A student project for AHSE1500: Foundations of Business and Entrepreneurship (taught in Spring 2006) featuring an Olin College themed tradable card game. It consists of cards of students, professors, locations, and events.
This record contains the Final Report for the project and scanned images of all the cards in the game.
Electromyography can be used as a human machine interface in which a person could control a computer or device with the electrical signals that cause muscle movement. This research used EMG data from the SEEDS data base to explore what features should be used by machine learning algorithms to accurately classify EMG data into which motion a subject is preforming. We extracted features from the EMG data and then ran three different feature selection algorithms to find which features were the most useful in classification. In the end, we found eight features that our various selection algorithms selected the most and concluded that those features would be good starting place when trying to classify EMG data.
The Rockwell Automation SCOPE team worked to provide an out-of-box quality control sensor for automation applications. Quality control sensors need to provide fast inspection capabilities for factories to ensure continuous quality of products. The team also looked into business opportunities for the sensor in line with Rockwell Automation’s industrial customer base. The team optimized the current sensor and made improvements. They also explored market segments where the sensor could make a significant impact on a factory’s quality control and automation processes.