Thresholding is a way to extract the features of an object from its background. Various methods are in existence for obtaining the threshold value. An effective method, the Tsallis entropy model, is used to calculate the threshold value based on which the image is segmented. To get better feature extraction, the non-extensive parameter ‘q’ is used to distinguish the long-range correlation. The ...

I this paper, we propose a fast adaptive importance sampling method for the efficient simulation of buffer overflow probabilities in queueing networks. The method comprises three stages. First, we estimate the minimum cross-entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer l...

Workgroup assignment problems commonly appear in various settings including international business schools. Especially if diverse people, like students, need to be divided into workgroups one may seek environments where diversity is fostered by generating heterogeneous workgroups. We study a problem of workgroups diversity maximization, i.e., the problem of building workgroups with the goal of ...

This paper addresses the problem of scheduling activities in projects with stochastic activity durations. The aim is to determine for each activity a gate—a time before it the activity cannot begin. Setting these gates is analogous to setting inventory levels in the news vendor problem. The resources required for each activity are scheduled to arrive according to its gate. Since activities’ dur...

A derivative-free framework for optimizing a non-smooth range-based contrast function in order to estimate independent components is presented. The proposed algorithm employs the von-Mises Fisher (vMF) distribution to draw random samples in the cross-entropy (CE) method, thereby intrinsically maintaining the unit-norm constraint that removes the scaling indeterminacy in independent component an...

An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of opt...

Deciding test data volume is a major challenge in present System on Chip (SoC) design. Compaction of test data volume results in high compression ratio. Entropy analysis plays a vital role in such cases. Entropy is measurement of the amount of information contained in the data set. Entropy calculations tell the need of how much test data vector that can be compressed. This paper is based on fin...

Understanding information processing in the brain requires the ability to determine the functional connectivity between the different regions of the brain. We present a method using transfer entropy to extract this flow of information between brain regions from spike-train data commonly taken in neurological experiments. Transfer entropy is a statistical measure based in information theory that...

Brain tumor is the most deadly disease that affects human life span. To segment the brain tumor part, many segmentation techniques have been emerged in image processing like region based Segmentation, Boundary based segmentation. In this paper, several entropies based methods and several cluster techniques are compared and analyzed for brain tumor segmentation. Several entropies such as rough e...