Beyond the Threshold: Life in the New Russia

Amanda Sprang spent nine months, from September of 1995 to May of 1996, studying at Colby College's program in St. Petersburg, Russia. Through contacts made during previous trips to Russia in middle and high school, Amanda was able to quickly rekindle her old friendships and make new ones with many young Russians from different backgrounds. The following work is a collection of twelve essays about life in the New Russia. The essays are framed by a foreword and an epilogue that help place the entire work in a historical context. Although the theme of each essay emerges from a particular incident, within every story Amanda has addressed numerous topics relating to Russian life in today’s changing society. Her first essay, “Art Klinika," takes place in an avant-garde night club in St. Petersburg, and includes a brief yet impressionable, encounter with three young Russian men. “The Birthday Party” recalls a wild evening at the home of her close friend, showing how the Russians greet special occasions. Both the third and fourth essays take place in Moscow, where Amanda returns to visit old friends. These two essays portray the lives of the new economic elite in comparison with the average citizen, as well as show how young Russians face the new challenges that greet them. "Politics Russian Style" recalls a political rally in St. Petersburg, and attempts to shed light on the wacky political world of an infant democracy. Chapters Six through Ten take place away from the western cities of St. Petersburg and Moscow, as Amanda brings us to the cold, mysterious land of Siberia in the dead of winter. She recounts her five day train ride with a retired, high-powered, Communist party official, her experiences in the provincial city of Irkutsk, and a brief trip to a Buddhist monastery and, later, an excursion to Lake Baikal. Back in St. Petersburg, Chapter Eleven gives a humorous account of a ski trip with several Russian friends. Amanda finishes her work with her final chapter, “The Dacha," which describes a weekend spent at a Russian country home with her friend's family.

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.