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In-Class Laboratory Exercises to Improve a Signals and Systems Course

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

New ECE laboratories

Tagged Division

Electrical and Computer

Page Count

10

DOI

10.18260/1-2--32955

Permanent URL

https://strategy.asee.org/32955

Download Count

598

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Paper Authors

biography

Jiahui Song Wentworth Institute of Technology

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Jiahui Song received her B.S. in Automation and M.S. in Pattern Recognition & Intelligent Systems from Southeast University. She received her Ph.D. in Electrical and Computer Engineering from Old Dominion University. She is currently an Associate Professor in the Department of Electrical Engineering and Technology at Wentworth Institute of Technology.

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Douglas Eric Dow Wentworth Institute of Technology

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Associate Professor at Wentworth Institute of Technology in the Department of Electrical and Computer Engineering (started 2008). Education B.A. in Liberal Arts Engineering from Wheaton College (Wheaton, IL); B.S. in Electrical Engineering from Texas A&M University (College Station, TX); M.S. in Computer Science from University of Colorado (Colorado Springs, CO); M.S. and Ph.D. in Biomedical Engineering from University of Michigan (Ann Arbor, MI). Worked in industry for about 9 years at Ampex Corporation (video systems manufacturing) in Colorado Springs CO, Panasonic (central research lab) in Osaka, Japan, and National University of Singapore (center for image enhanced medicine) in Singapore. Post Doc or Sabbatical research was done at Tohoku University (biology information systems) in Sendai, Japan, Mayo Clinic (respiration research lab) in Rochester MN, and Kansai University (knowledge information systems) in Osaka, Japan. Core focus involves embedded electronic systems for applications in medical rehabilitation, health monitoring, physical therapy and assistive technologies. This involves development of hardware and software systems with sensors, embedded control and mechanical actuators. Applications include respiration monitoring, sleep apnea, rehabilitation of impaired muscle for recovery of motor function, health monitoring for elderly to extend independent living, and diabetes management. These systems utilize internet of things (IoT) for remote communication between patient, medical staff, care-givers and instrumentation.

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Lili Ma New York City College of Technology

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Professor Ma received her Ph.D. in Electrical Engineering from Utah State University focusing on autonomous ground vehicles. After that she did three-year post-doctoral training at Virginia Tech working with autonomous aerial vehicles. Prior to joining the Computer Engineering Technology (CET) department at City Tech in fall 2016, she taught at Wentworth Institute of Technology for many years. Professor Ma’s research areas include autonomous mobile robots, vision-based control, visual servoing, visual tracking, vision-assisted coordinated control, and sensing & perception techniques.

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Abstract

In-Class Laboratory Exercises to Improve Signals and Systems Course

Signals and systems is a course that introduces students to mathematical concepts that are used to analyze systems or signals. Conventional courses in signals and systems use lecture and readings to explain the theory, and assign paper based problem sets of theory and math. Conceptual understanding of the course content remains a challenge for many undergraduate students. A series of exercises was developed to help students visualize the complex mathematical concepts and gain a better appreciation for how the concepts are useful in real-world situations. Some of the exercises were hardware based hands-on activities and others were software based simulations.

Similar to the curriculum at many universities, our program has signals and systems course for junior students in electrical engineering and computer engineering. This course is a 4 hour lecture, 4 credit course. The following topics are introduced to students: signal operations, classifications of signals and systems, time-domain analysis of continuous-time systems, Laplace transform, Fourier series, Fourier transform, sampling and discrete-time signal analysis. Problem sets related to these topics were assigned. In order to improve motivation and learning, application-oriented and hands-on active-learning opportunities were created. Hardware based hands-on included AM modulation and demodulation by Telecommunication Instructional Modelling System (TIMS). Software based simulation exercises included filter design to strengthen understanding of the frequency response.

Evaluations were based on student surveys (course evaluations) and student work (assigned homework, exams and in class lab exercises). Recent offerings of this course taught in the traditional way by the same instructors resulted in only 51% of the students receiving a “B-” or higher grade for the course. In the first iteration of in-class laboratory exercises, the number of students who received a “B-” or better increased to 76%. Moreover, 86% of students “agree” or “strongly agree” that in class laboratory exercises helped them to better learn the course content. 81% “of the students agree” or “strongly agree” that laboratory exercises increased their interest in the subject.

Song, J., & Dow, D. E., & Ma, L. (2019, June), In-Class Laboratory Exercises to Improve a Signals and Systems Course Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32955

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