Board 132: Multidisciplinary Modules on Sensors and Machine Learning
Abhinav Dixit, Uday Shankar Shanthamallu, Andreas Spanias, Sunil Rao, Sameeksha Katoch, Mahesh K. Banavar, Gowtham Muniraju, Jie Fan, Photini Spanias, Andrew Strom, Constantinos S. Pattichis, Huan Song · 2020
Abstract This paper describes modules and laboratories for training undergraduate students in multiple disciplines in sensors and machine learning. The project is part of an NSF IUSE grant that started in 2015 and describes a variety of sensor systems, their properties, and the process of interpreting signals from these sensors using classification algorithms. The paper starts with a description of feature extraction from sensor data and it provided details on the compaction properties of principal components. We then discuss basic methods for signal classification including the k means and support vector machine algorithms. Education methods and software used in our classes are described along with description of the assessment process. We discuss the delivery of these materials as modules which are customized for use at several levels including: senior high schools classes, undergraduate level, and continuing education short courses for practitioners. Descriptions of exercises, software and delivery methods are discussed in some detail.