The Hurst parameter captures the amount of long-range dependence (LRD) in a time series. There are severalmethods to estimate the Hurst parameter, being the most popular: the variance-time plot, the R/S plot, theperiodogram, and Whittle’s estimator. The first three are graphical methods, and the estimation accuracy depends onhow the plot is interpreted and calculated. In contrast, Whittle’s estimator is based on a maximum likelihood techniqueand does not depend on a graph reading; however, it is computationally expensive. A new method to estimate theHurst parameter is proposed. This new method is based on an artificial neural network. Experimental results showthat this method outperforms traditional approaches, and can be used on applications where a fast and accurateestimate of the Hurst parameter is required, i.e., computer network traffic control. Additionally, the Hurst parameterwas computed on series of different length using several methods. The simulation results show that the proposedmethod is at least ten times faster than traditional methods.
This paper presents a new image segmentation scheme based on active contours guided by the optimization techniques Particle Swarm Optimization (PSO) and Differential Evolution (DE), independently. The scheme uses the optimization methods over a polar coordinate system to perform the segmentation task increasing the energyminimizing capability regarding the traditional active contour model. This proposed model is applied in the segmentation of the human heart from datasets of sequential Computed Tomography images. In addition, to obtain a quantitative and qualitative evaluation of the segmentation results compared to regions outlined by experts, different similarity metrics have been adopted. The experimental results demonstrate that by using PSO or DE, the proposed scheme outperforms the traditional implementation of active contour model in terms of stability and efficiency, achieving a high accuracy segmentation regarding the ground truth.
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