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Sparse representation for detection ...

Sieger, Caroline Marguerite.

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Sparse representation for detection of transients using a multi-resolution representation of the auto-correlation of wavelets.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Sparse representation for detection of transients using a multi-resolution representation of the auto-correlation of wavelets./
Author:	Sieger, Caroline Marguerite.
Description:	139 p.
Notes:	Source: Masters Abstracts International, Volume: 48-05, page: 3033.
Contained By:	Masters Abstracts International48-05.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1475572
ISBN:	9781109747881

Sparse representation for detection of transients using a multi-resolution representation of the auto-correlation of wavelets.
Sieger, Caroline Marguerite.

Sparse representation for detection of transients using a multi-resolution representation of the auto-correlation of wavelets. - 139 p.

Source: Masters Abstracts International, Volume: 48-05, page: 3033.

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

This thesis seeks to detect damped sinusoidal transients, specifically capacitor switching transients, buried in noise and to answer the following questions: (1) Can the transient s(t; q) be sparsely represented from sdelta( t) = s(t; q)+epsilon(t) using sparsity methods, where epsilon(t) is white Gaussian noise? (2) Does computing the local auto-correlation of the signal around the transient improve detection? (3) How does the auto-correlation shell representation compare to the wavelet representation? (4) Which basis is "best"? (5) Which method and representation is best? This thesis explores detection schemes based on classical methods and newer sparsity methods. Classical methods considered include reconstruction via wavelets and reconstruction in the novel multi-resolution representation based on the autocorrelation functions of compactly supported wavelets. For simplicity, only four bases are considered: Haar, Daubechies 2, Daubechies 4, and Symlets 2. Sparsity methods include the iterative soft, hard, and combined thresholding algorithms.

ISBN: 9781109747881Subjects--Topical Terms:

1669109
Applied Mathematics.

Sparse representation for detection of transients using a multi-resolution representation of the auto-correlation of wavelets.
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040 $a UMI $c UMI
100 1 $a Sieger, Caroline Marguerite. $3 1669508
245 1 0 $a Sparse representation for detection of transients using a multi-resolution representation of the auto-correlation of wavelets.
300 $a 139 p.
500 $a Source: Masters Abstracts International, Volume: 48-05, page: 3033.
500 $a Adviser: Taufiquar Khan.
502 $a Thesis (M.S.)--Clemson University, 2010.
520 $a This thesis seeks to detect damped sinusoidal transients, specifically capacitor switching transients, buried in noise and to answer the following questions: (1) Can the transient s(t; q) be sparsely represented from sdelta( t) = s(t; q)+epsilon(t) using sparsity methods, where epsilon(t) is white Gaussian noise? (2) Does computing the local auto-correlation of the signal around the transient improve detection? (3) How does the auto-correlation shell representation compare to the wavelet representation? (4) Which basis is "best"? (5) Which method and representation is best? This thesis explores detection schemes based on classical methods and newer sparsity methods. Classical methods considered include reconstruction via wavelets and reconstruction in the novel multi-resolution representation based on the autocorrelation functions of compactly supported wavelets. For simplicity, only four bases are considered: Haar, Daubechies 2, Daubechies 4, and Symlets 2. Sparsity methods include the iterative soft, hard, and combined thresholding algorithms.
590 $a School code: 0050.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0364
690 $a 0544
710 2 $a Clemson University. $b Mathematical Science. $3 1023032
773 0 $t Masters Abstracts International $g 48-05.
790 1 0 $a Khan, Taufiquar, $e advisor
790 1 0 $a Brannan, James R. $e committee member
790 1 0 $a Lee, Hyesuk $e committee member
790 $a 0050
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1475572