Optimization by Vector Space Methods
Optimization by Vector Space Methods
Stochastic Resonance and Coincidence Detection in Single Neurons
Neural Processing Letters
Design of detectors based on stochastic resonance
Signal Processing
Convex Optimization
2005 Special Issue: Stochastic resonance in noisy spiking retinal and sensory neuron models
Neural Networks - 2005 Special issue: IJCNN 2005
Noise
Noise-enhanced nonlinear detector to improve signal detection in non-Gaussian noise
Signal Processing - Special section: Distributed source coding
EURASIP Journal on Applied Signal Processing
Stochastic resonance in locally optimal detectors
IEEE Transactions on Signal Processing
Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors
IEEE Transactions on Signal Processing - Part I
Noise-Enhanced Detection of Subthreshold Signals With Carbon Nanotubes
IEEE Transactions on Nanotechnology
Convexity properties in binary detection problems
IEEE Transactions on Information Theory
Energy-efficient detection in sensor networks
IEEE Journal on Selected Areas in Communications
Adaptive stochastic resonance in noisy neurons based on mutual information
IEEE Transactions on Neural Networks
Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise
IEEE Transactions on Neural Networks
Noise enhanced hypothesis-testing in the restricted Bayesian framework
IEEE Transactions on Signal Processing
Optimal stochastic signaling for power-constrained binary communications systems
IEEE Transactions on Wireless Communications
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
Stochastic signaling in the presence of channel state information uncertainty
Digital Signal Processing
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We present theorems and an algorithm to find optimal or near-optimal "stochastic resonance" (SR) noise benefits for Neyman-Pearson hypothesis testing and for more general inequality-constrained signal detection problems. The optimal SR noise distribultion is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence ofsuch optimal SR noise in inequality-constrained signal detectors. There exists a sequence of noise variables whose detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman-Pearson and other signal detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results.