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Syllabus for

Academic year
TMA462 - Wavelet analysis  
 
Syllabus adopted 2014-02-17 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES

This course round is cancelled. This course round is given every other year. Is not given 2015/2016


Teaching language: English
Open for exchange students

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0101 Examination 7,5 c Grading: TH   7,5 c    

In programs

MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)

Examiner:

Bitr professor  Mohammad Asadzadeh



  Go to Course Homepage

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Basic Fourier analysis

Aim

Fourier analysis (frequency analysis) is an indis-pensable tool for deterministic and statistical signal analysis and processing (and in the theory of partial differential equations). Presently, the so-called wavelet transform is widely used as a complement to traditional Fourier transforms. The aim of the course is, in part, to describel how these trans-forms are used in practice, e.g., for 'sampling' of signals, in antenna theory, in geometrical topics, in computer homography, in probability theory, and also the 'fast' transforms which are now executed by computers in this context, for example in image processing.

Learning outcomes (after completion of the course the student should be able to)

- understand the relevant terminology, so as to be able to read reports and research papers on applied Fourier analysis, - give definitions of the different transforms treated in the course, and state the conditions for their applicability, - apply Fourier transform methods in different areas of mathematics - write simple computer implementations (e.g. for Matlab) of transforms, and use them for signal processing.

Content

The course deals with the Fourier transform and some related transforms, such as wavelet transforms, Hankel and Radon transforms, discrete transforms and fast evaluation of transforms. In particular issues concerning discretization (sampling, for example) and the application to signal and image processing are treated. Generalized functions (distributions) and other fundamental mathematical tools for Fourier analysis are also treated.

Literature

See the course web page

Examination

Written examination and exercises handed in, partly based on computer calculations.


Page manager Published: Mon 28 Nov 2016.