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 Pattichis, Huan Song

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

Integrating sensing and machine learning is important in elevating precision in several Internet of Things (IoT) and mobile applications. In our Electrical Engineering classes, we have begun developing self-contained modules to train students in this area. We focus specifically in developing modules in machine learning including pre-processing, feature extraction and classification. We have also embedded in these modules software to provide hands-on training. In this paper, we describe our efforts to develop an online simulation environment that will support web-based laboratories for training undergraduate students from Electrical Engineering and other disciplines in sensors and machine learning. We also present our efforts to enable students to visualize and understand the inner workings of various machine learning algorithms along with descriptions of their performance with several types of synthetic and sensor data.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
Volume2018-June
StatePublished - Jun 23 2018
Event125th ASEE Annual Conference and Exposition - Salt Lake City, United States
Duration: Jun 23 2018Dec 27 2018

ASJC Scopus subject areas

  • Engineering(all)

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    Dixit, A., Shanthamallu, U. S., Spanias, A., Rao, S., Katoch, S., Banavar, M. K., Muniraju, G., Fan, J., Spanias, P., Strom, A., Pattichis, C., & Song, H. (2018). Multidisciplinary modules on sensors and machine learning. ASEE Annual Conference and Exposition, Conference Proceedings, 2018-June.