Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
On the use of stochastic resonance in sine detection
Signal Processing
Erratum: Stochastic resonance in discrete time nonlinear AR(1)models
IEEE Transactions on Signal Processing
Nonlinear signal detection from an array of threshold devices for non-Gaussian noise
Digital Signal Processing
Perturbative corrections to stochastic resonant quantizers
Signal Processing - Special section: Distributed source coding
A neurocomputational model of stochastic resonance and aging
Neurocomputing
Fisher information and noise-aided power estimation from one-bit quantizers
Digital Signal Processing
Nonlinear statistics to improve signal detection in generalized Gaussian noise
Digital Signal Processing
Stochastic resonance in sequential detectors
IEEE Transactions on Signal Processing
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection
IEEE Transactions on Signal Processing
Stochastic resonance and improvement by noise in optimal detection strategies
Digital 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
On optimal threshold and structure in threshold system based detector
Signal Processing
An adaptive stochastic-resonance-based detector and its application in watermark extraction
WSEAS Transactions on Signal Processing
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
Digital Signal Processing
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This paper presents a study of the phenomenon of stochastic resonance in quantizers, and discusses the use of this phenomenon for the detection of weak sinusoidal signals in noise. Stochastic resonance in 2-level, symmetric 3-level, and symmetric multilevel quantizers is investigated. Expressions are derived for the signal-to-noise ratio (SNR) gain of the quantizers driven by a small amplitude sinsuoidal signal and i.i.d. noise. The gain depends on the probability density function (PDF) of the input noise, and for a given noise PDF, the gain can be maximized by a proper choice of the quantizer thresholds. The maximum gain GSR is less than unity if the input noise is Gaussian, but several non-Gaussian noise PDFs yield values of GSR exceeding unity. Thus, the quantizers provide an effective enhancement in the SNR, which can be utilized to design a nonlinear signal detector whose performance is better than that of the matched filter. The nonlinear detector in consideration consists of a stochastically resonating (SR) quantizer followed by a correlator. An asymptotic expression for the probability of detection of the SR detector is derived. It is shown that the detection performance of the SR detector is better than that of the matched filter for a large class of noise distributions belonging to the generalized Gaussian and the mixture-of-Gaussian families.