A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3440

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DC Field	Value	Language
dc.contributor	Frakt, Austin B. (Austin Berk)	-
dc.contributor	Karl, William Clement.	-
dc.contributor	Willsky, Alan S.	-
dc.contributor	Massachusetts Institute of Technology. Laboratory for Information and Decision Systems.	-
dc.date	2003-04-29T15:42:34Z	-
dc.date	2003-04-29T15:42:34Z	-
dc.date	1996	-
dc.date.accessioned	2013-06-04T16:17:29Z	-
dc.date.available	2013-06-04T16:17:29Z	-
dc.date.issued	2013-06-05	-
dc.identifier	http://hdl.handle.net/1721.1/3440	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Austin B. Frakt, W. Clem Karl, Alan S. Willsky.	-
dc.description	Includes bibliographical references (p. 29-30).	-
dc.description	Supported by Air Force Office of Scientific Research. F49620-95-1-0083, F49620-96-1-0028 (awarded to Boston University) Supported by a National Defense Science and Engineering Graduate Fellowship awarded by the Defense Advanced Research Projects Agency. F49620-93-1-0604	-
dc.format	30 p.	-
dc.format	2233510 bytes	-
dc.format	application/pdf	-
dc.language	eng	-
dc.publisher	Massachusetts Institute of Technology, Laboratory for Information and Decision Systems	-
dc.relation	LIDS-P ; 2369	-
dc.subject	TK7855.M41 E3845 no.2369	-
dc.title	A multiscale hypothesis testing approach to anomaly detection and localization from noisy tomographic data	-
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