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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.


With the growing importance of underground power systems and the need for greater reliability of the power supply, cable monitoring and accurate fault location detection has become an increasingly important issue. The presence of inherent random fluctuations in power system signals can be used to extract valuable information about the condition of system equipment. One such component is the power cable, which is the primary focus of this research. This thesis investigates a unique methodology that allows online monitoring of an underground power cable. The methodology analyzes conventional power signals in the frequency domain to monitor the condition of a …

Contributors
Govindarajan, Sudarshan, Holbert, Keith E., Heydt, Gerald, et al.
Created Date
2016

Recent new experiments showed that wide-field imaging at millimeter scale is capable of recording hundreds of neurons in behaving mice brain. Monitoring hundreds of individual neurons at a high frame rate provides a promising tool for discovering spatiotemporal features of large neural networks. However, processing the massive data sets is impossible without automated procedures. Thus, this thesis aims at developing a new tool to automatically segment and track individual neuron cells. The new method used in this study employs two major ideas including feature extraction based on power spectral density of single neuron temporal activity and clustering tree to separate …

Contributors
Wu, Ruofan, Si, Jennie, Sadleir, Rosalind, et al.
Created Date
2016