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dc.contributor.advisorGregory W. Wornell.en_US
dc.contributor.authorShulkind, Galen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-09-17T14:52:06Z
dc.date.available2018-09-17T14:52:06Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117846
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 169-178).en_US
dc.description.abstractWith 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.en_US
dc.description.statementofresponsibilityby Gal Shulkind.en_US
dc.format.extent178 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleEfficient data collection strategies for rapid learning in physical environmentsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1052124154en_US


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