData collection and analysis, performed close to the source and transferred to other devices for different analysis, are the major paradigms of the Internet of Things (IoT). Usually, the raw data comes in the form of a time-series sequence that can be considered as functions, and as such can be examined by the functional analysis apparatus. Among others, the two major tasks in data analysis are (1) categorical signal classification and (2) change detection in signal statistical parameters. Here, we study both problems: featureless signal classification using discriminative interpolation regularized with the ℓ1 norm is performed using Classification by Discriminative Interpolation with Sparsity (CDIS), and the non-parametric density– difference estimation within a wavelet expansion framework for change detection is suggested using Wavelet–based Least Squares Density–Difference (WLSDD). Finally, we propose a novel method for estimating the density–difference between two distributions, called Regularized Wavelet–based Density–Difference (RWDD).en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.titleWavelet-based Functional Data Analysis for Classication and Change Point Detectionen_US
dc.typeDissertationen_US
dc.date.updated2019-07-01T15:22:57Z
thesis.degree.nameDoctorate of Philosophy in Computer Engineering and Sciencesen_US
thesis.degree.levelDoctoralen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.departmentComputer Engineering and Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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