UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN

Department of Electrical and Computer Engineering

ECE 310: Digital Signal Processing (Spring 2026)

Course Description:

Introduction to discrete-time systems and discrete-time signal processing with an emphasis on causal systems; discrete-time linear systems, difference equations, z-transforms, discrete convolution, stability, discrete-time Fourier transforms, analog-to-digital and digital-to-analog conversion, digital filter design, discrete Fourier transforms, fast Fourier transforms, spectral analysis, and applications of digital signal processing.

 

Course Prerequisite:

ECE 210

I. Teaching Staff

1. Instructors:

Prof. Corey Snyder (Sec. G) Prof. Ilan Shomorony (Sec. E)
Office: ECEB 2058 Office: CSL 313
Email: cesnyde2@illinois.edu Email: ilans@illinois.edu

2. Teaching Assistants:

Yulun (Ben) Wu Ethan Ran Lucas Nguyen Muzaffer Ozbey
Email: yulun4@illinois.edu Email: eran2@illinois.edu Email: lnguy7@illinois.edu Email: mozbey2@illinois.edu

II. Schedule

1. Lectures:

Lecture Time Day Location
Section G 9:00 a.m. - 9:50 a.m. M W F ECEB 1002
Section E 12:00 pm. - 12:50 p.m. M W F ECEB 1013

2. Office Hours:

Note: Start from 1/26

Time Monday Tuesday Wednesday Thursday Friday
9-10 a.m. Lecture Ben (ECE 2034) Lecture Ben (ECE 2034) Lecture
10-11 a.m.   Ben (ECE 2034) Prof. Shomorony (CSL 313) Ben (ECE 2034)  
11 a.m.-12 p.m.          
12-1 p.m. Lecture   Lecture Prof. Snyder (ECE 2034) Lecture
1-2 p.m.     Ethan (ECE 2034)   Ethan (ECE 2034)
2-3 p.m.   Lucas (ECE 2034) Ethan (ECE 2034) Lucas (ECE 2034) Ethan (ECE 2034)
3-4 p.m.   Lucas (ECE 2034)   Lucas (ECE 2034)  
4-5 p.m.          
5-6 p.m.     Muzaffer (ECE 2034)    
6-7 p.m.     Muzaffer (ECE 2034)    

III. Resources

1. Recommended Textbook:

2. Course Campuswire:

3. Associated Lab Course (Strongly recommended):

4. Additional Resources

The following additional resources cover much of the same material as the lectures and textbook. The syllabus below provides references to these resources as well as the Manolakis and Ingle textbook.

IV. Syllabus

Lecture Notes, Slides, and Materials

Section G (9am) Materials

Section E (12pm) Materials

Time Topics Reading Assignment Additional Resources Assessment Due

Week 1:
1/19 - 1/23

(No class on 1/19 for MLK Day)

 Course introduction
Continuous-time (CT) and discrete-time (DT) signals
Review of complex numbers		 Chapter 1: 1.1 - 1.4 SM: Ch 1, Appendix D, Appendix A, 3.1, 3.3-3.6
OS: 1, 2.1-2.2
PM: 1.1-1.2, 2.1-2.2
FK: 1, 5, 2, 9
Python Demo
What is DSP? - Video by IEEE
DSP at UIUC - 1
DSP at UIUC - 2		  
Week 2:
1/26-1/30		 Discrete-time systems
Linear and time-invariant (LTI) systems
Impulse response
Convolution
Difference equations		 Chapter 2: 2.1 - 2.7; 2.10

SM: 3.7-3.9
OS: 2.3-2.5
PM: 2.3-2.5
FK: 9, 10, 3
Convolution Python Demo
Difference Equations Python Demo

 HW1
 
Week 3:
2/2-2/6		 z-transform
Poles and zeros
Inverse z-transform		 Chapter 3: 3.1 - 3.4 SM: 4.1-4.5
OS: Ch 3
PM: 3.1-3.5
FK: 6, 7, 8 13
Partial Fractions Python Demo
Some z-transform properties
Some z-transform pairs		 HW2
 
