CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

# ITS Mathematical Tools

The course is not on the list Without time-table
Code Completion Credits Range
11MAI Z,ZK 4 2P+2C
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Applied Mathematics
Synopsis:

Series, Fourier Series. Discrete Fourier Transform. Segmentation of signals, windows, localization. Short-term Fourier Transform. From Fourier Analysis to PDE. Fundamentals of Numerical Mathematics. Numerical solutions to ODEs and PDEs. Continuous traffic flow models described by PDE. Car-following models as ODEs.

Requirements:

Entry requirements: see https://zolotarev.fd.cvut.cz/mni/ -

among other things, knowledge of basic operations with polynomials, functions of complex variable, calculations of sums of infinite and functional series, concepts of discrete and continuous signal, signal sampling, system input / output, spectrum. Basic knowledge of statistical learning methods in the scope of the subject 11MAMY. Working knowledge of the MATLAB system resp. python + NumPy + SciPy + pandas + statmodels.

Requirements for passing the course: see https://zolotarev.fd.cvut.cz/mni/ - submission of independently prepared homework assignments (etronically, dates published on the fly during lectures and on the web), submission of semester work no later than by the end of August.

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Mastering Fourier series for signal analysis, use of STFT for non-stationary signals, knowledge of the use of spectrograms. Fundamentals of numerical solution of ordinary and partial differential equations occurring in traffic flow models.

Study materials:

Kovacevic, J., Goyal, V. K., &amp; Vetterli, M. (2013). Fourier and wavelet signal processing. Fourier Wavelets.org, 294pp. With permission of authors the preprint is available from our webpage as PDF here.

Broughton, S. A., &amp; Bryan, K. (2018). Discrete Fourier analysis and wavelets: applications to signal and image processing. 2nd edition. John Wiley &amp; Sons.

James, G., Witten, D., Hastie, T., &amp; Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer. Electronic version, errata, and supplementary material available from https://www.statlearning.com/.

Friedman, J., Hastie, T., &amp; Tibshirani, R. (2009). The elements of statistical learning. Springer Series in Statistics. 2nd edition. New York: Springer. Electronic version, errata, and supplementary material available from https://web.stanford.edu/~hastie/ElemStatLearn/.

Heath, M. T. (2018). Scientific Computing: An Introductory Survey, Revised Second Edition. 2nd externed edition. Society for Industrial and Applied Mathematics.

Li, J., &amp; Chen, Y. T. (2019). Computational partial differential equations using MATLAB®. 2nd edition. CRC press.

Note:
Further information:
https://zolotarev.fd.cvut.cz/mni/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-04-21
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet1925706.html