This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability

The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
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Description
In this study I investigate the factors that may influence consumer preference and choice in China’s home interior decoration industry. With the fast development of information technology such as the internet in China, it becomes increasingly important to have a more precise understanding of consumer preference and choice in home

In this study I investigate the factors that may influence consumer preference and choice in China’s home interior decoration industry. With the fast development of information technology such as the internet in China, it becomes increasingly important to have a more precise understanding of consumer preference and choice in home interior decoration decisions so that companies in this industry can provide better services to meet customer needs. Using survey data from a sample of potential customers and a sample of existing customers of a large home interior decoration company, I find that (1) internet has become the mostly used channel by consumers to gather information about home interior decoration, (2) design style is the most influential factor in consumers’ choice of home interior decoration company, and (3) consumers are more likely to choose home interior decoration companies to provide full services when they are between 35 to 45 years old or above 55 years old, when it is the first time for them to purchase a real estate property, and when they are located in the Eastern region of China. Findings of this study can help home interior decoration companies better understand customer needs and preferences, facilitate changes in their marketing and sales strategies, and consequently strengthen their competitive advantage.
ContributorsYang, Jin (Author) / Shen, Wei (Thesis advisor) / Zhang, Anmin (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2015