Signal processing and probability - 3AMMA361

Goal(s)

Skills to know at the end of the class:

• Basic understanding of signal theory and signal processing
• Ability to design and to characterize linear time invariant systems and continuous and discreet signals
• Ability to implement basic methods of processing like spectral analysis, sampling, filtering, correlation.

Content(s)

Chapters
1. Introduction to signal processing

• Issues, signals classification, practical examples of signal processing

2. Continuous deterministic signals

• Time representation and frequency representation
• Fourier transform and Fourier series

3. Filtering and spectrum analysis

• Convolution and correlation operators
• Energy aspects and Parseval theorem
• Frequency filters and apodization

4. Sampling

• Shannon theorem
• Limits in practice and anti-aliasing filter

5. Discrete deterministic signals

• Basic transforms: Tz, dFT, DFT, FFT
• Convolution and correlation in discrete time

6. Introduction to digital filtering

• Finite impulse filters (FIR)
• Infinite impulse filters (IIR)

Laboratory
Session 1: Introduction to Matlab/Simulink
Session 2: Spectrum observation
Session 3: Introduction to digital filters

Mathematics and probability (degree level)