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008181129s2018 ||| | | | eng d
020 ▼a 9780438342545
035 ▼a (MiAaPQ)AAI10839893
035 ▼a (MiAaPQ)cornellgrad:10933
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 620
1001 ▼a Fang, Joyce.
24510 ▼a Online Model-Based Estimation for Automated Optical System Alignment and Phase Retrieval Algorithm.
260 ▼a [S.l.] : ▼b Cornell University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 163 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Dmitry Savransky.
5021 ▼a Thesis (Ph.D.)--Cornell University, 2018.
520 ▼a Online model-based estimation is applied to two major applications in optics: Automated optical component alignment and wavefront reconstruction with simultaneous system parameter estimation. Both applications utilize mechanical perturbation in
520 ▼a The first part of this study proposes a novel automated alignment method which improves efficiency and increases the flexibility of an optical system. Current optical systems with automated alignment capabilities are typically designed to includ
520 ▼a The second part of this study presents a novel algorithm for phase retrieval and optical system parameter estimation. Many wavefront reconstruction techniques estimate the amplitude and phase from multiple intensity measurements. One can generat
520 ▼a We modify and extend the system variable estimation method to serial phase retrieval algorithm. We present the use of iterated extended Kalman filter (IEKF) to estimate the system variables in a multiple-image phase retrieval framework. An itera
590 ▼a School code: 0058.
650 4 ▼a Engineering.
650 4 ▼a Mechanical engineering.
690 ▼a 0537
690 ▼a 0548
71020 ▼a Cornell University. ▼b Mechanical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0058
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999698 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자