TU Delft
Year
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NEDERLANDSENGLISH
Organization
2016/2017 Electrical Engineering, Mathematics and Computer Science Master Electrical Engineering
EE4540
Distributed Signal Processing
ECTS: 5
Responsible Instructor
Name E-mail
Dr.ir. R. Heusdens    R.Heusdens@tudelft.nl
Instructor
Name E-mail
Dr. G. Zhang    G.Zhang-1@tudelft.nl
Contact Hours / Week x/x/x/x
0/0/4/0
Education Period
3
Start Education
3
Exam Period
none
Course Language
English
Course Contents
In the course Distributed Signal Processing, attention will be paid to decentralized signal processing techniques. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. In industry, this trend has been referred to as ‘Big Data’, and it has had a significant impact in areas as varied as artificial intelligence, internet applications, computational biology, medicine, finance, marketing, journalism, network analysis, weather forecast, telecommunication, and logistics. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable.

In this course we will focus on two signal processing techniques for decentralized processing: one based on graphical models and one based on convex optimization. We will consider the following topics: graphical models, probabilistic inference, message passing, min-sum/max-product algorithm, Jacobi and Gauss-Seidel algorithm, convex optimization, gossip algorithm, dual ascent, dual decomposition and alternating direction method of multipliers (ADMM).
Study Goals
1. Graphical models
Keywords: Graphical models, trees, loopy graphs, conditional independence, Markov random fields, probabilisic inference, maximum a posteriori estimation, min-sum algorithm, linear coordinate descent algorithm, Jacobi algorithm, Gauss-Seidel algorithm, over-relaxation.

The student
• is able to explain the use of graphical models in decentralized signal processing
• is able to discuss the structure and principles of probabilistic inference algorithms in graphical models
• is able to discuss message-passing techniques
• is able to implement state-of-the-art message-passing algorithms for decentralized signal processing

2. Convex optimization
Keywords: Convex optimization, convex sets, convex functions, dual function, KKT conditions, gossip algorithm, duals ascent algorithm, dual decomposition, augmented Lagrangian, alternating direction method of multipliers (ADMM).

The student
• is able to explain the use of convex optimization in decentralized signal processing
• is able to discuss the structure and principles of convex optimization techniques
• is able to discuss primal and dual properties
• is able to implement state-of-the-art convex optimization algorithms for decentralized signal processing
Education Method
lectures, mini projects + hands-on exercises
Assessment
Written examination + evaluation of the mini projects