Week 4:
2/9-2/13		 System analysis via z-transform
System transfer function
Stability		 Chapter 3: 3.5 - 3.7 SM: 4.10-4.14
OS: 5.2
PM: 3.6
FK: 14, 15, 16
Stability Python Demo		 HW3
 
Week 5:
2/16-2/20		 Applications of linear system models
Sinusoidal signals
Fourier transforms
Discrete-time Fourier transform (DTFT)		 Chapter 4: 4.1 - 4.3 SM: 2.1-2.4
OS: 2.6-2.7 PM: 1.3, 4.1
FK: 17
Inverse Filter Python Demo
Applications of Linear System Theory		 HW4
 
Week 6:
2/23-2/27

Midterm 1 (Wednesday, 2/25)

No class Wednesday

Properties of the DTFT
Frequency response

 Chapter 4: 4.3 - 4.5
Chapter 5: 5.1 - 5.2		 SM: 2.4, 5.1
OS: 2.8-2.9, 5.1
PM: 4.2-4.4
FK: 18, 19
DTFT Python Demo
Filtering Python Demo		 HW5
 
Week 7:
3/2-3/6		 Frequency response (magnitude and phase responses)
Ideal filters
Sampling of continuous-time signals		 Chapter 5: 5.3 - 5.6
Chapter 6: 6.1		 SM: 5.2, 3.2
OS: 5.3-5.4, 4.1-4.2
PM: 4.4-4.5, 1.4
FK: 20, 21		 HW6
 
Week 8:
3/9-3/13		 Ideal A/D and D/A conversion
Aliasing effect
Discrete Fourier transform (DFT)
 Chapter 6: 6.2 - 6.3
Chapter 7: 7.1 - 7.2		 SM: 3.2, 2.5
OS: 4.2-4.3
PM: 1.4, 4.2.9, 5.1
FK: 22, 34
Sampling Demo		 HW7
 

Spring Break:

3/16-3/20

        
Week 9:
3/23-3/27		 Discrete Fourier transform (DFT)
DFT spectral analysis
DFT applications		 Chapter 7: 7.2 - 7.4; 7.6
Chapter 6: 6.4-6.5		 SM: 2.5-2.6
OS: 8.1-8.6, 10.1-10.2
PM: 5.2, 5.4
FK: 34, 36
DFT Python Demo
Toy Spectral Analysis Jupyter Notebook
Audio Spectral Analysis Demos		 HW8
 
Week 10:
3/30-4/3		 Fast Fourier transform (FFT)
Convolution using the DFT
Digital processing of analog signals		 Chapter 7: 7.5
Chapter 8: 8.1; 8.3		 SM: Ch 14, 6.3
OS: 8.7, 9.3, 6.1-6.2
PM: 5.3, 6.1-6.2, 7.1
FK: 37, 38.		 HW9
 
Week 11:
4/6-4/10

Midterm 2 (Wednesday, 4/8)

No class Friday (4/10) for EOH

Practical digital filters
FIR filter design

 Chapter 12: 12.1-12.2   HW10
 
Week 12:
4/13-4/17		 Generalized linear phase filters
FIR filter design by windowing		 Chapter 9: 9.1-9.3
Chapter 10: 10.1-10.3
Chapter 11: 11.1; 11.3		 SM: 6.4, Ch 11, Ch 12
OS: 5.7, Ch 7
PM: Ch 8
FK: 28, 29, 30.		 HW11
 
Week 13:
4/20-4/24		 Downsampling and decimation
Upsampling and interpolation
Multirate signal processing		 Chapter 12: 12.1-12.2

 

 HW12
 
Week 14:
4/27-5/1		 Practical A/D, D/A, upsampling D/A, ZOH
Applications: instructor's choice, student's choice		 Chapter 6: 6.5
Chapter 15: 15.3		   HW13
 
Week 15: 5/4-5/8 Final exam review  

 

 

