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Model-Based Deep Learning For Computational Imaging, Xiaojian Xu
Model-Based Deep Learning For Computational Imaging, Xiaojian Xu
McKelvey School of Engineering Theses & Dissertations
This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.
The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …
Multi-Device Data Analysis For Fault Localization In Electrical Distribution Grids, Jacob D L Hunte
Multi-Device Data Analysis For Fault Localization In Electrical Distribution Grids, Jacob D L Hunte
Electronic Thesis and Dissertation Repository
The work presented in this dissertation represents work which addresses some of the main challenges of fault localization methods in electrical distribution grids. The methods developed largely assume access to sophisticated data sources that may not be available and that any data sets recorded by devices are synchronized. These issues have created a barrier to the adoption of many solutions by industry. The goal of the research presented in this dissertation is to address these challenges through the development of three elements. These elements are a synchronization protocol, a fault localization technique, and a sensor placement algorithm.
The synchronization protocol …