Noise Enhanced Signal Detection of Variable Detectors under Certain Constraints

Abstract

In this paper, a noise enhanced binary hypothesis-testing problem was studied for a variable detector under certain constraints in which the detection probability can be increased and the false-alarm probability can be decreased simultaneously. According to the constraints, three alternative cases are proposed, the first two cases concerned minimization of the false-alarm probability and maximization of the detection probability without deterioration of one by the other, respectively, and the third case was achieved by a randomization of two optimal noise enhanced solutions obtained in the first two limit cases. Furthermore, the noise enhanced solutions that satisfy the three cases were determined whether randomization between different detectors was allowed or not. In addition, the practicality of the third case was proven from the perspective of Bayes risk. Finally, numerous examples and conclusions are presented.

Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model

“…Recently, WHO has begun to promote efforts to address social determinants as an important component of global tuberculosis control [19]. Recently, the improvement of medical conditions [20], the improvement of optimal control strategy [21], classification algorithm, and signal processing algorithm [22,23], have been widely used in the medical field, meanwhile, big data and data analysis techniques are applied to disease diagnosis [24], such that the accuracy of diagnosis results has been significantly improved, and have contributed to preventing the incidence of tuberculosis diseases. Much of the epidemiological TB literature relies on notified cases, and relatively few involve measurements and trend predictions of TB prevalence [25].…”