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...

diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. thus, the need for precise segmentation of corneal layer boundaries is inevitable. obviously, manual segmentation is time‑consuming and imprecise. in this paper, the gaussian mixture model (gmm) is used for automatic segmentation of three clinically important corneal boundaries on opt...

in a voice conversion system speech signal of a speaker (i.e. source speaker) is modified so that it sounds as if it had been pronounced by b speaker (i.e. target speaker). this process, sometimes, is called speaker conversion (changing speaker identity). achieved signal from speaker conversion system is desired to have high quality and very natural. to satisfy this, three major methods are pro...

the objective of this study was to simulate carbon monoxide (co) dispersion exited from the stacks of a cement industry in doroud, iran by gaussian model (gm). four sampling period was conducted for the measurement of co from the factory's three-stack flow during a period of one year. the input parameters were the rate of co emission, meteorological data,  factors related to the stack, and...

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...

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

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 ...

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...

in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...