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Lecturer(s)
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Vodák Rostislav, RNDr. Ph.D.
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Fišerová Eva, doc. RNDr. Ph.D.
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Ženčák Pavel, RNDr. Ph.D.
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Fürst Tomáš, RNDr. Ph.D.
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Course content
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1. Fourier methods and their application in digital music, sound processing 2. Fourier methods and their application in PDE 3. Boundary value problems -- an overview. Application to linear elasticity 4. Introduction to image processing 5. Introduction to analysis of biological signals. Application to ECG data
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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This course aims at several more complex problems from practice which require the use of mathematical tools from several different branches of mathematics.
The ability to solve more complex practical problems
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Prerequisites
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Linear algebra, calculus, basic numerical mathematics, programming skills, English
KMA/MA1 and KMA/MA2 and KMA/MA3 and KAG/LA1A and KMA/DR
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Assessment methods and criteria
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Student performance, Analysis of Creative works (Music, Pictorial,Literary), Seminar Work
Colloquiu: active participation. presentation of a solution to a selected more complex problem
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Recommended literature
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Dave Benson. (2006). Music: A Mathematical Offering. Cambridge University Press.
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Nathan Kutz. (2013). Data Driven Modeling & Scientific Computation. Oxford University Press.
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Peter J. Brockwell, Richard A. Davis. (2009). Time Series: Theory and Methods. Springer Series in Statistics.
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Rafael C. Gonzalez, Richard E. Woods. (2017). Digital Image Processing. Pearson.
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T. W. Körner. (1988). Fourier Analysis. Cambridge University Press; 1 edition.
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