introduction: secondary radiation such as photoelectrons, auger electrons and characteristic radiations cause a local boost in dose for a tumor when irradiated with an external x-ray beam after being loaded with elements capable of activating the tumor, e.g.; i and gd. materials and methods:  in this investigation, the mcnpx code was used for simulation and calculation of dose enhancement facto...

Transmitting the information in the form of images has drawn much importance in the modern age. The images are often corrupted by various types of noises during acquisition and transmission. Such images have to be cleaned before using in any applications. Image denoising is a thirst area in image processing for decades. Wavelet transform has been an efficient tool for image representation for d...

introduction in this study, we aimed to calculate dose enhancement factor (def) for gold (au) and iron (fe) nanoparticles (nps) in brachytherapy and teletherapy, using monte carlo (mc) method. materials and methods in this study, a new algorithm was introduced to calculate dose enhancement by aunps and fenps for iridium-192 (ir-192) brachytherapy and cobalt-60 (co-60) teletherapy sources, using...

In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the minimization of convex cost is proposed. Normally, functions underestimate larger signal amplitudes during de-noising process. The proposed more flexible shrinkage it solves underestimation problem greater extent and thus efficiently de-noises without affecting amplitudes. Expectation (EM) algor...

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse represen...

We examine and extend Sparse Grids as a discretization method for partial differential equations (PDEs). Solving a PDE in D dimensions has a cost that grows as O(ND) with commonly used methods. Even for moderate D (e.g. D = 3), this quickly becomes prohibitively expensive for increasing problem size N . This effect is known as the Curse of Dimensionality. Sparse Grids offer an alternative discr...

In this paper we investigate a method to improve the performance of sparse LU matrix factorization used to solve unsymmetric linear systems, which appear in many mathematical models. We introduced and used the concept of the supernode for unsymmetric matrices in order to use dense matrix operations to perform the LU factorization for sparse matrices. We describe an algorithm that uses supernode...

—We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g. blocks) into 3D data arrays which we call "groups". Collaborative ltering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformat...

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can onl...