External Info

This course builds on the signals and systems fundamentals acquired in ELEC 324. The student learns analysis, design, and synthesis of DSP systems and algorithms. Students study models and techniques that progress from theory to implementations. Students learn the design flow by performing simple but realistic laboratory exercises.

The course begins with a review of sampling theorem and signal digitization, LTI systems, frequency response, and poles and zeroes. New topics such as minimum phase filters and stability check are introduced during the review. A major aim of this course is learning to design DSP functions that can be implemented in real-time using dedicated or programmable hardware. This is accomplished by focusing on a key building block of DSP systems: filters. We study finite impulse response (FIR) and infinite impulse response (IIR) filter realization structures, implementation issues including computational complexity and coefficient sensitivity, and programming digital signal processors for real-time implementation. In practice, filters are designed using computer-based optimization tools such as Matlab. To obtain efficient designs, the designer needs a good grounding in basic theory. Thus, we discuss basic properties of linear phase FIR and causal IIR filters. We study FIR filter design using windows and a numerical optimization technique called equipripple design. IIR filter design from continuous time filters using bilinear transformation is then covered. Real filters are designed and tested on a signal processor development system in the laboratory.

Discrete Fourier transform (DFT) is the most common signal analysis tool for all areas of science and technology. DFT is usually implemented using a fast Fourier transform (FFT) algorithm. We study the basic properties of DFT, basic radix-2 FFT algorithms, and the application of DFT to spectral analysis and filtering.

Course Learning Outcomes (CLOs)
  • For signals and filters, identify and contrast their properties between analog and digital implementations.
  • Define, identify and give examples of the properties of DFT in Fourier spectral analysis.
  • Analyze and design rational (pole-zero) filters.
  • Be able to write programs to implement real-time digital filtering algorithms.
Credit Breakdown

Lecture: 3
Lab: 0.5
Tutorial: 0.5

Academic Unit Breakdown

Mathematics 0
Natural Sciences 0
Complementary Studies 0
Engineering Science 24
Engineering Design 24