Teaching Adaptive Filters and Applications in Electrical and Computer Engineering Technology Program
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Bibliographic record
Abstract
Abstract Teaching Adaptive Filters and Applications in Electrical and Computer Engineering Technology ProgramAbstract In this paper, we present pedagogy and experiences for teaching adaptive filtering andapplications in the advanced signal processing (DSP) course for electrical and computerengineering technology (ECET) program. The course is elective for senior students anddesignated as the second DSP course with focusing on real-time processing and adaptivefiltering applications. The second course is offered according to the current industry trendin the DSP area and students interest in their career development. The course pre-requisite assumes that students acquired working knowledge and skills of Laplacetransform, Fourier series, Fourier transform, z-transform, discrete Fourier transform,digital filter design, and real-time DSP experience with TX320TMS67C13 DSK in thefirst DSP course. Although adaptive filtering is an exciting topic, in which many real-lifeapplications can be explored, teaching this topic is often challenging due to the extensiveuse of mathematics such as matrices and statistics, especially for technology students. Inthis paper, we demonstrate that the used traditional math can be simplified to theminimum level so that technology students can easily understand and grasp key concepts.Furthermore, with MTALAB software tool and real-time DSP using a floating-pointdigital signal processor, TX320TMS67C13 DSK, students can apply the adaptivefiltering techniques in applications such as noise cancellation, speech processing, as wellas line enhancement, echo cancellation and active noise control. We also show thatTX320TMS67C13 DSK is an effective tool in teaching real-time adaptive filters. Inaddition, real-time implementation techniques for adaptive filtering projects are presented. In this paper, we will describe the pedagogy for teaching adaptive filtering principleswith MATLAB simulations and then focus on real-time DSP pedagogy for our hands-onprojects in various adaptive filter applications. We will also examine the assessmentaccording to our collected data from course evaluation, student surveys and studentcourse work. Finally we will address the possible improvement based on our assessment.References1. L. Tan, Digital Signal Processing: Fundamentals and Applications. Elsevier/AcademicPress, 2008.2. L. Tan and J. Jiang, A Simple DSP Laboratory Project for Teaching Real-Time SignalSampling Rate Conversions, the Interface Technology Journal, Vol. 9, No. 1, Fall 2008.3. N. Kehtaranavaz, and B., Simsek, C6x-Based Digital Signal Processing, Prentice Hall,Upper Saddle River, New Jersey 07458, 2000.4. Texas Instruments, TMS320C6x CPU and Instruction Set Reference Guide, LiteratureID# SPRU 189C, Texas Instruments, Dallas, Texas, 1998.5. Texas Instruments, Code Composer Studio: Getting Started Guide, Texas Instruments,Dallas, Texas, 2001.6. L. Tan, J. Jiang, “Teaching Advanced Digital Signal Processing with MultimediaApplications in Engineering Technology Programs,” ASEE Annual Conference, June2009.7. Ifeachor, Emmanuel and Jervis, Barrie. Digital Signal Processing, A PracticalApproach, Prentice-Hall Publishing, 2002.8. de Vegte, Joyce Van. Fundamentals of Digital Signal Processing, Prentice-HallPublishing, 2002.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it