Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Mathematical Science


Khan, Taufiquar

Committee Member

Brannan , James R

Committee Member

Lee , Hyesuk


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 s&delta(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 auto-correlation 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.