Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.

This work explains the comparison of various dc-dc converters for photovoltaic systems. In recent day insufficient energy and continues increasing in fuel cost, exploration on renewable energy system becomes more essential. For high and medium power applications, high input source from renewable systems like photovoltaic and wind energy system turn into difficult one, which leads to increase of cost for installation process. So the generated voltage from PV system is boosted with help various boost converter depends on the applications. Here the various converters are like boost converter, buck converter, buck-boost converter, cuk converter, sepic converter and zeta converter are analysed for photovoltaic system, which are verified using matlab / simulink.

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INTRODUCTION Polycysticovarian syndrome (PCOS) is a common endocrine disorder characterized by a variety of symptoms like hyperandrogenism, hyperinsulinaemia, menstrual dysfunction, unique ultrasonographic ovarian pattern and infertility [1]. Antral Follicle count (AFC) has been found to be reliable marker for ovarian reserve [2]. Since women with PCOS are extremely sensitive to gonadotrophin stimulation, knowledge of age related AFC normogram is clinically relevant. Wiser et al., published age-related normogram for AFC in women with PCOS using transvaginal ultrasound and found that the decline in number of AFC as the age progresses was linear and slower in PCOS when compared to infertile women without PCOS [2]. We used magnetic resonance imaging (MRI) instead of TVS, since MRI can acquire three dimensional images, not operator dependant and also it can be performed in patients for whom transvaginal ultrasound could not be performed like in unmarried women. The main objective of our study is to create an age related normogram for AFC (AFC) in women with PCOS and to compare that with women without polycystic ovarian syndrome using MRI. MATERIALS AND METHODSThis descriptive cross-sectional study was conducted after obtaining clearance from Institutional Ethics Committee between January 2013 to July 2015. The goal of the study was to create an age related normogram of AFC in women with PCOS and to compare it with those without PCOS using MRI. Women between the ages of 18 to 45 were included in the study. A total of 1500 women were examined, out of which 400 fitted the criteria for