June 22, 2003
June 22, 2003
June 25, 2003
8.841.1 - 8.841.9
MATLAB Exercises to Explain Discrete Fourier Transforms
Kathleen A.K. Ossman, Ph.D. University of Cincinnati
Digital Signal Processing is used in many applications including cellular phones, CDs, DVDs, speech recognition, pattern recognition, and control systems. Traditionally, digital signal processing (DSP) has been taught as an advanced undergraduate or graduate course in an engineering curriculum. Over the last decade, in response to the increase in industrial applications, DSP has been introduced earlier in engineering schools. However, courses in digital signal processing are noticeably absent in engineering technology programs. A recent look at ABET accredited electrical/electronics engineering technology programs  showed that only 6 of the 66 programs accessed offered DSP as a required course in the curriculum. Another nine programs offered DSP as a technical elective and the remaining 51 did not offer DSP to their students at all. As DSP becomes more pervasive in industrial applications, it is imperative that engineering technology graduates have some exposure to digital signal processing theory and practice. The main difficulty in teaching DSP to technology students is the level of mathematics. Students opening a textbook on digital signal processing  –  are faced with pages and pages of equations. In developing an introductory course in DSP for our electrical and computer engineering technology students, I have found MATLAB and SIMULINK  to be invaluable illustrative tools for teaching the mathematically intense concepts of DSP. This paper gives a brief overview of the required DSP course taught in the ECET department at the University of Cincinnati then details the specific MATLAB exercises used to illustrate one of the more difficult concepts in the course: discrete Fourier transforms (DFT).
Topics of Digital Signal Processing I
This introductory course in digital signal processing covers sampling and reconstructing of analog signals, convolution and correlation, finite impulse response (FIR) and infinite impulse response (IIR) digital filters, the discrete Fourier transform (DFT), and efficient computation of the DFT using fast Fourier transforms (FFT). The course goals and schedule are as follows:
§ To develop a clear understanding of the practical issues involved in sampling an analog signal and reconstructing an analog signal from a digital signal. § To explore the advantages of digital filters over analog filters, learn to design digital filters, and implement the designs using Texas Instruments' 6x DSK boards.
Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright © 2003, American Society for Engineering Education
Ossman, K. (2003, June), Matlab Exercises To Explain Discrete Fourier Transforms Paper presented at 2003 Annual Conference, Nashville, Tennessee. 10.18260/1-2--12002
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