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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.
In the brain-cell microenvironment, diffusion plays an important role: apart from delivering glucose and oxygen from the vascular system to brain cells, it also moves informational substances between cells. The brain is an extremely complex structure of interwoven, intercommunicating cells, but recent theoretical and experimental works showed that the classical laws of diffusion, cast in the framework of porous media theory, can deliver an accurate quantitative description of the way molecules are transported through this tissue. The mathematical modeling and the numerical simulations are successfully applied in the investigation of diffusion processes in tissues, replacing the costly laboratory investigations. Nevertheless, modeling must rely on highly accurate information regarding the main parameters (tortuosity, volume fraction) which characterize the tissue, obtained by structural and functional imaging. The usual techniques to measure the diffusion mechanism in brain tissue are the radiotracer method, the real time iontophoretic method and integrative optical imaging using fluorescence microscopy. A promising technique for obtaining the values for characteristic parameters of the transport equation is the direct optical investigation using optical fibers. The analysis of these parameters also reveals how the local geometry of the brain changes with time or under pathological conditions. This paper presents a set of computations concerning the mass transport inside the brain tissue, for different types of cells. By measuring the time evolution of the concentration profile of an injected substance and using suitable fitting procedures, the main parameters characterizing the tissue can be determined. This type of analysis could be an important tool in understanding the functional mechanisms of effective drug delivery in complex structures such as the brain tissue. It also offers possibilities to realize optical imaging methods for in vitro and in vivo measurements using optical fibers. The model also may help in radiotracer biomarker models for the understanding of the mechanism of action of new chemical entities.