ITS Mathematical Tools

Course guarantor:

Jan Přikryl

Lecturer:

Jan Přikryl

Tutor:

Jan Přikryl

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:

See https://zolotarev.fd.cvut.cz/mni/

Syllabus of tutorials:

See https://zolotarev.fd.cvut.cz/mni/

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

James, G., Witten, D., Hastie, T., & 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., & 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., & Chen, Y. T. (2019). Computational partial differential equations using MATLAB®. 2nd edition. CRC press.

Note:

Further information:

https://zolotarev.fd.cvut.cz/mni/

Time-table for winter semester 2024/2025:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon	roomKO:105 Přikryl J. 13:15–14:45 (lecture parallel125) Konviktská 20 roomKO:105 Přikryl J. 15:00–16:30 (parallel nr.125) Konviktská 20
Tue
Wed
Thu
Fri

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Master Full-Time IS (CS) from 2022/23 (compulsory course)
• Master Full-Time IS (CS) from 2023/24 (compulsory course)
• Master Full-Time IS (CS) from 2024/25 (compulsory course)
• Master Full-Time IS (CS) from 2024/25 (compulsory course)