An mobile Ad Hoc network is a collection of mobile nodes. They do not have any existing infrastructure and they do not have any centralized administrator. So the MANET is self-creating, self-organizing and self-administrative wireless network. In MANET each node acts as router. In practice some of the nodes may act as the selfish nodes. These nodes use the network and its services but they do not cooperate with other nodes. Such selfish nodes do not consume any energy such as CPU power, battery and bandwidth for retransmitting the data of other nodes. They will preserve the resources for their own use. In this paper we have provide the comparative study of different type of methods to increase the selfish node detection and the network throughput

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Selective Laser Melting (SLM) is an emerging, fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shape in reasonable time period. The quality of built parts highly depends on many process variables in selective laser melting. In this study, three important SLM process parameters such as layer thickness, orientation angle and scan speed are considered. Their influence on tensile strength of test specimen is studied. Margining Steel having grade 1.2709 was the material, commercially named CL50WS, which is used for fabricate Tensile Specimen in SLM. The experiments are conducted based on Taguchi's L8 orthogonal array. The validity of process parameter and response is tested by using analysis of variance (ANOVA). The multi linear regression model is developed in order to predict Tensile strength of test specimen. The experimental data and data obtained by regression equation is closely correlated which validated the models. The layer thickness and scan speed is highly affect the quality of SLM fabricated parts whereas orientation angle have little important.

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This paper presents a new color space normalization (CSN) technique for enhancing the discriminating power of color space along with the principal component analysis (PCA) for the face recognition process. The common RGB technique is not suitable for the characterizing of the skin color due to the presence of luminance factor. In the YCbCr color space, the luminance information is contained in Y component, and the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. Different color spaces have different discriminating power, in this paper, eye can be perfectly detected by using YcbCr color space and the mouth regions can be perfectly detected by using the YIQ color space. Then PCA is used to express the large 1-D vector of pixels constructed from 2-D facial image into the compact principal components of the feature space. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. PCA allows us to compute a linear transformation that maps data from a high dimensional space to a lower dimensional space. It covers standard deviation, covariance, eigenvectors and eigenvalues. Face recognition is obtained by PCA without much loss of information. Experiments using different databases by varying the facial expressions (open/closed eyes, smiling/not smiling) show that the proposed method by combining color space discrimination and PCA can improve face recognition to a great extend.

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