Multidisciplinary modules on sensors and machine learning
| Autor: | Dixit, Abhinav, Shanthamallu, Uday Shankar, Spanias, Andreas, Rao, Sunil, Katoch, Sameeksha, Banavar, Mahesh K., Muniraju, Gowtham, Fan, Jie, Spanias, Photini, Strom, Andrew, Pattichis, Constantinos, Song, Huan |
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| Přispěvatelé: | Pattichis, Constantinos [0000-0003-1271-8151] |
| Rok vydání: | 2018 |
| Zdroj: | ASEE Annual Conference and Exposition, Conference Proceedings 125th ASEE Annual Conference and Exposition |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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