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Full-Text Articles in Other Computer Engineering
Improving The Programmability Of Networked Energy Systems, Noman Bashir
Improving The Programmability Of Networked Energy Systems, Noman Bashir
Doctoral Dissertations
Global warming and climate change have underscored the need for designing sustainable energy systems. Sustainable energy systems, e.g., smart grids, green data centers, differ from the traditional systems in significant ways and present unique challenges to system designers and operators. First, intermittent renewable energy resources power these systems, which break the notion of infinite, reliable, and controllable power supply. Second, these systems come in varying sizes, spanning over large geographical regions. The control of these dispersed and diverse systems raises scalability challenges. Third, the performance modeling and fault detection in sustainable energy systems is still an active research area. Finally, …
Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh
Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh
Doctoral Dissertations
Society has benefited from the technological revolution and the tremendous growth in computing powered by Moore's law. However, we are fast approaching the ultimate physical limits in terms of both device sizes and the associated energy dissipation. It is important to characterize these limits in a physically grounded and implementation-agnostic manner, in order to capture the fundamental energy dissipation costs associated with performing computing operations with classical information in nano-scale quantum systems. It is also necessary to identify and understand the effect of quantum in-distinguishability, noise, and device variability on these dissipation limits. Identifying these parameters is crucial to designing …
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Masters Theses
Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.
State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to …