Sharif Digital Repository

This paper presents an innovative formulation of the RKPM (reproducing kernel particle method) pioneered by Liu. A major weakness of the conventional RKPM is in dealing with the derivative boundary conditions. The EFGM (element free Galerkin method) pioneered by Belytschko shares the same difficulty. The proposed RKPM referred to as GRKPM (gradient RKPM), incorporates the first gradients of the function in the reproducing equation. Therefore in three-dimensional space GRKPM consists of four independent types of shape functions. It is due to this feature that the corrected collocation method can be readily generalized and combined with GRKPM to enforce the EBCs (essential boundary... 

The main purpose of this article is to generalize a characterization of constant functions to the context of metric-measure spaces. In fact, we approximate a measurable function, in terms of a certain integrability condition, by Lipschitz functions. Then, similar to Brezis (2002) [2], we establish a necessary and sufficient condition in order that any measurable function which satisfies an integrability condition to be constant a.e. Also, we provide a different proof for the main result of Pietruska-Pałuba (2004) [7] in the setting of Dirichlet forms  

Kernel based methods are significantly important in the pattern classification problem, especially when different classes are not linearly separable. In this paper, we propose a new kernel, which is the modified version of the polynomial kernel. The free parameter (d) of the proposed kernel considerably affects the error rate of the classifier. Thus, we present a new algorithm based on the Fisher criterion to find the optimum value of d. Simulation results show that using the proposed kernel for classification leads to satisfactory results. In our simulation in most cases the proposed method outperforms the classification using the polynomial kernel  

We give an algorithm to construct a translation-invariant transport kernel between two arbitrary ergodic stationary random measures on Rd, given that they have equal intensities.As a result, this yields a construction of a shift-coupling of an arbitrary ergodic stationary random measure and its Palm version.This algorithm constructs the transport kernel in a deterministic manner given a pair of realizations of the two measures.The (non-constructive) existence of such a transport kernel was proved in [9].Our algorithm is a generalization of the work of [3], in which a construction is provided for the Lebesgue measure and an ergodic simple point process.In the general case, we limit ourselves... 

Distance metric plays an important role in many machine learning algorithms. Recently, there has been growing interest in distance metric learning for semi-supervised setting. In the last few years, many methods have been proposed for metric learning when pairwise similarity (must-link) and/or dissimilarity (cannot-link) constraints are available along with unlabeled data. Most of these methods learn a global Mahalanobis metric (or equivalently, a linear transformation). Although some recently introduced methods have devised nonlinear extensions of linear metric learning methods, they usually allow only limited forms of distance metrics and also can use only similarity constraints. In this... 

We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macro-blocks of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks will be solved by estimating the proper covariance function of the Gaussian process defined over a region around the missing macro-blocks (i.e. its kernel function). In order to preserve block edges, the kernel is constructed adaptively by using the local edge related information. Moreover, we can achieve more improvements by local estimation of... 

Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of... 

In this study, we explored the growth kinetics of the Al-Fe intermetallic (IM) layer at the joint interface of the St-12/Al-5083 friction stir lap welds during post-weld annealing treatment at 350, 400 and 450 C for 30 to 180 min. Optical microscope (OM), field emission gun scanning electron microscope (FEG-SEM) and transmission electron microscope (TEM) were employed to investigate the structure of the weld zone. The thickness and composition of the IM layers were evaluated using image analysis system and electron back-scatter diffraction (EBSD), respectively. Moreover, kernel average misorientation (KAM) analysis was performed to evaluate the level of stored energy in the as-welded state.... 

In this paper, we introduce a model-based Bayesian denoising framework for phonocardiogram (PCG) signals. The denoising framework is founded on a new dynamical model for PCG, which is capable of generating realistic synthetic PCG signals. The introduced dynamical model is based on PCG morphology and is inspired by electrocardiogram (ECG) dynamical model proposed by McSharry et al. and can represent various morphologies of normal PCG signals. The extended Kalman smoother (EKS) is the Bayesian filter that is used in this study. In order to facilitate the adaptation of the denoising framework to each input PCG signal, the parameters are selected automatically from the input signal itself. This... 

Feature coding has received great attention in recent years as a building block of many image processing algorithms. In particular, the importance of the locality assumption in coding approaches has been studied in many previous works. We review this assumption and claim that using the similarity of data points to a more global set of anchor points does not necessarily weaken the coding method, as long as the underlying structure of the anchor points is considered. We propose to capture the underlying structure by assuming a random walker over the anchor points. We also show that our method is a fast approximation to the diffusion map kernel. Experiments on various data sets show that with a... 

The main purpose of this article is to generalize a theorem about the size of minimal submanifolds in Euclidean spaces. In fact, we state and prove a non-existence theorem about harmonic maps from a stochastically complete manifold into a cone type domain. The proof is based on a generalized version of the maximum principle applied to the Lapalace-Beltrami operator on Riemannian manifolds. © 2009 Birkhäuser Verlag Basel/Switzerland  

The growth of intelligent attacks has prompted the designers to envision the intrusion detection as a built-in process in operating systems. This paper investigates a novel anomaly-based intrusion detection mechanism which utilizes the manner of interactions between users and kernel processes. An adequate feature list has been prepared for distinction between normal and anomalous behavior. The method used is introducing a new component to Linux kernel as a wrapper module with necessary hook function to log initial data for preparing desired features list. SVM neural network was applied to classify and recognize input vectors. The sequence of delayed input vectors of features was appended to... 

This paper makes a case for writing unrestricted eBPF network functions which then get automatically decomposed between kernel and user-space. © 2021 Owner/Author  

Segmentation of a scene to the part made is a challenging work. In this paper a graphical model is used for this task. The methods based on geometrical derivatives such as curvature and normal often haven't good result in segmentation of geometrically-complex architecture and lead to over-segmentation and even failure. Proposed method for segmentation contains two steps. At first region growing based on curvature, normal and color is used for growing region. This segmented cloud is used for unary potential in graphical model. Fully connected graph for Conditional Random Field with Gaussian kernel for pair wise potentials is used for correcting this segmentation. Gaussian kernels are based on... 

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class... 

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class...