Stochastic resonance: theory and applications
Stochastic resonance: theory and applications
Detection of Signals in Noise
On the use of stochastic resonance in sine detection
Signal Processing
Stochastic resonance for an optimal detector with phase noise
Signal Processing
Design of detectors based on stochastic resonance
Signal Processing
Noise-enhanced performance for an optimal Bayesian estimator
IEEE Transactions on Signal Processing
Stochastic resonance in discrete time nonlinear AR(1) models
IEEE Transactions on Signal Processing
Stochastic resonance in sequential detectors
IEEE Transactions on Signal Processing
Neural signal-detection noise benefits based on error probability
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Noise enhanced hypothesis-testing in the restricted Bayesian framework
IEEE Transactions on Signal Processing
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
Digital Signal Processing
Effects of multiscale noise tuning on stochastic resonance for weak signal detection
Digital Signal Processing
Weak signal detection: Condition for noise induced enhancement
Digital Signal Processing
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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 the level of the noise. This is obtained with a nonlinear signal-noise mixture where a non-Gaussian noise acts on the phase of a periodic signal.