Course content

The course presents a mathematical introduction to the wavelet theory: Continuous and discrete wavelet transform, wavelet base and wavelet packages, wavelets and singular integrals. Applications related for example to signal analysis, image processing, numerical analysis will be also discussed.

Learning outcome

1. Knowledge. The course presents a mathematical introduction to the wavelet theory: Continuous and discrete wavelet transform, wavelet base and wavelet packages, wavelets and singular integrals. Applications related for example to signal analysis, image processing, numerical analysis will also be discussed. 2. Skills The students should be able to handle problems and conduct researches related to theoretical and applied problems related to wavelet theory, and, more generally, time-frequency analysis. In particular techniques connected with signal and image processing, data banks should be studied. 3. Competence The students should be able to participate in scientific discussions and conduct researches on high international level in wavelet theory and its applications as well as to collaborate in joint interdisciplinary researches.

Learning methods and activities

Lectures, alternatively guided self-study.

The course is taught every second year, if there are enough students, next time Fall semester 2023. If there are few students, there will be guided self-study.

Recommended previous knowledge

Fourier analysis course, or at least Mathematics 4.

Course materials

Will be announced at the start of the course.