Stochastic resonance: theory and applications
Stochastic resonance: theory and applications
Digital Signal Processing Handbook
Digital Signal Processing Handbook
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
Nonlinear signal detection from an array of threshold devices for non-Gaussian noise
Digital Signal Processing
Noise-enhanced nonlinear detector to improve signal detection in non-Gaussian noise
Signal Processing - Special section: Distributed source coding
Nonlinear statistics to improve signal detection in generalized Gaussian noise
Digital Signal Processing
Learning data structures with inherent complex logic: neurocognitive perspective
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
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A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers, in which all the quantizer outputs are added to produce the array output. This parsimonious signal representation is used to implement an optimal detection from the output of the array. Such conditions can be relevant for fast real-time processing in large-scale sensor networks. We demonstrate that, even for suprathreshold input signals, the presence of independent noises added to the thresholds in the array, can lead to a better performance in the optimal detection. We relate these results to the phenomenon of suprathreshold stochastic resonance, by which nonlinear transmission or processing of signals with arbitrary amplitude can be improved by added noises in arrays.