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

  • 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

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

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

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

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11 CM3   
11-12    
12-13     
13-14AAC231    
14-15  INM201
INM202
 
15-16    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Monday, 13h - 15h: Lecture AAC231

Tuesday, 10h - 12h: Lecture CM3

Thursday, 14h - 16h: Exercise, TP INM201
INM202

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