Applications of Digital Signal Processing

(3-0-3-4)

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

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

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

Technical Interest Groups / Course Categories: Threads / ECE Electives

Course Coordinator: Xiaoli Ma

Prerequisites: ECE 4270 [min D, with concurrency]

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.

Textbook(s)

Course Outcomes

Apply Digital Signal Processing Theories to real life signals and data.

Apply Machine Learning Methodologies to real life signals and data.

Express signal processing systems in mathematical form. 

Write code describing a signal processing and machine learning system.

Describe how signal processing and machine learning are used in applications (e.g., audio and digital image processing).

Strategic Performance Indicators (SPIs)

N/A

Topic List

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