Inhalt

[ 404MMENWFAU23 ] UE Wavelets – Functional Analytical Basics

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M - Master's programme Mathematics Ronny Ramlau 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computational Mathematics 2025W
Learning Outcomes
Competences
The students extract from Fourier- or wavelet transform (or Fourier- and Wavelet series decompositions) of a time series information from the time-frequency behavior of the function. Using the discrete Wavelet Transform they can compress data.
Skills Knowledge
  • Compute the Fourier/ windowed Fourier/Wavelet Transform of a function
  • decompose and reconstruct a function with respect to a frame
  • decompose/reconstruct a function with respect to a orthogonal wavelet basis
  • Signal- and image compression using the discrete wavelet transform
none
Criteria for evaluation Presentation of exercises at blackboard and presentation of projects
Language English
Changing subject? No
Earlier variants They also cover the requirements of the curriculum (from - to)
403MMIEWFAU22: UE Wavelets – Functional Analytical Basics (2022W-2023S)
On-site course
Maximum number of participants 25
Assignment procedure Direct assignment