TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Sultana, Razia
A1  - Christ, Andreas
T1  - Research on System Architecture for Device Independent Applications for 3D Imaging and Virtual Reality
T2  - mLearn2011 Conference Proceedings
N2  - Today's network landscape consists of quite different network technologies, wide range of end-devices with large scale of capabilities and power, and immense quantity of information and data represented in different formats. Research on 3D imaging, virtual reality and holographic techniques will result in new user interfaces (UI) for mobile devices and will increase their diversity and variety. A lot of efforts are being done in order to establish open, scalable and seamless integration of various technologies and content presentation for different devices including mobile considering individual situation of the end user. This is very difficult because various kinds of devices used by different users or in different times/parallel by the same user which are not predictable and have to be recognized by the system in order to identify device capabilities. Not only the devices but also Content and User Interfaces are big issues because they could include different kinds of data format like text, image, audio, video, 3D Virtual Reality data and other upcoming formats. A very suitable and useful example of the use of such a system is mobile learning because of the large amount of varying devices with significantly different features and functionalities. This is true not only to support different learners, e.g. all learners within one learning community, but also to support the same learner using different equipment parallel and/or at different times. Those applications may be significantly enhanced by including virtual reality content presentation. Whatever the  purposes are, it is impossible to develop and adapt content for all kind of devices including mobiles individually due to different capabilities of the devices, cost issues and author‘s requirement. A solution  should be found to enable the automation of the content adaptation process.
KW  - Mobile Learning
KW  - generalized content
KW  - device detection
KW  - content adaptation
KW  - device independent learning
Y1  - 2011
UR  - https://iamlearn.org/wp-content/uploads/2018/01/mLearn2011_Proceedings.pdf
SP  - 411
EP  - 416
ER  -