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ASU Electronic Theses and Dissertations


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.


In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and …

Contributors
Wang, Kun, Li, Jing, Wu, Teresa, et al.
Created Date
2018

Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational …

Contributors
YEH, HUAI-MING, Yan, Hao, Pan, Rong, et al.
Created Date
2019

With the development of computer and sensing technology, rich datasets have become available in many fields such as health care, manufacturing, transportation, just to name a few. Also, data come from multiple heterogeneous sources or modalities. This is a common phenomenon in health care systems. While multi-modality data fusion is a promising research area, there are several special challenges in health care applications. (1) The integration of biological and statistical model is a big challenge; (2) It is commonplace that data from various modalities is not available for every patient due to cost, accessibility, and other reasons. This results in …

Contributors
Liu, Xiaonan, Li, Jing, Wu, Teresa, et al.
Created Date
2019

Under different environmental conditions, the relationship between the design and operational variables of a system and the system’s performance is likely to vary and is difficult to be described by a single model. The environmental variables (e.g., temperature, humidity) are not controllable while the variables of the system (e.g. heating, cooling) are mostly controllable. This phenomenon has been widely seen in the areas of building energy management, mobile communication networks, and wind energy. To account for the complicated interaction between a system and the multivariate environment under which it operates, a Sparse Partitioned-Regression (SPR) model is proposed, which automatically searches …

Contributors
Ning, Shuluo, Li, Jing, Wu, Teresa, et al.
Created Date
2018