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Thomas Grosges

Researcher at University of Technology of Troyes

Publications -  66
Citations -  2293

Thomas Grosges is an academic researcher from University of Technology of Troyes. The author has contributed to research in topics: Signal & Finite element method. The author has an hindex of 18, co-authored 65 publications receiving 1791 citations. Previous affiliations of Thomas Grosges include French Institute for Research in Computer Science and Automation & Centre national de la recherche scientifique.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
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A new image encryption scheme based on a chaotic function

Michael François, +3 more
- 01 Mar 2012 - 
TL;DR: The results of several statistical analysis about randomness, sensitivity and correlation of the cipher-images show that the proposed cryptosystem is efficient and secure enough to be used for the image encryption and transmission.
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Pseudo-random number generator based on mixing of three chaotic maps

Michael François, +4 more
- 01 Apr 2014 - 
TL;DR: A secure pseudo-random number generator three-mixer is proposed, which uses permutations whose positions are computed and indexed by a standard chaotic function and a linear congruence to create a secure cryptosystem.
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Fitting the optical constants of gold, silver, chromium, titanium, and aluminum in the visible bandwidth

Dominique Barchiesi, +1 more
- 01 Jan 2014 - 
TL;DR: In this paper, the complex number relative permittivities of Au, Ag, Al, Cr, and Ti from either Palik or Johnson and Christy experimental data in the visible domain of wavelengths are successfully fitted by using the result of the particle swarm optimization method with FDTD constraint, as a starting point for the Nelder-Mead method.
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Models of near-field spectroscopic studies: comparison between Finite-Element and Finite-Difference methods.

Thomas Grosges, +2 more
- 17 Oct 2005 - 
TL;DR: It is shown that an accurate description of the dispersion and of the geometry of the material must be included for a realistic modeling and that a grid size around rhoa approximately 4pia/lambda should be used in order to describe more accurately the confinement of the light around the nanostructures.