Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78370
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dc.contributor.authorOng, Yan Lin
dc.date.accessioned2019-06-19T03:20:08Z
dc.date.available2019-06-19T03:20:08Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/78370
dc.description.abstractThe theory and applications on Compressed Sensing is a promising, quickly developing area which garnered a great amount of interest in the field of engineering, mathematics, analytics and info-communication. CS introduces a skeleton/template which allows for the concurrently execution of recovering and compressing of vectors in a bounded dimension. It deals with the recovery of sparse high-dimensional input signals with a considerably small amount of sample measurements through the execution of some efficient algorithms. Quite a few algorithms have been developed for the purpose of signal reconstruction from compressed measurements, and especially enticing amongst them is greedy pursuit algorithm: Orthogonal Matching Pursuit (OMP). This paper investigates how the performance of OMP changes when the various parameter such as linear dimension n, number of measurements m and sparsity are increased.en_US
dc.format.extent62 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleAsymptotic performance analysis of compressed sensing reconstruction algorithmen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorAnamitra Makuren_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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