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David Rousseau

Researcher at University of Angers

Publications -  183
Citations -  2935

David Rousseau is an academic researcher from University of Angers. The author has contributed to research in topics: Stochastic resonance & Noise (signal processing). The author has an hindex of 24, co-authored 176 publications receiving 2374 citations. Previous affiliations of David Rousseau include University of Lyon & Institut national de la recherche agronomique.

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Generalized fractal dimensions of laser Doppler flowmetry signals recorded from glabrous and nonglabrous skin

TL;DR: The findings suggest that the multifractality of the normalized LDF signals is different on glabrous and nonglabrous skin, and the complexity in the hand palms could be more important at scales corresponding to the myogenic control mechanism than at the other studied scales.
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Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke.

TL;DR: The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge‐preserving spatial regularization for the deconvolved of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke.
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Impact of the reperfusion status for predicting the final stroke infarct using deep learning.

TL;DR: In this paper, the authors trained and tested CNNs to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in a rehabilitation center.
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CTIS-Net: A Neural Network Architecture for Compressed Learning Based on Computed Tomography Imaging Spectrometers

TL;DR: In this article, a new convolutional neural network (CNN) architecture called CTIS-Net is proposed to benefit from the specific structure of CTIS images, which is tailored to the reconstruction of a hyperspectral cube.
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Nonlinear Devices Acting as SNR Amplifiers for a Harmonic Signal in Noise

TL;DR: Simple nonlinearities, easily implementable as electronic circuits, are shown capable of producing an amplification of the signal-to-noise ratio (SNR) by the nonlinearity.