  
Final Exams: 5/11-5/18        

V. Grading

  1. Weekly Homework: 20% of final grade
    • Grading: Homework average is computed by dropping the two lowest scores and then computing the average; this implies that each student may omit two homeworks in case of extenuating circumstances. Since the solutions will be posted immediately after the submission deadline, no late submission will be accepted.
    • Submission: Homework should be uploaded as a PDF file to Gradescope in which we have added each student enrolled. If you have not been auto-enrolled to our course Gradescope, you may join using entry code D66BGN
    • Due dates: Homework is assigned each Friday, due the following Friday at 11:59pm. The corresponding solution will be posted immediately after the due date.
    • Write neatly. Please box the equations you will be solving and the final answer. If we cannot read it we cannot grade it!
    • Regrade requests must be submitted on gradescope within one week of grades being posted. All regrade requests must have a brief justification.
    • Again, late homework submissions will not be accepted.
  2. Exams (will be held in-person): 80% of Final Grade
    1. Midterm Exam 1: 22% of Final Grade
      • Date: Wednesday, 2/25, 7:00-9:00pm
      • Location: TBD
      • Coverage: material from weeks 1-4, through HW4.
      • Conflict exam: 
        • Date: Thursday, 2/26
        • Location: TBD
      • HKN Review Session: TBA
        • Date:
        • Locations:
    2. Midterm Exam 2: 22% of Final Grade
      • Date: Wednesday, 4/8, 7:00-9:00pm
      • Location: TBD
      • Coverage: materials corresponding to HWs 5-9.
      • Conflict exam:
        • Date: Thursday, 4/9
        • Location: TBD
      • HKN Review Session: TBA
        • Date:
        • Location:
    3. Final Exam: 36% of Final Grade
      • Date:
      • Location:
      • Coverage: material from the whole semester
      • Conflict exam:
      • HKN Review Session: TBA
        • Date:
        • Location:
  3. Final Grade Cutoffs: The following cutoffs will be used to assess final grades. The cutoffs will never be raised, but might be lowered based on the class distribution. We will communicate any changes clearly in class and here on the website.
  • A+: 93-100%, A: 90-93%, A-: 87-90%
  • B+: 83-87%, B: 80-83%, B-: 77-80%
  • C+: 73-77%, C: 70-73%, C-: 67-70%
  • D+: 63-67%, D: 60-63%, D-: 57-60%

VI. Integrity and AI Usage

This course will operate under the following honor code: All exams and homework assignments are to be worked out independently without any aid from any person or device. Copying of other students' work is considered cheating and will not be permitted. By enrolling in this course and submitting exams and homework assignments for grading, each student implicitly accepts this honor code.

Generative AI tools, such as ChatGPT, Microsoft Copilot, and Gemini, can answer questions and generate text, images, and other media. The appropriate use of generative AI varies from course to course. In ECE 310 there are times when generative AI may be useful in the course and other times where we prohibit the use of generative AI. Specifically, you MAY NOT use generative AI in ECE 310 to complete homework assignments. You MAY use generative AI in ECE 310 to aid your studying and preparation for exams. This includes generating additional practice problems, verifying your solutions, and checking your own understanding of course concepts.

If you have a question about the use of Generative AI, please reach out to your instructor.  Failure to abide by these guidelines is a violation of academic integrity. We will investigate suspected uses of generative AI that do not follow these guidelines and apply sanctions as outlined in the University of Illinois Student Code.

VII. Homework Material

Exercises Due Date Solution
Homework 1 01/30 @ 11:59pm Homework 1 Solution
Homework 2 02/06 @ 11:59pm Homework 2 Solution
Homework 3 02/13 @ 11:59pm Homework 3 Solution
Homework 4 02/20 @ 11:59pm Homework 4 Solution
Homework 5 03/01 @ 11:59pm (Extension for Exam 1) Homework 5 Solution
Homework 6 03/06 @ 11:59pm Homework 6 Solution
Homework 7 03/13 @ 11:59pm Homework 7 Solution
Homework 8 03/27 @ 11:59pm Homework 8 Solution
Homework 9 04/03 @ 11:59pm Homework 9 Solution
Homework 10 04/12 @ 11:59pm (Extension for Exam 2) Homework 10 Solution
Homework 11 04/17 @ 11:59pm Homework 11 Solution
Homework 12 04/24 @ 11:59pm Homework 12 Solution
Homework 13 05/05 @ 11:59pm Homework 13 Solution