François Chapeau-Blondeau

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The optimal detection of a signal of known form hidden in additive white noise is examined in the framework of stochastic resonance and noise-aided information processing. Conditions are exhibited where the performance in the optimal detection increases when the level of the additive (non-Gaussian bimodal) noise is raised. On the additive signal–noise(More)
PURPOSE The cardiovascular system (CVS) regulation can be studied from a central viewpoint, through heart rate variability (HRV) data, and from a peripheral viewpoint, through laser Doppler flowmetry (LDF) signals. Both the central and peripheral CVSs are regulated by several interacting mechanisms, each having its own temporal scale. The central CVS has(More)
—We address the problem of synthesizing a generalized Gaussian noise with exponent 1/2 by means of a nonlinear memoryless transformation applied to a uniform noise. We show that this transformation is expressable in terms of a special function known under the name of the Lambert W function. We review the main methods for numerical evaluation of the relevant(More)
—A novel instance of a stochastic resonance effect, under the form of a noise-improved performance, is shown to be possible for an optimal Bayesian estimator. Estimation of the frequency of a periodic signal corrupted by a phase noise is considered. The optimal Bayesian estimator, achieving the minimum of the mean square estimation error, is explicitly(More)
A stochastic resonance eeect, under the form of a noise-improved performance, is shown possible for an optimal detector. This is established with a nonlinear signal–noise mixture where the noise acts on the phase of a periodic signal. The optimal detector, achieving minimal probability of detection error, is explicitly derived. Conditions are exhibited(More)
A stochastic resonance effect, under the form of a noise-improved performance, is shown feasible for a whole range of optimal detection strategies, including Bayesian, minimum error-probability, Neyman–Pearson, and minimax detectors. In each case, situations are demonstrated where the performance of the optimal detector can be improved (locally) by raising(More)
We analyze the parametric estimation that can be performed on a signal buried in noise based on the parsimonious representation provided by a parallel array of threshold devices. The Fisher information contained in the array output about the input parameter is used as the measure of performance in the estimation task. For estimation on a suprathreshold(More)
We compare the performance of two detection schemes in charge of detecting the presence of a signal buried in an additive noise. One of these is the correlation receiver (linear detector), which is optimal when the noise is Gaussian. The other detector is obtained by applying the same correlation receiver to the output of a nonlinear preprocessor formed by(More)