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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Committee:
Dr. Mark Davenport, ECE, Chair , Advisor
Dr. Chris Rozell, ECE
Dr. Justin Romberg, ECE
Dr. Matthieu Bloch, ECE
Dr. Yao Xie, ISyE
Abstract:
The purpose of this work is to explore the capability of sensing systems to acquire information adaptively when they are subject to practical measurement constraints. By leveraging problem structure such as sparsity and probabilistic data models, intelligent sampling schemes have the potential to enable higher quality estimation with less sensing effort in diverse applications such as imaging, recommendation systems, information retrieval, and psychometric studies. Existing approaches to adaptive sensing are often limited in practice as they require the ability to take arbitrary measurements while in realistic situations, measurements must taken according to various limitations. Two representative constrained scenarios are considered: linear settings in which measurement rows are chosen from a fixed collection and where estimation may be performed only via sequentially chosen paired comparisons. Theoretical and empirical evidence are provided to suggest that adaptivity can result in substantial improvements in these constrained settings.
