Education is one of the areas that are experiencing phenomenal changes as a result of the advancement and use of information technology. Mobile and e-learning are already facilitating the teaching and learning experience with the use of latest channels and technologies. Blended learning is a potential outcome of advanced technology based learning system. The charm of blended learning approach lies in the adaptation of technology aided learning methods in addition to the existing traditional based learning. With the introduction of technology, the overall learning as well as teaching experience is considerably enhanced by covering negative aspects of the traditional approach. In this paper a blended learning model for higher education where traditional classroom lectures are supported via e-learning.

Abstract-Character Recognition is one of the important tasks in Pattern Recognition. The complexity of the character recognition problem depends on the character set to be recognized. Neural Network is one of the most widely used and popular techniques for character recognition problem. This paper discusses the classification and recognition of printed Hindi Vowels and Consonants using Artificial Neural Networks. The vowels and consonants in Hindi characters can be divided in to sub groups based on certain significant characteristics. For each group, a separate network is designed and trained to recognize the characters which belong to that group. When a test character is given, appropriate neural network is invoked to recognize the character in that group, based on the features in that character. The accuracy of the network is analyzed by giving various test patterns to the system.

This paper introduces a lightweight wideband planar antenna with an antenna array configuration for microwave head imaging. The designed antenna is composed of a double hollow rectangular-shaped patch and with the slotted ground. Its measurements are small: 0.28λ Â 0.24λ Â 0.006λ, where λ is the lowest operating frequency's wavelength. The design process has been completed using CST and then fine-tune the parameters of the architecture structure to accomplish the ideal bandwidth. The antenna is manufactured and measured in order to test antenna properties. The prototype was being used for brain screening than with the Hugo-head model. With adequate impedance matching, the antenna meets an operational bandwidth of 1.82 GHz (1.22-3.04 GHz) with an overall peak gain of 5 dBi with a stable radiation property.Fidelity factors both for face-to-face and side by side alignment are 92.7% and 83.75%, respectively, indicating that the antenna had a little signal interruption. To determine the backscattered signals of the antenna with Hugo-head phantom, a nine-antenna array-based setup is assumed, with one antenna serving as a transmitter and the remaining eight receiving the dispersed signals.

Optical character recognition is a vital task in the field of pattern recognition. English character recognition has been extensively studied by many researchers but in case of Indian languages which are complicated; the research work is very limited. Devanagari is an indian script used by huge number of indian people. Devanagari forms the basis for several indian languages including Hindi, Sanskrit, Kashmiri, Marathi and so on. This article presents a review of earlier research work related to devanagari character recognition along with some applications of optical character recognition system.

A new approach, to measure normalization completeness for conceptual model, is introduced using quantitative fuzzy functionality in this paper. We measure the normalization completeness of the conceptual model in two steps. In the first step, different normalization techniques are analyzed up to Boyce Codd Normal Form (BCNF) to find the current normal form of the relation. In the second step, fuzzy membership values are used to scale the normal form between 0 and 1. Case studies to explain schema transformation rules and measurements. Normalization completeness is measured by considering completeness attributes, preventing attributes of the functional dependencies and total number of attributes such as if the functional dependency is non-preventing then the attributes of that functional dependency are completeness attributes. The attributes of functional dependency which prevent to go to the next normal form are called preventing attributes.