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Energy consumption in wireless communication systems is increasing tremendously, wherein most of the energy consumedis by the evolved NodeBs in LTE network. Nowadays the research trend is majorly focused on Green communication which aims at reducing the Energy consumed in a network. In this paper, a radio resource allocation algorithm that is meant to balance energy efficiency and throughput maximization is proposed. The proposed algorithm is used to allocate Resource Blocks (RBs) to users considering the data rate requirements of the users based on priority for LTE downlink while maintaining Quality of Service (QoS). In this way the scarce spectrum is effectively utilized and the energy efficiency (EE) is improved. The proposed algorithm is compared with conventional scheduling algorithms such as Round Robin (RR), Best CQI (Best CQI/BCQI) and is analyzed on the basis of throughput, fairness and Energy Consumption Ratio (ECR).

This paper introduces Gabor wavelet grids localized within the region of importance (ROI) for a robust pattern recognition system that is capable of recognizing various image classes. The representation of the patterns is based on Gabor wavelet transforms. The features are extracted as a vector of values using an optimally chosen multiple frequency, multiple orientation 2D symmetrical Gabor wavelet grid that spans the region of importance (ROI). This technique of feature extraction is biologically motivated. The purpose of this study is to demonstrate that localized Gabor wavelet grids with a MLP classifier can be effectively used for recognition of various types of patterns. Recognition of close to 100% is demonstrated for some real world patterns like faces, currency notes, postage stamps and typed characters.

We present a system for recognizing human faces from a database consisting of multiple images per test subject, which spans the normal variations in a human face. The faces are represented based on a Gabor wavelet transform. The features are extracted as a vector of values using a carefully chosen symmetrical Gabor wavelet matrix. This feature extraction is biologically motivated and models systems based on human vision. The extracted features are fed into an artificial neural network, in dual phases. The training and testing phases of the neural network work on the features extracted by the same method. Excellent pattern-recognition-specific neural network like a multilayer perceptron with back propagation provides the necessary classification once the feature extraction is complete