2026-09-18T22:49:57Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/166132022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Alan V. Oppenheim. Sestok, Charles K. (Charles Kasimer) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2005-05-17T14:40:20Z 2005-05-17T14:40:20Z 2003 2004 http://hdl.handle.net/1721.1/16613 55673235 Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004. Includes bibliographical references (p. 119-123). This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Traditionally, statistical signal processing algorithms are developed from probabilistic models for data. The design of the algorithms and their ultimate performance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or prohibitively costly. A potential technique to cope with such situations is data selection, where a subset of the measurements that can be collected and processed in a cost-effective manner is used as input to the signal processing algorithm. Careful evaluation of the selection procedure is important, since the probabilistic description of distinct data subsets can vary significantly. An algorithm designed for the probabilistic description of a poorly chosen data subset can lose much of the potential performance available to a well-chosen subset. This thesis considers algorithms for data selection combined with binary hypothesis testing. We develop models for data selection in several cases, considering both random and deterministic approaches. Our considerations are divided into two classes depending upon the amount of information available about the competing hypotheses. In the first class, the target signal is precisely known, and data selection is done deterministically. In the second class, the target signal belongs to a large class of random signals, selection is performed randomly, and semi-parametric detectors are developed. by Charles K. Sestok, IV. Ph.D. 123 p. 734239 bytes 741965 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Data selection in binary hypothesis testing Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e406a8b3-9d85-4c0d-b5a6-d99bacc452e4"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Data selection in binary hypothesis testing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2004</PublicationDate> <Authors> <Author> <DisplayName>Sestok, Charles K. (Charles Kasimer)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Traditionally, statistical signal processing algorithms are developed from probabilistic models for data. The design of the algorithms and their ultimate performance depend upon these assumed models. In certain situations, collecting or processing all available measurements may be inefficient or prohibitively costly. A potential technique to cope with such situations is data selection, where a subset of the measurements that can be collected and processed in a cost-effective manner is used as input to the signal processing algorithm. Careful evaluation of the selection procedure is important, since the probabilistic description of distinct data subsets can vary significantly. An algorithm designed for the probabilistic description of a poorly chosen data subset can lose much of the potential performance available to a well-chosen subset. This thesis considers algorithms for data selection combined with binary hypothesis testing. We develop models for data selection in several cases, considering both random and deterministic approaches. Our considerations are divided into two classes depending upon the amount of information available about the competing hypotheses. In the first class, the target signal is precisely known, and data selection is done deterministically. In the second class, the target signal belongs to a large class of random signals, selection is performed randomly, and semi-parametric detectors are developed.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>