The process of learning a new skill can be time consuming and difficult for both the teacher and the student, especially when it comes to computer modeling. With so many terms and functionalities to familiarize oneself with, this task can be overwhelming to even the most knowledgeable student. The purpose of this paper is to describe the methodology used in the creation of a new set of curricula for those attempting to learn how to use the Dynamic Traffic Simulation Package with Multi-Resolution Modeling. The current DLSim curriculum currently relates information via high-concept terms and complicated graphics. The information in this paper aims to provide a streamlined set of curricula for new users of DLSim, including lesson plans and improved infographics.

Machine learning has a near infinite number of applications, of which the potential has yet to have been fully harnessed and realized. This thesis will outline two departments that machine learning can be utilized in, and demonstrate the execution of one methodology in each department. The first department that will be described is self-play in video games, where a neural model will be researched and described that will teach a computer to complete a level of Super Mario World (1990) on its own. The neural model in question was inspired by the academic paper “Evolving Neural Networks through Augmenting Topologies”, which was written by Kenneth O. Stanley and Risto Miikkulainen of University of Texas at Austin. The model that will actually be described is from YouTuber SethBling of the California Institute of Technology. The second department that will be described is cybersecurity, where an algorithm is described from the academic paper “Process Based Volatile Memory Forensics for Ransomware Detection”, written by Asad Arfeen, Muhammad Asim Khan, Obad Zafar, and Usama Ahsan. This algorithm utilizes Python and the Volatility framework to detect malicious software in an infected system.

When creating computer vision applications, it is important to have a clear image of what is represented such that further processing has the best representation of the underlying data. A common factor that impacts image quality is blur, caused either by an intrinsic property of the camera lens or by introducing motion while the camera’s shutter is capturing an image. Possible solutions for reducing the impact of blur include cameras with faster shutter speeds or higher resolutions; however, both of these solutions require utilizing more expensive equipment, which is infeasible for instances where images are already captured. This thesis discusses an iterative solution for deblurring an image using an alternating minimization technique through regularization and PSF reconstruction. The alternating minimizer is then used to deblur a sample image of a pumpkin field to demonstrate its capabilities.