COM-303 / 6 credits

Teacher: Prandoni Paolo

Language: English


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

Keywords

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

Learning Prerequisites

Required courses

calculus, linear algebra

Recommended courses

Circuits 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 system 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

Resources

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/

Ressources en bibliothèque

Références suggérées par la bibliothèque

Notes/Handbook

lecture slides available for download at the beginning of the semester.

A complete online DSP MOOC is available on Coursera.

Websites

Moodle Link

Prerequisite for

adaptive signal processing, image processing, audio processing, advanced signal processing

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Signal processing for communications
  • Lecture: 4 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22