The “Lunatic Fringe” -- Barry Goldwater and the Conservative Revolution of the 1960s --

How did conservatives, who had become effectively ostracized by their party following the Great Depression and the societal reforms of the New Deal, regain leverage within the GOP during the 1960s? My hypothesis is two-fold. First, I contend that a small group of conservative activists led by F. Clifton White, in spite of a dearth of resources and manpower, managed to infiltrate Republican infrastructure and “hijack” the delegate- selection process. The distinctly conservative and recalcitrant disposition of the Goldwater delegates demonstrates that these activists succeeded. Second, I argue that in addition to temporarily overpowering the national convention in 1964, conservatives thereafter retained control of the party insofar as subsequent GOP candidates were obliged to garner the support of conservative pockets of the country in order to win the presidential nomination. The resulting rightward shift of the Republican Party following the 1960s is a direct corollary of the conservative takeover outlined in this study.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.