Digital Commons Network^™

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine learning …

Predicting The Complexity Of Locality Patterns In Loop Nests In C Scientific Programs, Nasser M. Alsaedi

Dissertations

On modern computer systems, the performance of an application depends on its locality. Most existing locality measurements performed by compiler static analysis mainly target analyzing regular array references in loop nests. Measurements based on compiler static analysis have limited applicability when the loop bounds are unknown at compile time, when the control flow is dynamic, or when index arrays or pointer operations are used. In addition, compiler static analysis cannot adapt to input change.

Training-based locality analysis predicts the data reuse change across program inputs to provide run-time information. This analysis quantifies the number of unique memory locations accessed between …

Exploring The Dynamics Of Scientific Research, Shilpa Lakhanpal

Dissertations

Scientific research papers present the research endeavors of numerous scientists around the world, and are documented across multitudes of technical conference proceedings, and other such publications. Given the plethora of such research data, if we could automate the extraction of key interesting areas of research, and provide access to this new information, it would make literature searches incredibly easier for researchers. This in turn could be very useful for them in furthering their research agenda. With this goal in mind, we have endeavored to provide such solutions through our research. Specifically, the focus of our research is to design, analyze …

Training Set Density Estimation For Trajectory Predictions Using Artificial Neural Networks, Zachary Reinke

Masters Theses

Demand on earth orbiting surveillance systems in increasing as more equipment is put into orbit. These systems rely on predictive techniques to periodically track objects. The demand on these systems may be reduced if object trajectory data to develop scalable training sets used for training artificial neural networks (ANNs) to predict trajectories of a dynamic system. These methods use multi-variable statistics to analyze data energy content to provide the ANN with low density, feature-rich, training data. The developed techniques have been shown to increase ANN prediction accuracy while reducing the size of the training set when applied to a linear …