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020 ▼a 9780438031326
035 ▼a (MiAaPQ)AAI10786396
035 ▼a (MiAaPQ)umn:19072
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 621.3
1001 ▼a Wang, Gang.
24510 ▼a Non-convex Phase Retrieval Algorithms and Performance Analysis.
260 ▼a [S.l.]: ▼b University of Minnesota., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 162 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Georgios B. Giannakis.
5021 ▼a Thesis (Ph.D.)--University of Minnesota, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0130.
650 4 ▼a Electrical engineering.
650 4 ▼a Statistics.
650 4 ▼a Computer engineering.
690 ▼a 0544
690 ▼a 0463
690 ▼a 0464
71020 ▼a University of Minnesota. ▼b Electrical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0130
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997351 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033