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- W2894909558 abstract "With the ubiquity of intelligent systems capable ofsensing, inferring and acting upon their surroundings, it becomescritical to learn rapidly about unknown systems or environments.However, obtaining empirical data is often costly and involvessetting up time consuming experiments or deploying specializedsensors. We are interested in deriving scalable algorithms andsystem architectures that facilitate efficient data collection,maximizing inference quality under limited resource budget. In thiswork, we consider efficient data collection strategies in severalapplications involving physical environments. We study the problemof learning dynamical systems with initial approximated models,where we prescribe methods for choosing near optimal experimentalparameters to collect empirical data. We study the problem ofantenna array topology design where we prescribe configurationsallowing efficient scene inference under various measurementschemes and budget constraints. We introduce a novel nonlinearradar modality and discuss efficient design techniques for thissetting. Finally, we introduce a novel methodology for opticalimaging of non line of sight hidden scenes by utilizing occlusionsand investigate how to achieve efficient illumination of the scenefor fast hidden target interrogation." @default.
- W2894909558 created "2018-10-12" @default.
- W2894909558 creator A5078872457 @default.
- W2894909558 date "2018-01-01" @default.
- W2894909558 modified "2023-09-26" @default.
- W2894909558 title "Efficient data collection strategies for rapid learning in physical environments" @default.
- W2894909558 hasPublicationYear "2018" @default.
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