| LDR | | 02606nam u200469 4500 |
| 001 | | 000000422881 |
| 005 | | 20190215170502 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355636147 |
| 035 | |
▼a (MiAaPQ)AAI10640889 |
| 035 | |
▼a (MiAaPQ)umd:18561 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Kazemipour, Abbas.
▼0 (orcid)0000-0003-2641-7894. |
| 245 | 10 |
▼a Compressed Sensing Beyond the I.I.D. and Static Domains: Theory, Algorithms and Applications. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 260 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B. |
| 500 | |
▼a Advisers: Min Wu |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2017. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Mathematics. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0405 |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Electrical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-07B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996690
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |