Master in Control and Robotics in Signal and Image Processing (CORO SIP)

General

Read more about this program on the school's website

Program Description

Signal and Image Processing (CORO SIP)

The Signal and Image Processing (SIP) program 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 program 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 program of study lasts two academic years - denoted by M1 and M2. Signal and Image Processing is one of five specialisms available within the Control and Robotics stream. Some of the M1 courses are taught across the five specialisms whereas the M2 courses are specialism-specific.

The language of instruction is English across the two years.

Curriculum

Course Content - M1
30 ECTS Credits per semester.
Language of instruction: English
M1 - Autumn Semester Courses ECTS M1 - 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

Course Content - M2

30 ECTS Credits per semester.
Language of instruction: English

Autumn Semester Courses ECTS Spring Semester ECTS
Statistical signal processing and estimation theory 4 Master Thesis or Industrial internship 30
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 -

NB Course content may be subject to minor changes

Internship

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 the industry:

  • Optimization of a tire 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.

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 in 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 analyze 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.

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.
  • Positions: data analyst, research scientist, process engineer, design engineer, research and innovation engineer (post Ph.D.)

Faculty and Research Facilities

This Master relies on Centrale Nantes’ faculty and the research facilities of the LS2N Laboratory.

Partnerships

Total, Renault, Nantes University Hospital (CHU).

Last updated May 2020

About the School

Founded in 1919, Centrale Nantes (ECN) is among the top higher education and research institutions in France in Science & Engineering. Its purpose is to develop top-level scientists & engineer ... Read More

Founded in 1919, Centrale Nantes (ECN) is among the top higher education and research institutions in France in Science & Engineering. Its purpose is to develop top-level scientists & engineers in multidisciplinary and specific fields from 2150 students per year in engineering track (5 years), Master Degrees and PhDs. International development is at the heart of the strategic policy of Centrale Nantes: 100% of its engineering students are going abroad and 30% of the campus population is international. Read less