COM-303 / 6 crédits

Enseignant: Prandoni Paolo

Langue: Anglais

## Summary

Students learn digital signal processing theory, including discrete time, Fourier analysis, filter design, adaptive filtering, sampling, interpolation and quantization; they are introduced to image processing and data communication system design.

## Content

1. Basic discrete-time signals and systems: signal classes and operations on discrete-time signals, signals as vectors in Hilbert space
2. Fourier Analysis: properties of Fourier transforms, DFT, DTFT; FFT.
3. Discrete-Time Systems: LTI filters, convolution and modulation; difference equations; FIR vs IIR, stability issues.
4. Z-transform: properties and regions of convergence, applications to linear systems.
5. Filter Design: FIR design methods, IIR design methods, filter structures.
6. Stochastic and Adaptive Signal Processing: random processes, spectral representation, Optimal Least Squares adaptive filters.
7. Interpolation and Sampling: the continuous-time paradigm, interpolationthe sampling theorem, aliasing.
8. Quantization: A/D and D/A converters.
9. Multi-rate signal processing: upsampling and downsampling, oversampling.
10. Multi-dimensional signals and processing: introduction to Image Processing.
11. Practical applications: digital communication system design, ADSL.

## Keywords

Signal processing, discrete-time, continuous-time, filter, filter design, sampling, aliasing, DSP, Fourier transform, FFT, modem, ADSL

## Required courses

• Calculus
• Linear Algebra

## Recommended courses

• Signals and systems
• Basic probability theory

## Important concepts to start the course

Vectors and vector spaces, functions and sequences, infinite series

## Learning Outcomes

By the end of the course, the student must be able to:

• Identify signals and signal types
• Recognize signal processing problems
• Apply the correct analysis tools to specific signals
• Check systems stability
• Manipulate rational transfer functions
• Implement signal processing algorithms
• Design digital filters
• Interpret complex signal processing systems

## Transversal skills

• Use a work methodology appropriate to the task.
• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Use both general and domain specific IT resources and tools

## Teaching methods

Course with exercises sessions and coding examples and exercises in Python (Jupyter Notebooks)

## Expected student activities

Complete weekly homework, explore and modify Jupyter Notebook examples

## Assessment methods

Final exam fully determines final grade.

## Supervision

 Office hours Yes Assistants Yes Forum Yes

## Bibliography

Signal processing for Communications, EPFL Press, 2008, by P. Prandoni and M. Vetterli. The book is available for sale in printed form online and in bookstores; in iBook format on the Apple store and is also available as a free pdf file at http://www.sp4comm.org/

## Notes/Handbook

A complete online DSP MOOC is available on Coursera.

## Dans les plans d'études

• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Signal processing for communications
• Cours: 4 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Signal processing for communications
• Cours: 4 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: optionnel
• Semestre: Printemps
• Forme de l'examen: Ecrit (session d'été)
• Matière examinée: Signal processing for communications
• Cours: 4 Heure(s) hebdo x 14 semaines
• Exercices: 2 Heure(s) hebdo x 14 semaines
• Type: obligatoire

## Cours connexes

Résultats de graphsearch.epfl.ch.