The goal of this paper is to analyze content shared in social media by presidential candidates in the United States of America. In particular, Twitter posts are being inspected, using advanced methods of big data analysis. In this report we describe the usage of tools, such as Map Reduce, collaborative filtering, k-means clustering and others, to determine different features of candidates' communication. We also made an application for recommending ideas to candidates based on the their Twitter analysis. At the end we show overview of our findings and propose directions for further analysis.
Chu Pak Sum, Filip Strycko, Niclas Ogeryd, Zhao Che
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.