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Board 132: Multidisciplinary Modules on Sensors and Machine Learning

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

16

Permanent URL

https://peer.asee.org/29923

Download Count

34

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Paper Authors

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Abhinav Dixit Arizona State University

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I am a PhD student at School of Electrical, Computer and Energy Engineering at Arizona State University. My research interest includes early detection of neurological diseases through irregularities in speech.
I also work as a Research Assistant at SenSIP Center, ECEE at ASU. I am currently involved in developed of JDSP HTML5, an interactive DSP software developed in HTML5.

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Uday Shankar Shanthamallu Arizona State University

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“I received my B.S degree in Electronics and Communications from the National Institute of Engineering, India in 2011. I am currently pursuing my Master’s and PhD program in Electrical Engineering at Arizona State University(ASU). I am advised by Dr. Andreas Spanias. I joined Sensor, Signal and Information Processing Center (SenSIP) at ASU in Jan 2016.
My research interests lie at the overlap of sensors and Machine learning and Big Data including, but not limited to Pattern recognition and Anomaly detection. In summer 2016, I did a summer internship at NXP Semiconductors where I worked on sensor data analytics for anomaly detection. I worked on integrating machine learning algorithms on an embedded sensor systems for Internet of Things applications, which can identify anomalies in real time. Before joining ASU, I worked as Systems engineer for 4 years at Hewlett Packard Research and Development, Bangalore, India.”

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Andreas S. Spanias Arizona State University

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Andreas Spanias is a professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. He is also the founder and director of the SenSIP industry consortium. His research interests are in the areas of adaptive signal processing, speech processing, and audio sensing. He and his student team developed the computer simulation software Java-DSP (J-DSP - ISBN 0-9724984-0-0). He is author of two text books: Audio Processing and Coding by Wiley and DSP; An Interactive Approach. He served as associate editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing vice-president for conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as distinguished lecturer for the IEEE Signal processing society in 2004.

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Sunil Rao

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Sameeksha Katoch Arizona State University

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Mahesh K. Banavar Clarkson University Orcid 16x16 orcid.org/https://0000-0002-3916-7137

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Mahesh K. Banavar is an assistant professor in the Department of Electrical and Computer Engineering at Clarkson University. He received the BE degree in Telecommunications Engineering from Visvesvaraya Technological University, Karnataka, India in 2005, the MS degree and the PhD degree, both in Electrical Engineering from Arizona State University in 2007 and 2010, respectively. His research areas is Signal Processing and Communications; and STEM education.

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Gowtham Muniraju Arizona State University

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received the B.E degree in Electronics and Communication engineering from Visvesvaraya Technological University, Karnataka, India, in 2016 and the M.S degree in Electrical engineering from Arizona State University, Tempe, AZ, USA, in 2018.

Currently pursuing the Ph.D. degree with the School of Electrical, Computer and Energy Engineering, Arizona State University. Research interests include distributed sensor networks, machine learning, deep learning, convex and non-convex optimization.

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Jie Fan Arizona State University

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Currently, I am working as a Ph.D. candidate in SenSIP lab at ASU.

I received my M.S. degree in electrical engineering-communications from school of electrical, computer and energy engineering at Arizona State University in Oct. 2016. Also the B.S. degree in electrical engineering is received from Huazhong University of Science and Technology (HUST) in May 2014.

In my master thesis, I developed a novel signal recovery algorithm for sensor array with failures. Comparing to conventional approaches based on interpolation and neural networks, our method requires no priori knowledge from the failure conditions and provides high success rate of recovering the missing data.

My current research is mainly abou graphic digital signal processing (GDSP). I focus on applying DSP operators to graphic data applications, such as data classification and data denoising. Those GDSP based methods are very suitable for dataset with complex graphic structure. Comparing to traditional machine learning algorithms, such methods may also provide better performance in success rate of estimation.

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Photini Spanias Arizona State University

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Photini Spanias is Principal Lecturer at the Mary Lou Fulton Teachers College at Arizona State University. She is teaching math methods classes. Her research interests are in math methods and in teacher preparation. She is also interested in online education research.

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Andrew Strom Corona Del Sol High School

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Andrew Strom has been teaching mathematics at Corona Del Sol for 21 years. He has taught a variety of subjects: Algebra 1-2, Geometry, Algebra 3-4, Honors Algebra 3-4, Pre-Calculus, Honors Pre-Calculus, College Mathematics and AP Statistics. Andrew enjoys the beauty of mathematics and loves working with young people.

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Constantinos Pattichis

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Huan Song

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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.

Dixit, A., & Shanthamallu, U. S., & Spanias, A. S., & Rao, S., & Katoch, S., & Banavar, M. K., & Muniraju, G., & Fan, J., & Spanias, P., & Strom, A., & Pattichis, C., & Song, H. (2018, June), Board 132: Multidisciplinary Modules on Sensors and Machine Learning Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/29923

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