COM-514 / 6 credits

Teacher(s): Fageot Julien René Pierre, Simeoni Matthieu Martin Jean-André, Bejar Haro Benjamin

Language: English


Summary

Signal processing tools are presented from an intuitive geometric point of view which is at the heart of all modern signal processing techniques. The student will develop the mathematical depth and rigor needed for the study of advanced topics in signal processing and approximation theory.

Content

Learning Prerequisites

Required courses

Signal processing for communications (or Digital signal processing on Coursera)

Linear Algebra I and II (or equivalent). 

Recommended courses

Signals and Systems

 

Important concepts to start the course

Good knowledge of linear algebra concepts. Basics of Fourier analysis and signal processing. Good knowledge of Python and its scientific packages (Numpy, Scipy).

 

 

Learning Outcomes

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

  • Master the right tools to tackle advanced signal and data processing problems
  • Develop an intuitive understanding of signal processing through a geometrical approach
  • Get to know the applications that are of interest today
  • Learn about topics that are at the forefront of signal processing research
  • Identify and implement the algorithm best suited to solve a given convex optimisation problem
  • Assess the computational cost and numerical stability of a numerical solver

Transversal skills

  • Collect data.
  • Write a scientific or technical report.
  • Use a work methodology appropriate to the task.
  • Demonstrate the capacity for critical thinking
  • Use both general and domain specific IT resources and tools

Teaching methods

Ex cathedra with exercises, homeworks and practicals.

Expected student activities

Attending lectures, completing exercises.

Assessment methods

homeworks and project assignement 50%, final exam (written) 50%

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

M. Vetterli, J. Kovacevic and V. Goyal, "Signal Processing: Foundations", Cambridge U. Press, 2014.

Available in open access at http://www.fourierandwavelets.org

Ressources en bibliothèque

Moodle Link

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Mathematical foundations of signal processing
  • Lecture: 3 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     
14-15INM203    
15-16INM203   INM203
16-17   
17-18     
18-19     
19-20     
20-21     
21-22     

Friday, 15h - 17h: Lecture INM203

Monday, 14h - 15h: Lecture INM203

Monday, 15h - 17h: Exercise, TP INM203