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Mobile learning (m-learning) can be considered as a new paradigm of e-learning. The developed solution enables the presentation of animations and 3D virtual reality (VR) on mobile devices and is well suited for mobile learning. Difficult relations in physics as well as intricate experiments in optics can be visualised on mobile devices without need for a personal computer. By outsourcing the computational power to a server, the coverage is worldwide.
This Master's Thesis discusses intelligent sensor networks considering autonomous sensor placement strategies and system health management. Sensor networks for an intelligent system design process have been researched recently. These networks consist of a distributed collective of sensing units, each with the abilities of individual sensing and computation. Such systems can be capable of self-deployment and must be scalable, long-lived and robust. With distributed sensor networks, intelligent sensor placement for system design and online system health management are attractive areas of research. Distributed sensor networks also cause optimization problems, such as decentralized control, system robustness and maximization of coverage in a distributed system. This also includes the discovery and analysis of points of interest within an environment. The purpose of this study was to investigate a method to control sensor placement in a world with several sources and multiple types of information autonomously. This includes both controlling the movement of sensor units and filtering of the gathered information depending on individual properties to increase system performance, defined as a good coverage. Additionally, online system health management was examined in this study regarding the case of agent failures and autonomous policy reconfiguration if sensors are added to or removed from the system. Two different solution strategies were devised, one where the environment was fully observable, and one with only partial observability. Both strategies use evolutionary algorithms based on artificial neural networks for developing control policies. For performance measurement and policy evaluation, different multiagent objective functions were investigated. The results of the study show that in the case of a world with multiple types of information, individual control strategies performed best because of their abilities to control the movement of a sensor entity and to filter the sensed information. This also includes system robustness in case of sensor failures where other sensing units must recover system performance. Additionally, autonomous policy reconfiguration after adding or removing of sensor agents was successful. This highlights that intelligent sensor agents are able to adapt their individual control policies considering new circumstances.
The idea of this game is to use a flashcard system to create a short story in a foreign language. The story is developed by a group of participants by exchanging sentences via a flashcard system. This way the participants can learn from each other by knowledge sharing without fear of making mistakes because the group members are anonymous. Moreover they do not need a constant support from a teacher.
Under a grant of the German ProInno program („Erhöhung der Innovationskompetenz mittelständischer Unternehmen“)the Hochschule Offenburg participated during the past 2 years in an industry project prototyping a new type of service for modern Air Traffic Control (ATC) applications.<br> Objective of the project has been the joint development of hardware and software components for a so-called TIS-B (Traffic Information System - Broadcast) support infrastructure to enable new cockpit applications increasing the air situation awareness for pilots of commercial airliners [1]. At the core of the project is a space-time-scheduler, controlling a battery of TIS-B groundstations over a Wide Area Surveillance Network [4].<br> The project has been successfully concluded and is currently in its evaluation phase. Industry partner was the Karlsruhe-located company COMSOFT, international market leader in ATC sensor networks.