Digital Signal Processing Applications

EE482 Spring 2026

Description

Professor

Dr. Brendan Morris,
Office: SEB 3216
OH: TBD: TuTh 10:30-11:30

Lecture

Lecture: TuTh 11:30-12:45, CHB C128
Final: Th May 14, 10:10-12:10
Look up your final exam schedule now to determine conflicts.

Textbook:
Real-Time Digital Signal Processing: Fundamentals, Implementations, and Applications, 3rd Edition, Kuo, Lee, Tian, ISBN: 978-1-118-41432-3
Recommended Text:
Digital Image Processing, 3e, Gonzalez and Woods, ISBN: 978031687288
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools and Techniques to Build Intelligent Systems, 2e, Geron, ISBN: 9781492032649
The Scientist and Engineer's Guide to Digital Signal Processing, Smith, ISBN: 978-0966017632. Available Free Online http://www.dspguide.com/pdfbook.htm


Catalog Description:
Application of signals and systems theory. Topics may include audio and speech signal processing, image processing, multi-spectral imaging, biomedical signals, and active sensing technologies such as Radar and Lidar.
Prerequisites: EE361

Course Syllabus: [pdf]

Grading

ComponentPercentageDate
Final: 20% 05/14 10:10
Quizzes (5): 25% TBD
Project: 25% 05/17
Homework: 30% Bi-Weekly
Homework will include programming problems. While Matlab will be used during instruction, it is highly recommended to use Python. Students may work together in study groups but all assignments must be completed individually. Homework will be due in class on the designated date. No late homework will be accepted unless prior notification and arrangements are made.

The course will have a term project. You will be required to submit a project report in the form of a conference styled manuscript and make a presentation.

Gradebook

The gradebook is available through UNLV Webcampus [link].
Note that the calculated % is not necessarily reflective of your final grade. The gradebook should be used mainly to ensure that I have correctly recorded your scores.

Schedule (Tentative)

WeekDateLecture TopicReadingAssignment
1 01/20 Tu class01: Course Intro [pdf] [Zoom] RTDSP Ch 1 [pdf] HW01 [pdf]
Due Th 01/29
01/22 Th class02: Real-time DSP Intro [Ch1.0-1.3] Fundamentals [Ch2.0-2.2]
2 01/27 Tu class03: Random variables and Numerical effects [Ch2.3-2.5] RTDSP Ch 2 [pdf]
01/29 Th class04: quiz01 Review [PFE notes]
3 02/03 Tu class05: quiz01 - Ch1-2 RTDSP Ch 3 [pdf] HW02 [pdf]
Due Th 02/12
02/05 Th class06: FIR Filter Design and Implementation [Ch3]
4 02/10 Tu class07: IIR Filter Design and Implementation [Ch4.1-4.2] RTDSP Ch 4 [pdf] HW03 [pdf]
Due W 2/19
02/12 Th class08: IIR Filter Design and Implementation [Ch4.3-4.6]
5 02/17 Tu class09: IIR Filter Design and Implementation
02/19 Th class10: quiz02 review - Ch3-4
6 02/24 Tu class11: quiz02 - Ch3-4
Project Introduction [pdf]		 RTDSP Ch 5 [pdf] HW04 [pdf]
Due Tu 3/10
02/26 Th class12: Frequency Analysis
7 03/03 Tu class13: Frequency Analysis
03/05 Th class14: Frequency Analysis - FFT [pdf]
Texas Instruments FFT source notes [ppt]
8 03/10 Tu class15: quiz03 - review Ch5 RTDSP Ch 6 [pdf] HW05 [pdf]
Due Tu 3/31
03/12 Th class16: quiz03 - Ch5
9 03/17 Tu Spring Break
03/19 Th Spring Break
10 03/24 Tu class17: Adaptive Signal Processing RTDSP Ch 9 [pdf]
03/26 Th class18: Speech Signal Processing [pdf][Ch9]
11 03/31 Tu class19: Speech Signal Processing
04/02 Th class20: quiz04 - Ch6, 9
12 04/07 Tu class21: Image Processing [Ch11] RTDSP Ch 11 [pdf] HW06 [pdf]
Due M 4/08
04/09 Th class22: Image Frequency Filtering [pdf]
13 04/14 Tu class23: Object Recognition [pdf] Viola and Jones [pdf] HW07
04/16 Th class24: Classical Object Detection [pdf]
14 04/21 Tu class25: Deep Object Detection [pdf] Zhao Review
Object Detection [pdf]		 HW08
04/23 Th class26: Deep Object Detection
15 04/28 Tu class27: Machine Learning and Neural Network Overview [pdf]
04/30 Th class28: quiz05 - Ch11+detection
16 05/05 Tu class29: Project Presentations
05/07 Th class30: Project Presentations
17 05/12 Tu Final Review -
05/14 Th Final