Earticle

This paper presents the revised estimates of various ultrasonographic markers used in practice for estimation of gestational age among singleton Indian population. Accurate estimation of gestational age plays a vital role in efficient obstetric planning and also helps us to estimate fetal malformation or growth retardation. Incorrect estimation of gestational age can result in grave complication for the patient and liability for health care providers. But the spectacular improvement in ultrasound imaging quality in the past decade has contributed to the rapid increase in its use as a diagnostic tool in the past decade. The ultrasound based growth chart used in practice date back to 1980’s. These charts have been found to estimate the gestational age with an accuracy of 2 weeks which is high compared to the state-of-the-art scanning systems. Hence in this paper an effort has been made to review the various ultrasonographic markers for fetal growth estimation and also propose revised estimates for US charts for singleton Indian population. Results indicate that using the proposed scheme, it may be possible to estimate the gestational age with an accuracy of 2 to 3 days.

A theoretical model for the I-V characteristics of buried-gate GaAs metal semiconductor field-effect transistors has been developed by solving dc continuity equation. This analysis includes the ion implanted buried-gate process. It is shown that the current- voltage could be rather increased when introducing an optical fiber to the buried-gate GaAs MESFETs structure. The current -voltage characteristics and the channel conductance of the device have been evaluated. The results indicate very good performance of the device compared to other devices like MESFET under back illumination and MESFET with front illumination having surface gate. Buried-gate optical field effect transistor (OPFET) will be highly suitable for power device application, optical communication and optical computing.

This paper presents algorithms based on image processing for detecting internal blood spots and eggshell dirt by processing acquired images from eggs under different illuminations. The algorithm can also detect the severity of dirt on eggshell. In order to carry out image processing and extract useful features of captured images of eggs by machine vision we developed an algorithm in HSI color space. The hue histogram was used for blood spots detection, and maximum values of two ends of histogram were selected as criterions of defect detection. Eggshell dirt was detected using connected areas detection technique. The results of experiments showed that accuracy of differentiation of blood spots algorithm was 90.66% of defected eggs and 91.33% of intact eggs and total average of this algorithm was 91%. Accuracy of differentiation of dirt detect algorithm was 86% of clean eggs, 83% of low dirt eggs and 88% of high dirt eggs. Then total average of this algorithm was 85.66%.

The security in wireless sensor networks (WSNs) is a critical issue due to the inherent limitations of computational capacity and power usage. While a variety of security techniques are being developed and a lot of research is going on in security field at a brisk pace but the field lacks a common integrated platform which provides a comprehensive comparison of the seemingly unconnected but linked issues. In this paper we attempt to comparatively analyse the various available security approaches highlighting their advantages and weaknesses. This will surely ease the implementers’ burden of choosing between various available modes of defence.