Applications of Digital Signal Processing


CMPE Degree: This course is Elective for the CMPE degree.

EE Degree: This course is Elective for the EE degree.

Lab Hours: 3 supervised lab hours and 0 unsupervised lab hours.

Technical Interest Group(s) / Course Type(s): Digital Signal Processing

Course Coordinator: Xiaoli Ma

Prerequisites: (ECE 3077, ISYE 3770, or MATH 3670) and ECE2026

Corequisites: None.

Catalog Description

Applications of DSP in speech, image processing, radar, pattern
recognition, and adaptive filtering requiring working software
implementations applied to the analysis of real signals.


Course Outcomes

  1. Apply Digital Signal Processing Theories to real life signals and data.
  2. Apply Machine Learning Methodologies to real life signals and data.
  3. Express signal processing systems in mathematical form.
  4. Write code describing a signal processing and machine learning system.
  5. Describe how signal processing and machine learning are used in applications (e.g., audio and digital image processing).

Student Outcomes

In the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.

1. ( P ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics

2. ( M ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors

3. ( LN ) An ability to communicate effectively with a range of audiences

4. ( LN ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts

5. ( LN ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives

6. ( P ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

7. ( LN ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Strategic Performance Indicators (SPIs)

Not Applicable

Course Objectives

Topical Outline

Review of Basic DSP and Machien Learning Methods
Pattern Recognition
Classification, Segmentation, and Clustering
Convolutional Neural Networks (CNNs)
Sequence Modeling and Recurrent Neural Networks (RNNs)
Various types of signals including: Speech, Image, Video, Medical, Radar, Lidar, Geophysical, Seismic, Health, Financial and Sonar