Reinforcement learning (RL) is a general method for agents to learn optimal control policies through exploration and experience. Due to its generality, RL can generate novel policies that may not be easily expressed with ...

This dissertation studies saliency and its applications in audio and visual signals. For each portion of the signal, its saliency means the likelihood of attracting bottom-up attention in the perception process. In computer ...

This work explores a scalable data analytics pipeline for real-time attack detection through the use of customized honeypots at the National Center for Supercomputing Applications (NCSA). Attack detection tools are common ...

System-on-Chips (SoCs) constitutes the primary backbone of modern embedded computing devices including many safety-critical applications e.g., autonomous vehicles, health care systems. The presence of any undetected bugs ...

The proliferation of digital image capture devices and the Internet have fueled a tremendous amount of research in the fields of multimedia signal processing and multimedia communication over the last decade. This dissertation ...

Community structure is observed in many real-world networks in fields ranging from social networking to biological networks. Over the last decade many approaches have been proposed to efficiently detect the underlying ...

Demands for data storage and computer memory are growing exponentially. It is thus essential to find a new scalable, energy-efficient memory technology. We have been investigating the smallest and most energy-efficient ...

In order to better understand the perturbation of the physical and electronic structure of graphene monolayers and bilayers due to interactions with the supporting substrate, we have utilized scanning tunneling microscopy ...

This thesis investigates wet chemically synthesized graphene nanoribbons (GNRs) on semiconductor substrates using scanning tunneling microscopy and documents the extensive maintenance required for the ultra-high vacuum ...