Intelligent security system detects the hidden objects in the smart grid

Abstract

<p>Monitoring the general public gathered in large numbers is one of the most challenging tasks faced by the law and order enforcement team. There is swiftly demand to that have inbuilt sensors which can detect the concealed weapon, from a standoff distance the system can locate the weapon with very high accuracy. Objects that are obscure and invisible from human vision can be seen vividly from enhanced artificial vision systems. Image Fusion is a computer vision technique that fuses images from multiple sensors to give accurate information. Image fusion using visual and infrared images has been employed for a safe, non-invasive standoff threat detection system. The fused imagery is further processed for specific identification of weapons. The unique approach to discover concealed weapon based on DWT in conjunction with Meta heuristic algorithm Harmony Search Algorithm and SVM classification is presented. It firstly uses the traditional discrete wavelet transform along with the hybrid Hoteling transform to obtain a fused imagery. Then a heuristic search algorithm is applied to search the best optimal harmony to generate the new principal components of the registered input images which is later classified using the K means support vector machines to build better classifiers for concealed weapon detection. Experimental results demonstrate the hybrid approach which shows the superior performance.</p>

The security of RC4 algorithm using keys generation depending on user's retina

“…T is a pixel average-gray value. The computation of information entropy is described by (5) to a pair of values (g, t) [28].…”

“…The security of RC4 algorithm using keys generation depending on user's retina (Huda M. Salih) 453 aforementioned. The significant amount of RNG bits are delivered clearly and so a passive intruder has an easy ability to read the RNG performance and may influence any shortcomings contained there [5], [6].…”

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

“…Recognition algorithms rely on individual variances in physical or behavioral traits [2]. A biometric recognition technology that is used to detect, recognize, identify, or authenticate a person in a digital image or video frame [3], computer vision [4], machine learning [5], real-web services [6], computer games [7], and time video [8]. Face recognition, authentication, tracking, expression categorization approaches, and feature expression mechanics are all under investigation [9].…”