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Compressive sensing.

Booth, Joshua Dennis.

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Compressive sensing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Compressive sensing./
Author:	Booth, Joshua Dennis.
Description:	65 p.
Notes:	Source: Masters Abstracts International, Volume: 49-02, page: 1212.
Contained By:	Masters Abstracts International49-02.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1483097
ISBN:	9781124344942

Compressive sensing.
Booth, Joshua Dennis.

Compressive sensing. - 65 p.

Source: Masters Abstracts International, Volume: 49-02, page: 1212.

Thesis (M.S.)--Duquesne University, 2010.

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.

ISBN: 9781124344942Subjects--Topical Terms:

1669109
Applied Mathematics.

Compressive sensing.
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020 $a 9781124344942
035 $a (UMI)AAI1483097
035 $a AAI1483097
040 $a UMI $c UMI
100 1 $a Booth, Joshua Dennis. $3 1680499
245 1 0 $a Compressive sensing.
300 $a 65 p.
500 $a Source: Masters Abstracts International, Volume: 49-02, page: 1212.
500 $a Adviser: Carl Toews.
502 $a Thesis (M.S.)--Duquesne University, 2010.
520 $a 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.
590 $a School code: 0067.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Statistics. $3 517247
650 4 $a Computer Science. $3 626642
690 $a 0364
690 $a 0463
690 $a 0984
710 2 $a Duquesne University. $b Computational Mathematics. $3 1671744
773 0 $t Masters Abstracts International $g 49-02.
790 1 0 $a Toews, Carl, $e advisor
790 1 0 $a Simon, Donald $e committee member
790 1 0 $a Wimmer, Karl $e committee member
790 1 0 $a Jackson, Jeffrey $e committee member
790 $a 0067
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1483097