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Gregory W. Wornell. Shulkind, Gal Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-09-17T14:52:06Z 2018-09-17T14:52:06Z 2018 2018 http://hdl.handle.net/1721.1/117846 1052124154 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 169-178). With the ubiquity of intelligent systems capable of sensing, inferring and acting upon their surroundings, it becomes critical to learn rapidly about unknown systems or environments. However, obtaining empirical data is often costly and involves setting up time consuming experiments or deploying specialized sensors. We are interested in deriving scalable algorithms and system architectures that facilitate efficient data collection, maximizing inference quality under limited resource budget. In this work, we consider efficient data collection strategies in several applications involving physical environments. We study the problem of learning dynamical systems with initial approximated models, where we prescribe methods for choosing near optimal experimental parameters to collect empirical data. We study the problem of antenna array topology design where we prescribe configurations allowing efficient scene inference under various measurement schemes and budget constraints. We introduce a novel nonlinear radar modality and discuss efficient design techniques for this setting. Finally, we introduce a novel methodology for optical imaging of non line of sight hidden scenes by utilizing occlusions and investigate how to achieve efficient illumination of the scene for fast hidden target interrogation. by Gal Shulkind. Ph.D. 178 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Efficient data collection strategies for rapid learning in physical environments Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="c0571020-a37b-4aa5-a443-bb2d884162c9"> <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>Efficient data collection strategies for rapid learning in physical environments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Shulkind, Gal</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>With the ubiquity of intelligent systems capable of sensing, inferring and acting upon their surroundings, it becomes critical to learn rapidly about unknown systems or environments. However, obtaining empirical data is often costly and involves setting up time consuming experiments or deploying specialized sensors. We are interested in deriving scalable algorithms and system architectures that facilitate efficient data collection, maximizing inference quality under limited resource budget. In this work, we consider efficient data collection strategies in several applications involving physical environments. We study the problem of learning dynamical systems with initial approximated models, where we prescribe methods for choosing near optimal experimental parameters to collect empirical data. We study the problem of antenna array topology design where we prescribe configurations allowing efficient scene inference under various measurement schemes and budget constraints. We introduce a novel nonlinear radar modality and discuss efficient design techniques for this setting. Finally, we introduce a novel methodology for optical imaging of non line of sight hidden scenes by utilizing occlusions and investigate how to achieve efficient illumination of the scene for fast hidden target interrogation.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>