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A Microcontroller-based DSP Laboratory Curriculum

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

CoED: Embedded Systems and Robotics

Tagged Division

Computers in Education

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Paper Authors


Ying Lin Western Washington University

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Ying Lin has been with the faculty of Engineering and Design Department at Western Washington University since September 2010 after she taught for two years at SUNY, New Platz. She received her MS in Applied Statistics and Ph.D. in Electrical Engineering from Syracuse University, NY, respectively. Her teaching interests include first-year Intro to Electrical Engineering, circuit analysis sequence, and upper-division communication systems and digital Signal Processing courses. Her research areas focus on statistical signal processing for wireless sensor network applications and secure communications in wireless networks.

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Todd D. Morton Western Washington University

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Todd Morton has been teaching the upper level embedded systems and senior project courses for Western Washington University's Electronics Engineering Technology(EET) program for 25 years. He has been the EET program coordinator since 2005 and also served as department chair from 2008-2012. He is the author of the text ’Embedded Microcontrollers’, which covers assembly and C programming in small real-time embedded systems and has worked as a design engineer at Physio Control Corporation and at NASA's Jet Propulsion Laboratory as an ASEE-NASA Summer Faculty Fellow. He has a BSEE and MSEE from the University of Washington.

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A Microcontroller-based DSP Laboratory Curriculum In this paper, we present digital signal processing (DSP) hands-on laboratory coursework which was developed based on a low-cost embedded microcontroller (MCU) platform. Recent advances in MCUs (e.g. ARM Cortex M MCUs) have made the embedded microcontroller an option for most DSP applications and therefore a practical option for the DSP laboratory.

The selected MCU tool uses the same ARM Cortex-M4 platform as used for the embedded microcontroller courses in our program with the addition of the ARM CMSIS DSP library. Our work was inspired by the needs of creating meaningful hands-on DSP lab experiments in the allotted one term period (ten weeks) and by the goal of improving student success in implementing DSP-based culminating projects that meet desired goals within realistic constraints. The benefits of integrating the MCU tools in the DSP course are very promising. It permits more practical DSP laboratories and DSP-based capstone projects that render richer design experiences and makes meeting realistic design constraints feasible. Furthermore, it provides an integrated laboratory curriculum structure between embedded microcontroller and DSP courses which reduces students’ unnecessary effort of learning new tools in different courses. Consequently students can focus more on the subject matter. Finally, our successful effort may be adapted to the EE curricula in other institutions to meet their DSP teaching needs and may be utilized in other curricular areas such as controls and communications.

Some of the preliminary work has been reported in a preceding work-in-progress paper, such as the hardware and software platform selection and development for the DSP lab coursework. Specifically, the Freescale tower system with the Kinetis K65TWR board is adopted along with a custom CODEC tower board to support high-fidelity audio signal processing applications. Additional hardware platforms and features are being developed. The software platform is based on the KDS IDE used in the microcontroller courses with the ARM CMSIS DSP library.

In this work, we have developed a series of the selected MCU-based DSP hands-on laboratory exercises that are tied to the fundamentals of DSP by using real-life audio signals. These hands-on lab experiments have been designed to enhance students’ understanding of important DSP topics and to practice real-time DSP algorithm implementations. These lab exercises focus on topics including spectrum analysis through DFT/FFT, FIR and IIR digital filtering, impact of fixed-point and floating point on digital filtering, and adaptive filtering.

These lab experiments were assigned to and carried out by students who took the DSP class in spring 2016 for the first time. We noted that one of the improvements of using the MCU platform over the traditional DSP hardware platform (such as TMS320C6713DSK) is that it permits students completing more complex DSP algorithms (e.g., the adaptive filtering lab) in the 10-week term of study.

Detailed descriptions of the developed hands-on lab experiments, assessment results such as students’ feedback that demonstrates the effectiveness of these lab coursework, and proposed future modifications will be presented in the final paper.

Lin, Y., & Morton, T. D. (2017, June), A Microcontroller-based DSP Laboratory Curriculum Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27480

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