Teaching Adaptive Filters and Applications in Electrical and Computer Engineering Technology Program
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Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle