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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/12795

Title: Using a Discriminator to Improve Compressive Sensing Efficiency
Authors: Hencke, Kevin
Advisors: Benedetto, John
Department/Program: Applied Mathematics and Scientific Computation
Type: Thesis
Sponsors: Digital Repository at the University of Maryland
University of Maryland (College Park, Md.)
Subjects: Applied mathematics
Computer science
Mathematics
Keywords: compression
Compressive sensing
fault detection
Fourier analysis
Principal Component Analysis
Spectral analysis
Issue Date: 2012
Abstract: Our work defines, implements, and evaluates a modification to a spectrum-based compression scheme for data streams coming from jet aircraft health-monitoring sensors. The modification consists of the addition of a discriminator which separates data streams into similar classes. We create and justify a simulation of a jet sensor network as a source for data streams. The data streams are compressed and decompressed under the new compression scheme and also under two old ones, and the reconstructions are evaluated for quality. The discriminator-based modification to the existing compression algorithm is found to yield better quality than the other two compression algorithms, at the cost of increased runtime.
URI: http://hdl.handle.net/1903/12795
Appears in Collections:UMD Theses and Dissertations
Computer Science Theses and Dissertations
Mathematics Theses and Dissertations

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