Graph Signal Processing

The goal of this lecture is that the students at the end of the lecture

- understand the basics of Graph Signal (GS) Representation and GS Processing based on spectral graph theory

- have an overview and technical depth of some methods for graph filtering and sampling

- are able to apply GSP methods to a range of areas including the analysis of distributed sensor networks and point clouds

This is a theory lecture. The lecture is given in English.

Contents

  • Short introduction to graph signals and node domain processing
  • Node domain graph filters
  • Graph Fourier Transform, Filtering,
  • Application of GFT to common operators
  • Graph Spectra
  • Graph Signal models, node domain sampling, frequency domain sampling, Conditions for reconstruction
  • Robust Graph spectral sampling
  • Applications of GSP to domains including transportation networks, sensor networks, point clouds, and learning with Graph Signals

Dates

Summer term

Lecture

  • TBA

Exercise

  • TBA

Materials

Materials will be distributed via Stud.IP

Exam

Written Exam (90 min)

Prüfungs- und Vorlesungsanmeldung


Ihr Dozent

Prof. Dr. Amr Rizk
Professors
Address
Appelstraße 9a
30167 Hannover
Building
Room
Prof. Dr. Amr Rizk
Professors
Address
Appelstraße 9a
30167 Hannover
Building
Room

Ihre Betreuerinnen und Betreuer

Michael Rudolph, M. Sc.
Research Staff
Address
Appelstraße 9a
30167 Hannover
Building
Room
Michael Rudolph, M. Sc.
Research Staff
Address
Appelstraße 9a
30167 Hannover
Building
Room