Interference aware resource allocation model for D2D under cellular network

Device-to-Device communication (D2D) has emerged as an efficient communication model in future generation cellular network for offloading cellular traffic and enhance overall network performance. D2D communication aid in attaining better spectrum utilization, lower delay, and less energy consumption, which can well adapt to meet demand of higher transmission rate, larger network capacity. Further, enhances spectral efficiency by reutilizing resource. However, it may result in severe cross-tier interference and co-tier interference. Therefore, efficient interference modelling design are required to address performance degradation caused by the interferences. The existing model has focused on addressing interference considering D2D association operating on same cell with the cellular association. As a result, it incurs interference to the cellular user located in the same cell. However, practically D2D association in overlapping area will reutilize spectrum of multiple neighboring cells. As a result, it incurs interference in multiple cells. For overcoming research challenges, this work presented Interference Aware Resource Allocation (IARA) model for D2D under cellular network as a game theory model. This work consider a resource allocation game where base station as a contender for catering D2D resource needs under different assumptions. Experiment are conducted to evaluate performance of IARA. The outcome shows IARA attained significant performance improvement over state-of-art models in terms of sum rate (utility), successful packet transmission, revenue, and delay

Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma

Melanoma is the most widely occurring and life threatening form of skin cancer. Early detection of in situ melanoma has challenged researchers for many decades now. Currently there exists no computer aided mechanisms to accurately detect early melanoma. T he currently existing computer aided diagnostics mechanisms are capable of melanoma classification and are unable to detect in situ melanoma. This paper introduces a Multi Parameter Extraction and Classification System ( 푀푀푀푀푀 ) to aid early detection o f skin cancer melanoma. The 푀푀푀푀푀 defines the skin lesion images in terms of characteristic parameters which are further used for classification. In this paper the extraction of 21 parameters is achieved using a six phase approach. The parameters extr acted are analyzed using statistical methods. It is clear from the results obtained that no single parameter can affirm the detection of in situ melanoma, hence an advanced analysis mechanisms considering all the parameters need to be adopted to effective ly detect melanoma in its initial stages

A NOVEL TECHNIQUE FOR FACE IDENTIFICATION

This paper presents a correspondence measure method based on novel method whichcombines a permutation of Voila Jones Face detection method and Eigen face recognition(Identification)technique. In the proposed method, we come to overcome the problem of low accuracy. We propose a new system here to align the face using Voila Jones Face detection method followed by Eigen face recognition technique. Compared to other face detection methods, the proposed method is very efficient for the face detection purpose. The system is built to meet real time face recognition criteria