Digital signal processing EEET-425-2241

FALL 2024

Course Information

 

EEET-425: Digital Signal Processing

 

Class Time and Location:

Lecture On-Line

Tuesday 09:30AM - 10:45 AM

Thursday 09:30AM - 10:45 AM                           

Online via ZOOM

 

Labs

Section L1 – Friday 10:00AM – 11:50 PM ENT-3125

Section L2 – Friday 12:00AM – 1:50 PM ENT-3125

 

 

Course Mode:

Lecture online and Labs In-Person

Prerequisite(s)/Co-requisite courses:

EEET-331 and EEET-332 Signals, Systems & Transforms

STAT-145 – Introduction to Statistics I

MATH-251 – Probability and Statistics I

 

Catalog Description

Develops the knowledge and ability to process signals using Digital Signal Processing (DSP) techniques. Starts with foundational concepts in sampling, probability, statistics, noise, fixed and floating-point number systems, and describes how they affect real world performance of DSP systems. Fundamental principles of convolution, linearity, duality, impulse responses, and discrete Fourier transforms are used to develop FIR and IIR digital filters and to explain DSP techniques such as windowing. Students get an integrated lab experience writing DSP code that executes in real-time on DSP hardware. 

 

Course Rationale and Goals

Signal processing is an essential component of many systems, including embedded systems. This course extends the concepts of continuous-time and discrete-time systems introduced in Signals and Systems and Transforms, specifically convolution, system representations, the Fourier transform and presents the application of these concepts to processing of signals. The goal is to give students the knowledge to digitally process signals using both time and frequency domain representations. Hands-on experience with DSP hardware is provided in the laboratory exercises.

 

Course Learning Outcomes

A student who successfully fulfills the course requirements will have demonstrated the following:

 

Teaching Philosophy

Lectures will present the main topics for the course.  In class activities are used to support learning and understanding of concepts.  The laboratory exercises combine simulation and the practical aspects of building and testing prototypes and coding for DSP.

 

Course Topics

Here is a list of some of the topics that will be covered in the course

Analog to Digital Conversion:

  • Signal to noise calculations
  • ADC and Dithering
  • ADC Sampling and Aliasing

 

DSP Math and Number Systems:

  • Fixed Point Number Systems
  • Floating Point number systems

 

Discrete Fourier Transforms:

  • Convolution
  • Properties of the Fourier Transform
  • The Fast Fourier Transform

Digital Filtering:

  • Moving Average Filters
  • FIR and IIR filters
  • Windowed Filters
  • Chebyshev Filters