Privacy is an increasingly important subject for organizations. Nowadays, organizations (unknowingly) process vast amounts of personal data of their customers in numerous different information systems. There are strict legislations regarding the processing of personal data, and from mid-2016, these legislations will only get more strict with the introduction of the General Data Protection Regulation in the European Union. In an approach to ensure compliance with these legislations, different techniques such Privacy Enhancing Technologies, Privacy-by-Design and Privacy Design Strategies were introduced in the past decades. However, these techniques tend to be defined in such a high-level of abstraction that they are hard to use in practice. This paper discusses and explains various software techniques which can help to design information systems that can better protect the privacy of their users. Next, these techniques are combined as a solution named Privacy Management System. This system is able to ensure and enforce full data processing transparency of an organization and should close the gap between the privacy legislations and software development.
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.
The CPU scheduling is the basis of multi-programming operating systems. By switching the
CPU among processes, the operating system can make the computer more productive. The
scheduler controls the way processes are managed in the operating system.
Linux supports preemptive multitasking, this means that the process scheduler decides which process
runs and when.
Balance performance across different computer configurations is one challenge in modern operating
systems.Linux has two separate process-scheduling algorithms.
If a Linux system performs similar tasks in a regular manner, it could be useful to implement
optimizations to the Linux scheduler to optimize the performance of those tasks.
In this project, we analyze and evaluate the impact of changing the kernel values on the performance
of the calculation of 8,765,4321 digits of pi using the Leibniz formula measuring the time that the
system takes to perform the calculation.
Constanza Madrigal Reyes and Ismael Lizárraga González