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Mobile Ad-hoc Networks (MANETs) are a set of mobile nodes which communicate without any fixed infrastructure and centralized controller. Routing in such networks is a big challenge because of the dynamic nature of this networks that each node keeps moving continuously, power energy and bandwidth are limited. Finding routes which moreover optimization, reduce the overhead of the networks. Different protocols are proposed for routing in MANETs, but nowadays researchers incorporation routing protocol with swarm intelligence (SI) techniques. One of the important techniques is, use of ant colony optimization (ACO) with routing protocols. Nature has proven that the ants finding optimal path between the nest and food and adaptive nature of this agent, make help to propose a suitable routing protocol for MANETs. In this paper, we review some routing protocol that mingle ACO with existing routing protocol and introduce the advantages and disadvantages of them. Finally, we compare these protocols to each other.

In this paper, a user friendly algorithm based on the optimal homotopy analysis transform method (OHATM) is proposed to solve a system of generalized Abel's integral equations. The classical theory of elasticity of material is modeled by the system of Abel integral equations. It is observed that the approximate solutions converge to the exact solutions. Illustrative numerical examples are given to demonstrate the efficiency and simplicity of the proposed method in solving such types of systems of Abel's integral equations. Finally, several numerical examples are given to illustrate the accuracy and stability of this method. Comparison of the approximate solution with the exact solutions we show that the proposed method is very efficient and computationally attractive.

To aim at the challenge of face recognition to uncontrolled situations, robust face recognition system requires to take into account different kinds of face appearance feature. However, most existing methods only use features of just one type. We show that integrating two of global and local representations, Shearlets features and local binary pattern (LBP), which gets better performance than either alone. Shearlets features not only utilize scale and position information of different scales of decomposed image, but also use directional information. Shearlets features primary capture facial global attributes while LBP encode small local appearance details. Both feature sets are high dimensional so it is beneficial to apply block-based fisher linear discriminant (BFLD) and PCA to reduce dimensionality prior to normalization and integration. Then low dimensional Shearlets and LBP sets are combined by score level fusion. the proposed method is evaluated on two challenge face databases including MPIE and FERET with promising results.

In a number of pattern recognition application, moments have been used to recognize image patterns. The recognition process involves effective shape representation method. So, the present paper analyzes the recognition rate in two different shape representation methods called external and internal representation. With the proper shape representation of the given image pattern, the present paper has computed 7 boundary (external) based and 10 region (internal) based Hu moments. The experimental results on five different pattern image groups (Brick, Circle, Curve, Line and Zigzag) are precisely recognized by both boundary based and region based moments of order 1 and 10 respectively.

In this paper, we study the characterization of nonuniform wavelet sets on positive half line and we also prove the existence of nonuniform wavelet sets associated with the dilation N and the translation set and N are

In this article, we use the modified simple equation method to construct the exact traveling wave solutions for some nonlinear PDE's in mathematical physics namely the coupled Hirota – Satsuma KdV equations and the generalized coupled Hirota – Satsuma KdV equations. Based on this formulation, solitary solutions can be easily obtained by using the proposed method. Some known solutions obtained by the tanh- coth method are recovered as special cases. The proposed method is direct and more powerful than the other methods.

The Applications of Pattern recognition like wood classification, stone and rock classification problems, the major usage techniques ate different texture classification techniques. Generally most of the problems used statistical approach for texture analysis and texture classification. Gray Level Co-occurrence Matrices (GLCM) approach is particularly applied in texture analysis and texture classification. The GLCM gives better results with accuracy but its take much time for computation. The texture analysis methods mainly depends upon how the particular texture features characterizes texture image. The accuracy of a particular texture analysis method depends what type of features are extracted from a texture image for classification, whether these features correctly classifies the textures or not. The accuracy of texture analysis method depends not only the texture features are important but also the way in which texture features are applied is also an important and significant for a critical, particular and perfect texture classification and analysis. The present paper derived a new texture analysis method i.e. co-occurrence matrix based on fuzzy rules and water shed texton patterns. The present paper applies fuzzy rules on Original texture image based on water shed texton patterns and generates Co-occurrence matrices derived a new matrix called Fuzzy based Texton Co-occurrence Matrices (FbTCoM) for texture classification. The present paper integrates the advantages of co-occurrence matrix and texton image by representing the attribute of co-occurrence matrix using water shed texton pattern based on fuzzy rule. The co-occurrence features extracted from the FbTCoM provides complete texture information about a Texture image. The proposed method is experimented on Vistex, Brodatz textures, CUReT, MAYAGAN, PBOURKE, and Google color texture images. The experimental results indicate the proposed method classification performance is superior to that of many existing methods.