Compressive Sensing

This work is an expository overview of certain key elements in the area of compressive sensing. As a sub-discipline of signal processing, compressive sensing is concerned with both sampling and reconstruction techniques. In this expository, sampling will center on random matrices and expander graphs, while reconstruction will use multiple numerical optimization techniques. Although theoretical performance bounds for these techniques can be found scattered throughout the published literature, there are few practical rules for concrete problems. This thesis helps fill this gap by experimenting on the asymptotic bounds of the number of measurements needed to guarantee perfect reconstruction. These numerical experiments help to identify specific sensing regimes in which performance begin to break down.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2010

Author dc:creator

• Booth, Joshua

Contributors dc:contributor

• Carl Toews
• Donald Simon
• Karl Wimmer
• Jeffrey Jackson

• compressive sensing
• expander graphs
• random matrix
• signal processing
• sparse signal

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/340

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1353