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Non-convex Phase Retrieval Algorithms and Performance Analysis
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Non-convex Phase Retrieval Algorithms and Performance Analysis. |
| 개인저자 | Wang, Gang. |
| 단체저자명 | University of Minnesota. Electrical Engineering. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 162 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438031326 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Georgios B. Giannakis. |
| 요약 | High-dimensional signal estimation plays a fundamental role in various science and engineering applications, including optical and medical imaging, wireless communications, and power system monitoring. The ability to devise solution procedures t |
| 요약 | Phase retrieval is approached from a non-convex optimization perspective. To gain statistical and computational efficiency, the magnitude data (instead of the intensities) are fitted based on the least-squares or maximum likelihood criterion, wh |
| 요약 | Sparsity plays a instrumental role in many scientific fields - what has led to the upsurge of research referred to as compressive sampling. In diverse applications, the signal is naturally sparse or admits a sparse representation after some know |
| 일반주제명 | Electrical engineering. Statistics. Computer engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
