MARC보기
LDR02022nmm uu200397 4500
001000000334035
00520240805175016
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438058675
035 ▼a (MiAaPQ)AAI10827612
035 ▼a (MiAaPQ)ucla:16922
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 621.3
1001 ▼a Gunn, Cameron Allan.
24510 ▼a Convex Optimization Methods for System Identification with Applications to Noninvasive Intracranial Pressure Estimation.
260 ▼a [S.l.] : ▼b University of California, Los Angeles., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 135 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Lieven Vandenberghe.
5021 ▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018.
520 ▼a After a traumatic brain injury, it is important for some patients' intracranial pressure (ICP) to be measured while they are in intensive care. However, monitoring ICP first requires an invasive surgical procedure, an impediment that has prompte
520 ▼a Three sets of methods are presented in this dissertation. The first methods mitigate the effect of corruptions in cerebral blood flow velocity signals, which are strong predictors of ICP, but often contain artifacts or sections of missing data.
520 ▼a The methods are solved using proximal algorithms, a family of first-order convex optimization algorithms, which result in computationally tractable formulations.
590 ▼a School code: 0031.
650 4 ▼a Electrical engineering.
690 ▼a 0544
71020 ▼a University of California, Los Angeles. ▼b Electrical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0031
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999049 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자