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.
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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.
This paper analyzes China's transformative changes over the past four decades through a microeconomic lens focused on enterprises. Market-oriented non-state-owned enterprises have emerged as a pivotal force driving China's economic development within this context. The article investigates the determinants of their development. Notably, more than half of market-oriented non-state-owned enterprises…
This paper analyzes China's transformative changes over the past four decades through a microeconomic lens focused on enterprises. Market-oriented non-state-owned enterprises have emerged as a pivotal force driving China's economic development within this context. The article investigates the determinants of their development. Notably, more than half of market-oriented non-state-owned enterprises have entered the inheritance stage, necessitating the exploration of novel attributes for sustained growth.The study's research scope is defined across four dimensions, with a specific focus on approximately 4,000 market-oriented non-state-owned enterprises. It investigates the driving factors behind sustained performance growth at various stages of these enterprises, emphasizing five variables: "partnership governance, entrepreneurial spirit, development strategy, incentive mechanisms, and innovation capability." Employing a combination of "typical case studies" and "group validation" methods, the research examines the factors influencing sustained growth in these enterprises and their interrelationships. The goal is to construct a model for enterprise succession and development, ultimately offering recommendations to foster sustained growth.
The research paper is structured into an introduction, literature review and theoretical foundation, typical case studies, empirical research on a group, and a conclusion.
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Key findings include: Partnership governance positively impacts partners' entrepreneurial spirit, promoting sustained performance growth. Trajectory-oriented development strategies, effective incentive mechanisms, and leading innovation capabilities have a positive moderating effect on entrepreneurial spirit, fostering sustained performance growth. During the innovation development phase, partnership governance significantly influences entrepreneurial spirit with a noteworthy environmental moderation effect.
The paper recommends implementing a "Dual-Factor Improvement Model" that enhances both partnership governance systems and the selection and functioning mechanisms of entrepreneurial spirit partners. This approach aims to boost partners' entrepreneurial spirit and facilitate high-quality succession in market-oriented non-state-owned enterprises,,ultimately achieving sustained high-quality growth.
In conclusion, this research contributes to a deeper understanding of sustained performance growth in enterprises. It offers valuable insights for the succession and development of market-oriented non-state-owned enterprises and innovation-driven entrepreneurship. This research holds significant value in advancing sustained high-quality development among market-oriented non-state-owned enterprises in China, optimizing resource allocation, and nurturing talented individuals.