Description

This course provides an integrated introduction to signals, systems, stochastic processes, Fourier analysis, digital signal processing, control systems, and time-series modeling. The course begins with the characterization of continuous-time and discrete-time signals and systems, including LTI systems, Laplace and z-transform methods, block-diagram algebra, sampling, quantization, and filter design. It then develops the foundations of probability, random vectors, random processes, Gaussian processes, and spectral analysis of random signals. 

The course concludes with statistical signal processing, time-series forecasting, and stochastic state-space methods, with practical implementation in Python and MATLAB/Octave for engineering applications in signal analysis, estimation, detection, and control.

Recommended textbooks 

[1] Alan V Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing: Pearson New International, Edition 3, 2014, Person.
[2] Hwei Hsu, Schaum’s Outline of Signals and Systems, Edition 4, 2020, McGraw-Hill Education.
[3] George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung,Time Series Analysis: Forecasting and Control, Edition 5, 2015, Prentice Hall.