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.

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Description
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
The geotechnical community typically relies on recommendations made from numerical simulations. Commercial software exhibits (local) numerical instabilities in layered soils across soil interfaces. This research work investigates unsaturated moisture flow in layered soils and identifies a possible source of numerical instabilities across soil interfaces and potential improvement in numerical schemes

The geotechnical community typically relies on recommendations made from numerical simulations. Commercial software exhibits (local) numerical instabilities in layered soils across soil interfaces. This research work investigates unsaturated moisture flow in layered soils and identifies a possible source of numerical instabilities across soil interfaces and potential improvement in numerical schemes for solving the Richards' equation. The numerical issue at soil interfaces is addressed by a (nonlinear) interface problem. A full analysis of the simplest soil hydraulic model, the Gardner model, identifies the conditions of ill-posedness of the interface problem. Numerical experiments on various (more advanced and practical) soil hydraulic models show that the interface problem can also be ill-posed under certain circumstances. Spurious numerical ponding and/or oscillations around soil interfaces are observed consequently. This work also investigates the impact of different averaging schemes for cell-centered conductivities on the propensity of ill-posedness of the interface problem and concludes that smaller averaging conductivities are more likely to trigger numerical instabilities. In addition, an agent-based stochastic soil model, with hydraulic properties defined at the finite difference cell level, results in a large number of interface problems. This research compares sequences of stochastic realizations in heterogeneous unsaturated soils with the numerical solution using homogenized soil parameters. The mean of stochastic realizations is not identical to the solution obtained from homogenized soil parameters.
ContributorsLiu, Ruowen (Author) / Welfert, Bruno D (Thesis advisor) / Houston, Sandra L. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Ringhofer, Christian (Committee member) / Zapata, Claudia E (Committee member) / Arizona State University (Publisher)
Created2017