자료유형 | E-Book |
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개인저자 | Kazemipour, Abbas. |
단체저자명 | University of Maryland, College Park. Electrical Engineering. |
서명/저자사항 | Compressed Sensing Beyond the I.I.D. and Static Domains: Theory, Algorithms and Applications. |
발행사항 | [S.l.] : University of Maryland, College Park., 2017 |
발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2017 |
형태사항 | 260 p. |
소장본 주기 | School code: 0117. |
ISBN | 9780355636147 |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Advisers: Min Wu |
이용제한사항 | This item is not available from ProQuest Dissertations & Theses. |
요약 | Sparsity 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 |
요약 | In 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 |
요약 | Next, 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 |
요약 | Finally, 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 |
일반주제명 | Electrical engineering. Mathematics. Statistics. |
언어 | 영어 |
기본자료 저록 | Dissertation Abstracts International79-07B(E). Dissertation Abstract International |
대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14996690 |
인쇄
No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
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1 | WE00028988 | 621.3 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |