Biblio

The need for security in today's world has become a mandatory issue to look after. With the increase in a number of thefts, it has become a necessity to implement a smart security system. Due to the high cost of the existing smart security systems which use conventional Bluetooth and other wireless technologies and their relatively high energy consumption, implementing a security system with low energy consumption at a low cost has become the need of the hour. The objective of the paper is to build a cost effective and low energy consumption security system using the Bluetooth Low Energy (BLE) technology. This system will help the user to monitor and manage the security of the house even when the user is outside the house with the help of webpage. This paper presents the design and implementation of a security system using PSoC 4 BLE which can automatically lock and unlock the door when the user in the vicinity and leaving the vicinity of the door respectively by establishing a wireless connection between the physical lock and the smartphone. The system also captures an image of a person arriving at the house and transmits it wirelessly to a webpage. The system also notifies the user of any intrusion by sending a message and the image of the intruder to the webpage. The user can also access the door remotely on the go from the website.

Salt and Pepper Noise is very common during transmission of images through a noisy channel or due to impairment in camera sensor module. For noise removal, methods have been proposed in literature, with two stage cascade various configuration. These methods, can remove low density impulse noise, are not suited for high density noise in terms of visible performance. We propose an efficient method for removal of high as well as low density impulse noise. Our approach is based on novel extension over iterated conditional modes (ICM). It is cascade configuration of two stages - noise detection and noise removal. Noise detection process is a combination of iterative decision based approach, while noise removal process is based on iterative noisy pixel estimation. Using improvised approach, up to 95% corrupted image have been recovered with good results, while 98% corrupted image have been recovered with quite satisfactory results. To benchmark the image quality, we have considered various metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error) and SSIM (Structure Similarity Index Measure).