ECE 6534: Advanced Digital Signal Processing II

Course Description

Data permeates every part of society. As a result, there have been many advances on how to effectively process this data. These advances make use of concepts from many disciplines, including linear algebra, representation theory, network theory, and machine learning. These methods provide technical disciplines with many benefits: quicker data processing, more effective information extraction, and better data fusion from multiple sources. These new ideas also form the framework for solving some the greatest technical grand challenges of today (see the National Academy of Engineering's Grand Challenges). We explore these new advances, new methods, and new ideas throughout this course.

We begin the course by exploring signal processing from a perspective different than is traditionally taught: a geometric (or linear algebra based) perspective. We discuss this perspective because it forms the basis for much of modern signal processing. We then utilize our new perspective to learn about several of the biggest signal processing topics from the past 10 years. In this part of the course, you will help direct the course. You will learn these subjects through hands-on projects and research and present them to your peers. Topics may include: multidimensional signal processing, multilinear signal processing, wavelets, big data, optimization, compressive sensing, signal processing over graphs, and machine learning.

Textbooks

Foundations of Signal Processing
by Martin Vetterli, Jelena Kovacevic, and Vivek Goyal

Fourier and Wavelet Signal Processing
by Jelena Kovacevic, Vivek Goyal, and Martin Vetterli

Downloadable from the author's website
or purchasable (first book only) from Amazon

Learning Objectives

At the completion of this course, you should be able to:

1. Describe signal processing concepts from a geometric perspective
2. Discuss new, cutting-edge signal processing methods
3. Apply cutting-edge signal processing techniques to real world problems

Grade Distribution: