TY - JOUR
T1 - Multidisciplinary modules on sensors and machine learning
AU - Dixit, Abhinav
AU - Shanthamallu, Uday Shankar
AU - Spanias, Andreas
AU - Rao, Sunil
AU - Katoch, Sameeksha
AU - Banavar, Mahesh K.
AU - Muniraju, Gowtham
AU - Fan, Jie
AU - Spanias, Photini
AU - Strom, Andrew
AU - Pattichis, Constantinos
AU - Song, Huan
N1 - Funding Information:
Acknowledgement: This research is funded in part by the NSF IUSE program.
Publisher Copyright:
© American Society for Engineering Education, 2018.
PY - 2018/6/23
Y1 - 2018/6/23
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:85051189859
SN - 2153-5965
VL - 2018-June
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
T2 - 125th ASEE Annual Conference and Exposition
Y2 - 23 June 2018 through 27 December 2018
ER -