International Journal of Advanced Science and Technology

10

Authors: Rosslin John Robles and Tai-hoon Kim

A smart home or building is a home or building, usually a new one that is equipped with special structured wiring to enable occupants to remotely control or program an array of automated home electronic devices by entering a single command. Conventional security systems keep homeowners, and their property, safe from intruders. A smart home security system, however, offers many more benefits. Home automation technology notifies homeowners of any problems, so that they can investigate. In this paper, we discuss smart home and security, we also review the tool related to smart home security.

12

Authors: Rosslin John Robles and Tai-hoon Kim

Smart Home technology started for more than a decade to introduce the concept of networking devices and equipment in the house. According to the Smart Homes Association the best definition of smart home technology is: the integration of technology and services through home networking for a better quality of living. Many tools that are used in computer systems can also be integrated in Smart Home Systems. In this paper, we present the Technologies and tools that can be integrated or applied in Smart Home systems.

14

Authors: Sumana Barman, Amit Kumar Samanta, Tai-hoon Kim and Debnath Bhattacharyya

This paper presents a method to use View based approach in Bangla Optical Character Recognition (OCR) system providing reduced data set to the ANN classification engine rather than the traditional OCR methods. It describes how Bangla characters are processed, trained and then recognized with the use of a Backpropagation Artificial neural network. This is the first published account of using a segmentation-free optical character recognition system for Bangla using a view based approach. The methodology presented here assumes that the OCR pre-processor has presented the input images to the classification engine described here. The size and the font face used to render the characters are also significant in both training and classification. The images are first converted into greyscale and then to binary images; these images are then scaled to a fit a pre-determined area with a fixed but significant number of pixels. The feature vectors are then formed extracting the characteristics points, which in this case is simply a series of 0s and 1s of fixed length. Finally, a Artificial neural network is chosen for the training and classification process. Although the steps are simple, and the simplest network is chosen for the training and recognition process.