Master Course Description for EE-443 (ABET sheet)

Title: Design and Application of Digital Signal Processors

Credits: 5

UW Course Catalog Description

Coordinator: Jenq-Neng Hwang, Professor of Electrical Engineering

Goals: The goal of this course is to provide senior ECE students, who are majoring in Signal/Image Processing and Communications, with significant design experience and introducing them the important laboratory components of real-time DSP based on commercially available microprocessors for solving real world filtering, spectrum analysis, adaptive filtering and machine learning techniques for speech and audio processing applications.

Learning Objectives: Providing students with the fundamental skills and hands-on experience in applying the theory learned in EE 442/EE443 to various real-time DSP tasks based on embedded processor environment. The course also involves proposal preparation, team work scheduling and planning, real-time DSP data collection, technical presentation, and final project demo and report writing.

Textbook: Thad B. Welch, Cameron H.G. Wright and Michael G. Morrow, Real-Time Digital Signal Processing from MATLAB to C with the TMS320C6x DSPs, 3th Ed. CRC Press, Taylor & Francis Group, 2017.

Reference Materials: Texas Instruments TMS320C6748 DSP Development Kit (LCDK), Link

Prerequisites by Topic:

  1. Discrete Time Signals and LTI Systems
  2. Fourier and Z Transforms
  3. Fast Fourier Transform
  4. FIR and IIR Filters
  5. Adaptive Filters
  6. Basic Statistics and Optimization Theories


  1. DSP Development Systems and Use of the Lab.
  2. Architecture and Instruction Set of the TMS320C6x Processor, LCDK-IO, LCDK-PC communication
  3. FIR Filters and IIR Filters Implementations
  4. FFT Implementations and Adaptive Filters
  5. Digital Speech Recognition
  6. Machine Learning via Unsupervised Learning
  7. Machine Learning via Supervised Learning
  8. Machine Learning via Neural Networks and Deep Learning

Course Structure: The class meets for two lectures a week, each consisting of two 50-minute sessions. The whole class is divided into small groups (2-3 students). There are 3 homework (group based) that include some DSP design projects to get students familiar with the LCDK development system on the taught filtering and machine learning algorithms, and writing the assembly and C programs. One final project (group based) is due the end of quarter. This final project requires a midterm proposal, oral presentation of the progress, final project demo and report.

Computer Resources: The course uses TI TMS320C6748 DSP Development Kit (LCDK), which can be connected to PCs, for all real-time design assignments. The recommended platforms are the departmental PCs in EECSE 351, which contains functional generators, oscilloscopes, spectrum analyzers, microphones and speakers. The average student will require 10-12 hours of computer work per week.

Laboratory Resources: None

Grading: warm-up homework 30%, Final Project Proposal 10%, Final Project Presentation 10%, and Final Project Demo and Report 50%

ABET Student Outcome Coverage: This course addresses the following outcomes:

H = high relevance, M = medium relevance, L = low relevance to course.

(1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics. (H)

(2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors. (H)

(3) An ability to communicate effectively with a range of audiences. (H) Students will present their projects to the class in written and oral form.

(4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts. (L)

(5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives. (M)

(6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions. (H)

(7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. (M)

Prepared by: Jenq-Neng Hwang

Last revised: 6/21/2018