In this article, the impact of input pump profile on the gain spectrum as well as the saturation behavior of one-pump fiber optical parametric amplifiers (FOPAs) is investigated. Since in practical circumstances, pump sources used for FOPAs have Lorentz-Gaussian profile instead of Gaussian, a more realistic case is considered for simulating FOPAs in this article. The results of simulations for ...

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

new statistic based models provide a wide area of prediction equipments for different science areas. among these fields biology have just entered the contest of interdisciplinary sciences. drug discovery is a long and expensive process which could be decreased with theoretical approaches. in this study, 500 reported assayed anti cancer molecules were extracted from science direct articles, sket...

in this paper, the detector of moving target in mimo radar with widely separated antenna in the presence of α-stable clutter is proposed. due to the complexity of unknown parameter estimation in the presence of target, the rao test is applied. the α-stable model is a general model that can applied to different environments with appropriate selection of parameters. the problem with this model is...

in this paper a new algorithm is presented for coherent radar detection of targets, when distribution of the interference (clutter and noise) is non-gaussian. the neyman-pearson criterion is used for optimal detection and several successive received samples from a radar range cell are used for detection in the same cell. on the basis of recent empirical evidences, the interference distribution ...

we discuss in this paper the strong convergence for weighted sums of negatively orthant dependent (nod) random variables by generalized gaussian techniques. as a corollary, a cesaro law of large numbers of i.i.d. random variables is extended in nod setting by generalized gaussian techniques.