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dc.contributor.authorDixit, Abhinaven
dc.contributor.authorShanthamallu, Uday Shankaren
dc.contributor.authorSpanias, Andreasen
dc.contributor.authorRao, Sunilen
dc.contributor.authorKatoch, Sameekshaen
dc.contributor.authorBanavar, Mahesh K.en
dc.contributor.authorMuniraju, Gowthamen
dc.contributor.authorFan, Jieen
dc.contributor.authorSpanias, Photinien
dc.contributor.authorStrom, Andrewen
dc.contributor.authorPattichis, Constantinosen
dc.contributor.authorSong, Huanen
dc.coverage.spatialSalt Lake City, United Statesen
dc.creatorDixit, Abhinaven
dc.creatorShanthamallu, Uday Shankaren
dc.creatorSpanias, Andreasen
dc.creatorRao, Sunilen
dc.creatorKatoch, Sameekshaen
dc.creatorBanavar, Mahesh K.en
dc.creatorMuniraju, Gowthamen
dc.creatorFan, Jieen
dc.creatorSpanias, Photinien
dc.creatorStrom, Andrewen
dc.creatorPattichis, Constantinosen
dc.creatorSong, Huanen
dc.date.accessioned2021-01-22T10:47:46Z
dc.date.available2021-01-22T10:47:46Z
dc.date.issued2018
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/62421
dc.description.abstractIntegrating 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.en
dc.language.isoEnglish (US)en
dc.sourceASEE Annual Conference and Exposition, Conference Proceedingsen
dc.source125th ASEE Annual Conference and Expositionen
dc.source.urihttps://asu.pure.elsevier.com/en/publications/multidisciplinary-modules-on-sensors-and-machine-learning
dc.titleMultidisciplinary modules on sensors and machine learningen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.contributor.orcidPattichis, Constantinos [0000-0003-1271-8151]
dc.gnosis.orcid0000-0003-1271-8151


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