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020 ▼a 9780438098220
035 ▼a (MiAaPQ)AAI10901881
035 ▼a (MiAaPQ)OhioLINK:osu1502970194073045
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 621.3
1001 ▼a Whipps, Gene Thomas.
24510 ▼a Contributions to Distributed Detection and Estimation over Sensor Networks.
260 ▼a [S.l.] : ▼b The Ohio State University., ▼c 2017
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2017
300 ▼a 138 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Randolph Moses
5021 ▼a Thesis (Ph.D.)--The Ohio State University, 2017.
520 ▼a Wireless sensor networks have matured over the last several years from popular research and development platforms to commercially-available sensors and systems. In many applications, wireless sensor networks have size, weight, power, and cost li
520 ▼a First, we consider the problem of distributed detection from a large network of sensors and introduce a realistic network model. Sensor nodes make individual decisions from their local observation and then communicate these decisions through a s
520 ▼a Second, we study the problem of distributed quickest change detection from a network of sensors. Similar to the first part, sensor nodes communicate information to a central decision node, but in this part the central node continues to collect i
520 ▼a Finally, we address decentralized learning of an object appearance manifold. Sensor nodes observe an object from different aspects and then, in a decentralized manner, learn a joint statistical model for that object. We employ a mixture of facto
590 ▼a School code: 0168.
650 4 ▼a Electrical engineering.
650 4 ▼a Statistics.
690 ▼a 0544
690 ▼a 0463
71020 ▼a The Ohio State University. ▼b Electrical and Computer Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0168
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000307 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자