An effective connected dominating set based mobility management algorithm in MANETs

This paper proposes a connected dominating set (CDS) based mobility management algorithm, CMMA, to solve the problems of node entering, exiting and movement in mobile ad hoc networks (MANETs), which ensures the connectivity and efficiency of the CDS. Compared with Wu's algorithm, the proposed algorithm can make full use of present network conditions and involves fewer nodes. Also it has better performance with regard to the approximation factor, message complexity, and time complexity.

An implicit algorithm based on iterative modified approximate factorization method coupling with characteristic boundary conditions for solving subsonic viscous flows

An algorithm composed of an iterative modified approximate factorization(MAF(k)) method with Navier-Stokes characteristic boundary conditions(NSCBC) is proposed for solving subsonic viscous flows.A transformation on the matrix equation in MAF(k) is made in order to impose the implicit boundary conditions properly.To be in consistent with the implicit solver for the interior domain,an implicit scheme for NSCBC is formulated.The performance of the developed algorithm is investigated using spatially evolving zero pressure gradient boundary layer over a flat plate and a wall jet mixing with a cross flow over a flat plate with a square hole as the test cases.The numerical results are compared to the existing experimental datasets and a number of general correlations,together with other available numerical solutions,which demonstrate that the developed algorithm possesses promising capacity for simulating the subsonic viscous flows with large CFL number.

Alternating Cell Direction Implicit Method using Approximate Factorization on Hybrid Grids

In this study,a novel fast-implicit iteration scheme called the alternating cell direction implicit(ACDI)method is combined with the approximate factorization scheme.This application aims to offer a mathematically well-defined version of the ACDI method and to increase the accuracy of the iteration scheme used for the nu-merical solutions of partial differential equations.The ACDI method is a fast-implicit method that can be used for unstructured grids.The use of fast implicit iteration meth-ods with unstructured grids is not common in the literature.The new ACDI method has been applied to the unsteady diffusion equation to determine its convergence and time-dependent solution ability and character.The numerical tests are conducted for different grid types,such as structured,unstructured quadrilateral,and hybrid polygonal grids.Second,the ACDI was applied to the unsteady advection-diffusion equation to understand the time-dependent and progression capabilities of the pre-sented method.Third,a full potential equation solution is created to understand the complexflow solving ability of the presented method.The results of the numerical study are compared with other fast implicit methods,such as the point Gauss–Seidel(PGS)and line Gauss–Seidel(LGS)methods and the fourth-order Runge-Kutta(RK4)method,which is an explicit scheme,and the Laasonen method,which is a fully im-plicit scheme.The study increased the abilities of the ACDI method.Due to the new ACDI method,the approximate factorization method,which is used only in structural grids that are known to be advantageous,can be applied to any mesh structure.

A Factor-GARCH Model for High Dimensional Volatilities

This paper proposes a method for modelling volatilities(conditional covariance matrices)of high dimensional dynamic data.We combine the ideas of approximate factor models for dimension reduction and multivariate GARCH models to establish a model to describe the dynamics of high dimensional volatilities.Sparsity condition and thresholding technique are applied to the estimation of the error covariance matrices,and quasi maximum likelihood estimation(QMLE)method is used to estimate the parameters of the common factor conditional covariance matrix.Asymptotic theories are developed for the proposed estimation.Monte Carlo simulation studies and real data examples are presented to support the methodology.

Variational learning for finite Beta-Liouville mixture models

In the article, an improved variational inference （VI） framework for learning finite Beta-Liouville mixture models （BLM） is proposed for proportional data classification and clustering. Within the VI framework, some non-linear approximation techniques are adopted to obtain the approximated variational object functions. Analytical solutions are obtained for the variational posterior distributions. Compared to the expectation maximization （EM） algorithm which is commonly used for learning mixture models, underfitting and overfitting events can be prevented. Furthermore, parameters and complexity of the mixture model （model order） can be estimated simultaneously. Experiment shows that both synthetic and real-world data sets are to demonstrate the feasibility and advantages of the proposed method.