This paper demonstrates mainly on feature extraction by analytic and holistic methods and proposes a novel approach for feature level fusion for efficient expression recognition. Gabor filter magnitude feature vector is fused with upper part geometrical features and phase feature vector is fused with lower part geometrical features respectively. Both these high dimensional feature dataset has been projected into low dimensional subspace for de-correlating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset compared with state of art approaches. 

In this paper, we focused on of the most proliferated network that is Mobile Adhoc Network (MANET). Due to the dynamic nature and limited power of nodes, the routes will fail frequently which intern cause high power dissipation. This paper proposed a reliable and power efficient routing with the nodes having high power level. As well as, this approach also concentrated on the reduction of power consumption during route failures by adapting an on-demand local route recovery mechanism through a set of helping nodes and they are called as Support Nodes (SN). The cooperation of support nodes will reduce the power consumption and significantly increases the reliability. The performance of proposed approach was evaluated through average energy consumption, packet delivery ratio and end-to-end delay over varying node speed and varying packet size. The power optimization and reliability achieved by the proposed approach gives an ideal solution to the future communication in MANETs for a long time.