Now showing items 1-4 of 4
Noise enhanced detection in restricted Neyman-Pearson framework
Noise enhanced detection is studied for binary composite hypothesis-testing problems in the presence of prior information uncertainty. The restricted Neyman-Pearson (NP) framework is considered, and a formulation is obtained ...
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing ...
Optimal stochastic signal design and detector randomization in the Neyman-Pearson framework
Power constrained on-off keying communications systems are investigated in the presence of stochastic signaling and detector randomization. The joint optimal design of decision rules, stochastic signals, and detector ...
Detector randomization and stochastic signaling for minimum probability of error receivers
(Institute of Electrical and Electronics Engineers, 2012)
Optimal receiver design is studied for a communications system in which both detector randomization and stochastic signaling can be performed. First, it is proven that stochastic signaling without detector randomization ...