Teaching Adaptive Filters and Applications in Electrical and Computer Engineering Technology Program

In this paper, we present our pedagogy and our experiences with teaching adaptive filters combined with applications in an advanced digital signal processing (DSP) course. This course is the second DSP course offered in the electrical and computer engineering technology (ECET) program according to the current trend of the DSP industry and students’ interests in their career development. A significant component of this course is adaptive filtering applications 1-7 . A prerequisite for students is a working knowledge of the Laplace transform, Fourier series, Fourier transform, z-transform, discrete Fourier transform, digital filter design, and real-time DSP coding skills with TMS320C6713, a high-performance floating-point digital signal processor 8-9 (DSP board) by Texas Instruments, acquired through the first DSP course. Although adaptive filtering is an exciting topic which allows for the exploration of many real-life applications, teaching this topic is often challenging due to its relatively heavy reliance on advanced mathematics. It is possible for the traditional mathematics used for the adaptive filtering theory to be minimized so that engineering technology students can more easily understand and grasp key concepts. With the MATLAB software tool, students can simulate and verify different adaptive filtering applications. To enhance hands-on learning, students are required to implement adaptive filtering techniques taught during the lectures using a TMS320C6713. Furthermore, it can be shown that a TMS320C6713 with its stereo channels proves an effective and flexible tool for various DSP implementations. In this paper, we first focus on describing the pedagogy for teaching adaptive filter principles along with MATLAB simulations; then, we illustrate real-time DSP hands-on labs and projects with various applications. We will examine the assessment based on our collected data from course evaluations, student surveys and course work, and finally we will address possible improvement based on our assessment.