389.186 Signal Processing for Big Data
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2019S, SE, 2.0h, 3.0EC

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: SE Seminar

Aim of course

Students are to understand signal processing issues in the big data domain and they will learn and apply specific signal processing methods in this field.

Apart from the technical knowledge in big data, students will learn concepts of how to acquire knowledge in a novel and highly theoretical field of research which will help them, e.g., to prepare for research within a PhD programme.

Subject of course

Students will

  • start on a topic with some introductory references
  • find more advanced literature and identify key papers
  • understand principles, methods and potential applications
  • give presentations (25mins + 5mins discussion); talks & slides in English
  • possibly do some Matlab/C-Programming if required for the topic

A list with topics will be posted here on 1 April 2019

https://www.nt.tuwien.ac.at/wp-content/uploads/2019/04/sem.pdf

Short discussion of topics on 4 April 2019, 14:00, in room CG0402.

Deadline: 12 April 2019 (email to norbert.goertz@nt.tuwien.ac.at) for a ranked list of   three  preferred topics.

Dates for seminar presentations: 6 June 2019, from 14:00

(also 14 June, 14:00, if required)

Other presentation dates possible, by mutual agreement (email norbert.goertz@nt.tuwien.ac.at)

Additional information


Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: Directive concerning the handling of plagiarism (PDF)

Lecturers

Institute

Examination modalities

seminar presentation (presentation slides are a deliverable and, if applicabe, matlab code and processed data for verification of results).

Course registration

Not necessary

Curricula

Study CodeObligationSemesterPrecon.Info
710 FW Elective Courses - Electrical Engineering Elective

Literature

No lecture notes are available.

Previous knowledge

Discrete-time signal processing

Language

English