http://hdl.handle.net/10453/204 2013-05-22T03:40:26Z http://hdl.handle.net/10453/17747 Combating Security Threats via Immunity and Adapatbility in Cognitive Radio Networks Nikodem Jan; Chaczko Zenon; Nikodem Maciej; Klempous Ryszard; Wickramasooriya Ruckshan Jnaos Fodro, Ryszard Klempous, Caremn Paz-Suarez Araujo In this chapter we shall consider security, immunity and adaptability aspects of Cognitive Radio (CR) networks and its applications. We shall cover design of a immunity/adaptability and security simulation model for cognitive radio and discuss results of conducted experiments using Matlab simulation tools and Crossbow's XMesh using MoteWorks software platform. The aim of this chapter is to provide an overview of various applications of CR as well as methods of combating security threats faced when applying the CR technology. The immunity/ adaptability functions, their benefits and applications in CR are analysed, along with the challenges faced. We shall discuss in detail how our immunity and adaptability model can mitigate security threats faced by CR and carry out selected research on techniques that can help to mitigate malicious attacks and provide examples of simulation experiments. 2012-01-01T00:00:00Z http://hdl.handle.net/10453/17748 Face Subspace Learning Bian Wei; Tao Dacheng Stan Z. Li, Anil K. Jain NA 2011-01-01T00:00:00Z http://hdl.handle.net/10453/17750 System Wide Tracking of Individuals Madden Christopher; Piccardi Massimo; Van Den Hengel Anton Paolo Remagnino, Lakhmi C. Jain, Dorothy N.Monekosso Tracking the movements of people around surveillance systems is becoming increasingly important in the current security conscious environment. This chapter discusses recent research to assist with tracking the movement of individuals throughout the full surveillance area, even when those views do not overlap and may be separated by significant distances. This is due to advances in tracking within a camera view, which allows feature models to be built of an individual. The feature models can include a range of shape, appearance, and transition features appropriate for use with surveillance footage. These may also utilise spatial information about the objects from both the real world and image planes to increase individual discriminitivity. These feature models can be combined and compared within a statistical framework to determine the probability of any two tracks being made by the same individual. It is this combination of such tracks that allows for system wide tracking of individuals to be performed. Techniques are also presented to improve the accuracy of the feature information. These include the application of spatial or temporal smoothing, the identifcation and removal of signifcant feature errors, as well as the mitigation of other potential error sources like illumination. The results of both the individual features, and the combined system wide tracks are presented based upon an analysis of individuals observed in realistic surveillance footage. These show that current camera technology will not produce a fully automated system, but can provide signifcant information to assist security operators. 2011-01-01T00:00:00Z http://hdl.handle.net/10453/17749 Human Action Recognition Based on Radon Transform Chen Yan; Wu Qiang; He Xiangjian Weisi Lin, Dacheng Tao, Janusz Kacprzyk, Zhu Li, Ebroul Izquierdo, and Haohong Wang A new feature description is used for human action representation and recognition. Features are extracted from the Radon transforms of silhouette images. Using the features, key postures are selected. Key postures are combined to construct an action template for each action sequence. Linear Discriminant Analysis (LDA) is applied to obtain low dimensional feature vectors. Different classification methods are used for human action recognition. Experiments are carried out based on a publicly available human action database. 2011-01-01T00:00:00Z