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Discrete maths for students of mathfac hse 19-23

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The goal of this project is to explore both the theory behind the Extended Kalman Filter and the way it was used to localize a four-wheeled mobile-robot. This can be achieved by estimating in real-time the pose of the robot, while using a pre-acquired map through Laser Range Finder (LRF). The LRF is used to scan the environment, which is represented through line segments. Through a prediction step, the robot simulates its kinematic model to predict his current position. In order to minimize the difference between the matched lines from the global and local maps, a update step is implemented. It should be noted that every measurement has associated uncertainty that needs to be taken into account when performing each step of the Extended Kalman Filter. These uncertainties, or noise, are described by covariance matrices that play a very important role in the algorithm. Since we are dealing with an indoor structured environment, mainly composed by walls and straight-edged objects, the line segment representation of the maps was the chosen method to approach the problem.

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Web security issues remain a major challenge with numerous security concerns, Distributed Denial of Service Attack (DDos) leads among these. It was evolved from DOS and its main purpose is to consume large amount of server resources so that the server cannot provide service to normal users. Attackers usually gain access to large number of computers by exploiting the vulnerabilities to set up attack armies (i.e., Botnets). This paper will review and analyse different existing DDoS detecting techniques against different parameters, and propose a new hybrid architecture for the defence mechanism of DDoS attack.