Computer Engineering Commons^™

Exploration Of Event-Based Camera Data With Spiking Neural Networks, Charles Peter Rizzo

Doctoral Dissertations

Neuromorphic computing is a novel, non-von Neumann computing architecture that employs power efficient spiking neural networks on specialized hardware. Taking inspiration from the human brain, spiking neural networks are temporal computation units that propagate information throughout the network via binary spikes. Compared to conventional artificial neural networks, these networks can be more sparse, smaller in size, and more efficient power-wise when realized on neuromorphic hardware. Event-based cameras are novel vision sensors that capture visual information through a temporal stream of events instead of as a conventional RGB frame. These cameras are low-power collections of pixels that asynchronously emit events over …

Sensor Fusion For Object Detection And Tracking In Autonomous Vehicles, Mohamad Ramin Nabati

Doctoral Dissertations

Autonomous driving vehicles depend on their perception system to understand the environment and identify all static and dynamic obstacles surrounding the vehicle. The perception system in an autonomous vehicle uses the sensory data obtained from different sensor modalities to understand the environment and perform a variety of tasks such as object detection and object tracking. Combining the outputs of different sensors to obtain a more reliable and robust outcome is called sensor fusion. This dissertation studies the problem of sensor fusion for object detection and object tracking in autonomous driving vehicles and explores different approaches for utilizing deep neural networks …

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 …

Data Security And Privacy In Smart Grid, Yue Tong

Doctoral Dissertations

This dissertation explores novel data security and privacy problems in the emerging smart grid.

The need for data security and privacy spans the whole life cycle of the data in the smart grid, across the phases of data acquisition, local processing and archiving, collaborative processing, and finally sharing and archiving. The first two phases happen in the private domains of an individual utility company, where data are collected from the power system and processed at the local facilities. When data are being acquired and processed in the private domain, data security is the most critical concern. The key question is …