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.
Course Content - M1
30 ECTS Credits per semester.
Language of instruction: English
M1 - Autumn Semester Courses
M1 - Spring Semester Courses
Classical Linear Control
Programming Real-Time Systems
Systems Identification and Signal Filtering
Spectral and Time-Frequency Analysis
Course Content - M2
30 ECTS Credits per semester.Language of instruction: English
Autumn Semester Courses
Statistical signal processing and estimation theory
Master Thesis or Industrial internship
Digital signal and image representations
Machine learning, data analysis, and information retrieval
Signal and image restoration, inversion methods
Mathematical tools for signal and image processing
Biomedical signals, images, and methods
Modern Languages *
NB Course content may be subject to minor changes
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.
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.
Total, Renault, Nantes University Hospital (CHU).