Advanced Digital Signal Processing


CMPE Degree: This course is Not Applicable for the CMPE degree.

EE Degree: This course is Not Applicable for the EE degree.

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

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

Course Coordinator:

Prerequisites: ECE 4270

Corequisites: None.

Catalog Description

An introduction to advanced signal processing methods that are
used in a variety of application areas.

Course Outcomes

Not Applicable

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. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics

2. ( Not Applicable ) 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. ( Not Applicable ) An ability to communicate effectively with a range of audiences

4. ( Not Applicable ) 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. ( Not Applicable ) 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. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Strategic Performance Indicators (SPIs)

Not Applicable

Course Objectives

Topical Outline

Basic Signals and Systems
Review of 1-D signals
Review of random signals
Multi-D signals

Multirate Signal Processing
Interpolation and Decimation
Sample Rate Conversion
Oversampled Processing (A/D and D/A conversion)

Time-Frequency Representations
Short-Time Fourier Transform
Wigner-Ville Decomposition
1-D and 2-D Transforms (DCT, DST, KLT)

Linear Prediction
Autoregressive Modelling and Least Squares
Modelling Random Signals
Prony's Method

Inverse Problems (Signal Reconstruction)
Underdetermined Least Squares
Pseudo-Inverse (SVD)
Min-Norm Solutions
Regularized Methods
Reconstruction from Projections
Iterative Methods
Projection onto Convex Sets
Simulated Annealing
Reconstruction from Nonuniform Sampling
1-D and Multi-D Sampling
Random Sampling

Optimal Quantization
Lloyd-Max Quantizers
Vector Quantization