Breast cancer is one of the leading causes for high mortality rates among young women, in the developing countries. Currently mammography is used as the gold standard for screening breast cancer. Due to its inherent disadvantages, alternative techniques are being considered for this purpose. Breast thermography is one such imaging modality, which represents the temperature variations of breast in the form of intensity variations on an image. In the last decade, several studies have been made to evaluate the potential of breast thermograms in detecting abnormal breast conditions, from an image processing view point. This paper proposes a curvelet transform based feature extraction method for automatic detection of abnormality in breast thermograms. Statistical and texture features are extracted from thermograms in the curvelet domain, to feed a support vector machine for automatic classification. The classifier detects abnormal thermograms with an accuracy of 90.91 %. The results of the study indicate that texture features have better potential to detect abnormality in breast thermograms, when extracted in the multiresolution curvelet domain.

This paper reports on the eco-friendly synthesis of gold nanoparticles (AuNPs) using Solanum indicum fruit extract (SFE). We have evaluated various parameters for synthesis of AuNPs such as SFE (0.03%), HAuCl4 (0.5 mM) and reaction time (20 seconds). The synthesized AuNPs were characterized with different physical techniques such as transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX). TEM experiments showed that AuNPs presented an anisotropic shape and size ranging from 5-50nm. FT-IR spectroscopy revealed that biomolecules containing an amine group (-NH2), a carbonyl group,-OH groups and other stabilizing functional groups were adsorbed on the surface of the synthesized AuNPs. EDX showed the presence of the elements on the surface of the AuNPs. The cytotoxicity of the synthesized AuNPs were tested on two different human cancer cell lines, HeLa and MCF-7 and were found to be nontoxic, thus providing an opportunity to be used in biomedical applications.

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Abstract-Wireless sensor networks (WSN) are being used for huge range of applications where the traditional infrastructure based network is mostly infeasible. The most challenging aspect of WSN is that they are energy resource-constrained and that energy cannot be replenish. the wireless sensor network of power limited sensing devices called sensor deployed in a region to sense various types physical information from the environment, when these sensors sense and transmit data to other sensors present in the network, even the cluster head is elected according to check their residual energy considerable amount of energy will drain automatically to overcome this drawback by considering the protocol a fuzzy logic approach is used to elect the cluster head based on three descriptors-energy, centrality & distance and second CH is elected according to TDMA to overcome the data lost during energy drain occur in the CH .NS-2 simulation shows that proposed protocol provides higher energy efficiency. This paper proposes the mechanism or device is capable of utilizing its own system of control simply called as self-configurable clustering mechanism to detect the disordered CHs and replace them with other nodes. And results have been derived from simulator ns-2 to show the better performance.

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