Signal and Image Processing (CORO SIP)



Master Supervisors: Guy Lebret (M1), Sébastien Bourguignon (M2) and Olivier-Henri Roux

Overview

The Signal and Image Processing (SIP) programme provides the necessary skills in signal modelling, image processing and machine learning, relevant to the theory and the practice of data analysis and information retrieval, for the development of modern numerical methods.

The courses of SIP programme address the theory and the practice of advanced data analysis techniques, from computational statistics, applied mathematics, scientific computing and numerical imaging, to their practical implementation in several fields such as biomedical engineering, imaging science, audio processing and information technology.

The key feature of the programme is the design of mathematical solutions, for signal and image processing problems, accounting for the physical specificities of this data and adapting the numerical implementation of these solutions to the application context, the data amount and the available computational resources.



The programme of study lasts two academic years - denoted by M1 and M2. Signal and Image Processing is one of four specialisms available within the Control and Robotics stream. Some of the M1 courses are the taught across the four specialisms whereas the M2 courses are specialism-specific. See course content for more details. 

The language of instruction is English across the two years.
This Master’s programme is accredited by the French Ministry of Higher Education.

Skills developed

  • Establish a relevant statistical model for data representation and analysis
  • Propose a methodological solution and its numerical implementation suited to the application context
  • Acquire a solid background on real-life applications of signal and image processing in research and innovation

In addition to the above specialism-specific skills, students will also develop more general skills:

  • Identify models, perform simulations and analyse results
  • Undertake a literature survey of existing works on a scientific problem
  • Communicate comprehensive results in a meaningful way
  • Manage and supervise research and innovation projects

Course Content - M1


30 ECTS Credits per semester.
 
Autumn Semester Courses ECTS Spring Semester Courses ECTS
Signal Processing  5 Group Project 6
Classical Linear Control 5 Optimization Techniques  4
Artificial Intelligence 4 Mobile Robots
 		 4
Embedded Electronics 4 Programming Real Time Systems 4
Systems Identification and Signal Filtering 4 Computer Vision 4
Embedded Computing 4 Spectral and Time Frequency Analysis 4
Modern Languages * 4 Modern Languages * 4

* 'French as Foreign Language' except for French native speakers who will study 'Cultural and Communicational English'
NB Course content may be subject to minor changes

Course Content - M2

 
30 ECTS Credits per semester
 
Autumn Semester Courses ECTS
Statistical signal processing and estimation theory  4
Digital signal and image representations  4
Machine learning, data analysis and information retrieval  4
Signal and image restoration, inversion methods  4
Mathematical tools for signal and image processing  4
Biomedical signals, images and methods  4
Modern Languages *  4
Project  2
Conferences  -

* 'French as Foreign Language' except for French native speakers who will study 'Cultural and Communicational English'
 
Spring Semester ECTS
Master Thesis/Internship                                                  30


Examples of previous internships in Medicine:
 
  • Analysis of Electromyographic signals for neuromuscular disease characterization
  • Reconstruction of Positron Emission Tomography images in the context of low statistics
  • Resolution enhancement in Magnetic Resonance Imaging for cardiovascular diagnosis
 

Examples of previous internships in industry:

  • Optimization of a tyre pressure monitoring system in an automotive vehicle
  • Fast imaging algorithm for structured illumination microscopy
 

Examples of previous internships in research labs:

  • Numerical optimization for sparse ultrasound signal recovery
  • Analysis and classification of environmental sounds using deep learning methods

NB Course content may be subject to minor changes

Prospects for employment or further study:

 
  • Sectors: health, communication, technology, transportation
  • Fields: biomedical engineering, industrial imaging, audio engineering, computer science, Applied mathematics, research and innovation
Published on March 25, 2017 Updated on December 16, 2020