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Nowadays, it is assumed of many applications, companies and parts of the society to be always available online. However, according to [Times, Oct, 31 2011], 73% of the world population do not use the internet and thus aren't “online” at all. The most common reasons for not being “online” are expensive personal computer equipment and high costs for data connections, especially in developing countries that comprise most of the world’s population (e.g. parts of Africa, Asia, Central and South America). However it seems that these countries are leap-frogging the “PC and landline” age and moving directly to the “mobile” age. Decreasing prices for smart phones with internet connectivity and PC-like operating systems make it more affordable for these parts of the world population to join the “always-online” community. Storing learning content in a way accessible to everyone, including mobile and smart phones, seems therefore to be beneficial. This way, learning content can be accessed by personal computers as well as by mobile and smart phones and thus be accessible for a big range of devices and users. A new trend in the Internet technologies is to go to “the cloud”. This paper discusses the changes, challenges and risks of storing learning content in the “cloud”. The experiences were gathered during the evaluation of the necessary changes in order to make our solutions and systems “cloud-ready”.
This work describes a camera-based method for the calibration of optical See-Through Glasses (STGs). A new calibration technique is introduced for calibrating every single display pixel of the STGs in order to overcome the disadvantages of a parametric model. A non-parametric model compared to the parametric one has the advantage that it can also map arbitrary distortions. The new generation of STGs using waveguide-based displays [5] will have higher arbitrary distortions due to the characteristics of their optics. First tests show better accuracies than in previous works. By using cameras which are placed behind the displays of the STGs, no error prone user interaction is necessary. It is shown that a high accuracy tracking device is not necessary for a good calibration. A camera mounted rigidly on the STGs is used to find the relations between the system components. Furthermore, this work elaborates on the necessity of a second subsequent calibration step which adapts the STGs to a specific user. First tests prove the theory that this subsequent step is necessary.