TY - GEN
T1 - Signal processing and machine learning concepts using the reflections echolocation app
AU - Banavar, Mahesh K.
AU - Gan, Houchao
AU - Robistow, Benjamin
AU - Spanias, Andreas
N1 - Funding Information:
ACKNOWLEDGMENTS The work at CU is supported in part by the NSF DUE award 1525224. The work at ASU is supported in part by the NSF DUE award 1525716 and the SenSIP Center.
PY - 2017/12/12
Y1 - 2017/12/12
N2 - This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features extracted from this signal, the app measures distances and determines how the space can be utilized for various application such as libraries, conference rooms, or laboratories. The application and related algorithms use concepts such cross-correlation, feature extraction, learning/training algorithms, and discrimination/decision making. These concepts are typically covered in undergraduate classes such as Digital Signal Processing, Control Systems, and Probability and Statistics; and graduate-level classes such as Pattern Recognition and Detection and Estimation Theory. The app is used to create compelling demonstrations and immersive exercises to teach basic concepts related to signal processing and machine learning. Undergraduate student hands-on workshops and outreach activities are planned to evaluate the effectiveness of this approach. Assessment results will be presented at the conference.
AB - This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features extracted from this signal, the app measures distances and determines how the space can be utilized for various application such as libraries, conference rooms, or laboratories. The application and related algorithms use concepts such cross-correlation, feature extraction, learning/training algorithms, and discrimination/decision making. These concepts are typically covered in undergraduate classes such as Digital Signal Processing, Control Systems, and Probability and Statistics; and graduate-level classes such as Pattern Recognition and Detection and Estimation Theory. The app is used to create compelling demonstrations and immersive exercises to teach basic concepts related to signal processing and machine learning. Undergraduate student hands-on workshops and outreach activities are planned to evaluate the effectiveness of this approach. Assessment results will be presented at the conference.
KW - Echolocation
KW - Machine learning
KW - Mobile education apps
KW - STEM
KW - Signal processing
UR - http://www.scopus.com/inward/record.url?scp=85043262186&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043262186&partnerID=8YFLogxK
U2 - 10.1109/FIE.2017.8190437
DO - 10.1109/FIE.2017.8190437
M3 - Conference contribution
AN - SCOPUS:85043262186
T3 - Proceedings - Frontiers in Education Conference, FIE
SP - 1
EP - 5
BT - FIE 2017 - Frontiers in Education, Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE Frontiers in Education Conference, FIE 2017
Y2 - 18 October 2017 through 21 October 2017
ER -