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Compressed Sensing Beyond the I.I.D. and Static Domains: Theory, Algorithms and Applications

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개인저자Kazemipour, Abbas.
단체저자명University of Maryland, College Park. Electrical Engineering.
서명/저자사항Compressed Sensing Beyond the I.I.D. and Static Domains: Theory, Algorithms and Applications.
발행사항[S.l.] : University of Maryland, College Park., 2017
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2017
형태사항260 p.
소장본 주기School code: 0117.
ISBN9780355636147
일반주기 Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Advisers: Min Wu
이용제한사항This item is not available from ProQuest Dissertations & Theses.
요약Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few
요약In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theor
요약Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prov
요약Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few
일반주제명Electrical engineering.
Mathematics.
Statistics.
언어영어
기본자료 저록Dissertation Abstracts International79-07B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14996690

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