Mathematical Methods of Visual Computing VU

Aim

The objective of this course is to teach the (mathematical) basics for continuative methods (and courses). Successful students should understand the lecture content and be able to apply the taught methods in practice. They should know the pros and cons of the considered approaches and be able to choose an appropriate method for a given problem.

Subject

This course consists of two different parts with regard to contents: signals (taught by Prof. Grünbacher) and optimization (taught by Dr. Prandtstetter).
The signal-part primarily discusses spectra, DFT/FFT, sampling theorem, FIR filter, z-transformation, and IIR filter.

Exams for lecture part 1 "Signals" are possible until 17 January 2014. Registration via TISS.
Herbert Grünbacher


On the other hand, the optimization-part introduces linear programming (simplex method), the A* algorithm, approximation and randomization algorithms, and/or network flows.

Lecturer

Univ.Prof. Dipl.-Ing. Dr.rer.nat. Radu Grosu  (E182)
Prandtstetter, Matthias; Mag.rer.soc.oec. Dr.techn. Dipl.-Ing.  (E186)

Homepage

Homepage for signals: http://ti.tuwien.ac.at/rts/teaching/courses/mmvc
Homepage for optimization: https://www.ads.tuwien.ac.at/w/WS12/186849_Mathematische_Methoden_des_Visual_Computing_VU_3.0

Teaching material for signals

Kapitel z-Tranform + IIR kurz

Lecture slides
Problems for home work

Further reading:

James H. McClellan, Ronald Schafer, Mark Yoder:

Signal Processing First
Prentice Hall

John G. Proakis, Dimitris G. Manolakis: 
Digital Signal Processing
Prentice Hall

Vinay K. Ingle, John G. Proakis:
Digital Signal Processing Using MATLAB
Thomson Learning