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Compressed Sensing Beyond the I.I.D. and Static Domains: Theory, Algorithms and Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Compressed Sensing Beyond the I.I.D. and Static Domains: Theory, Algorithms and Applications. |
| 개인저자 | Kazemipour, Abbas. |
| 단체저자명 | University of Maryland, College Park. Electrical Engineering. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 260 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-07B(E). Dissertation Abstract International |
| ISBN | 9780355636147 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2017. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
