@article{453b5344e81244bfb3e6e79e54b9a114,

title = "Reducing probability of decision error using stochastic resonance",

abstract = "The problem of reducing the probability of decision error of an existing binary receiver that is suboptimal using the ideas of stochastic resonance is solved. The optimal probability density function of the random variable that should be added to the input is found to be a Dirac delta function, and hence, the optimal random variable is a constant. The constant to be added depends upon the decision regions and the probability density functions under the two hypotheses and is illustrated with an example. Also, an approximate procedure for the constant determination is derived for the mean-shifted binary hypothesis testing problem.",

keywords = "Modeling, Pattern classification, Signal detection",

author = "Steven Kay and Michels, {James H.} and Hao Chen and Varshney, {Pramod K.}",

note = "Funding Information: Manuscript received February 21, 2006; revised April 7, 2006. This work was supported by the Air Force Office of Scientific Research under Contract FA9550-05-C-0139. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Erik G. Larsson. S. Kay is with the Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI 02881 USA (e-mail:

[email protected]). J. H. Michels is with JHM Technologies, Ithaca, NY 14852 USA (e-mail:

[email protected]). H. Chen and P. K. Varshney are with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA (e-mail:

[email protected];

[email protected]). Digital Object Identifier 10.1109/LSP.2006.879455",

year = "2006",

month = nov,

doi = "10.1109/LSP.2006.879455",

language = "English (US)",

volume = "13",

pages = "695--698",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "11",

}