ECE 311

• Signal Processing

Lab course associated with ECE 310.

Lab course associated with ECE 310.

• Orientation and overview of Python and DSP (2 hrs)

• DTFT, DFT, DFT Spectral Analysis (2 hrs)

• Windowing, Convolution, LSI systems, Difference Equations (2 hrs)

• z-Transforms, Pole-Zero Diagrams, BIBO Stability (2 hrs)

• Frequency Response of Discrete Time Systems (2 hrs)

• FIR and IIR Filter Design (2 hrs)

• DSP and image processing applications (1 hour)

Students will have access to workstations running Python software, and during laboratory hours, the computer laboratory classroom will be staffed with a TA for the course.

Laboratory reports will be due for each of the laboratory assignments in the course, which are given roughly bi-weekly.

Laboratory projects based on ECE310 course material will be given on a bi-weekly schedule. These will emphasize the concepts from ECE310 through real-data processing and evaluation using Python.

One 180-minute laboratory session per week.

Python

Credit or concurrent registration in ECE 310.

Engineering Topics: 100%

To reinforce fundamentals of discrete-time linear systems and digital signal processing through computer laboratory exercises. Emphasizes design, implementation and applications.

A student completing this course should, at a minimum, be able to:

1. Use Python to create, display, and analyze signals in the time-domain (1, 2, 6)

2. Use Python to analyze and display signals in the frequency-domain using the FFT algorithm to model the DTFT as well as for spectral analysis using the DFT (1, 2, 6)

3. Perform convolution and simulate LSI systems and difference equations (1, 2, 6)

4. Plot pole-zero diagrams for LSI systems with rational transforms, use Python to study properties of the z-transform and its relationship to stability (1, 2, 6)

5. Determine and plot the frequency response of LSI systems (1, 2, 6)

6. Design FIR and IIR filters using Python to meet specifications on their frequency response using window design, frequency sampling design, and the bilinear transformation (1, 2, 6)

7. Use Python to apply the above methods to process real data for image and signal processing applications (1, 2, 6)

8. Write lab reports as Python notebooks to document observations and insights gathered from numerical experiments in the labs. (3)