Code: 11MAI-E ITS Mathematical Tools
Lecturer: Dr. Ing. Jan Přikryl Weekly load: 2P+2C Completion: A, EX
Department: 16111 Credits: 4 Semester: W
Description:
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.
Contents:
See https://zolotarev.fd.cvut.cz/mni/
Seminar contents:
See https://zolotarev.fd.cvut.cz/mni/
Recommended literature:
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. ?
Keywords:
Signal, DFT, STFT, localization, spectrogram, ODE, PDE, statistical lerning, mathematical models of traffic flow.

Abbreviations used:

Semester:

Mode of completion of the course:

Weekly load (hours per week):