-Recently the developments of Ubiquitous Computing and Augmented Reality technologies have made many changes on the existing applications. However, the contents used in the applications have been not revised conceptually. Thus, an expansive representation of the contents is required to adapt resources to new computing environments, and to reflect new emerging features such as realism, mobility and intelligence. In this paper, we address a noble concept: Ubiquitous Computing enabled Contents (u-Contents). Firstly, three key features, u-Realism, u-Mobility and u-Intelligence, are reviewed. Secondly, the realization issues are explained based on the properties of three key features. Lastly, u-Contents are discussed from the viewpoint of possible applications. Index Terms-Ubiquitous Computing, Augmented Reality, Ubiquitous Virtual Reality, Contents, and Mobility.
Iare distributed around users. The new environment enables
n Ubiquitous Computing Environment, computing resources
users to carry out impossible tasks as they can do in Virtual
Reality (VR) space. Most attractive users’ ability in VR is that
the contents in the space can be transformed according to users’
intentions and emotions. Recently, various researches of
Augmented Reality (AR) have shown possibility to realize this
ability of VR in real environment [
In this paper, we address a noble concept: Ubiquitous
Computing enabled Contents (u-Contents). Unlike [
In this chapter, the details of three key features in u-Contents are discussed with concrete examples. Since contents used in AR applications are most likely to be accepted as future u-Contents, we deal the examples with AR contents.
The main differences between conventional contents and u-Contents are caused from the contexts obtained from physical space. Most AR applications already have used the camera context, but their utilizations are limited in camera pose estimation. Various fusions can be generated to accelerate existing algorithms by helps of context-awareness technologies.
Contents
(a)
User Context Environment Context
AR Contents u-Contents (b) Figure 2. u-Contents realization (a) 3D contents and images are converted to AR contents by applying camera contexts (b) u-Contents are obtained by using adequate user’s and environmental contexts
The realism of contents is enhanced when all environmental contexts are mapped to parameters in a mixed space. For example, virtual shadows of the virtual objects as shown in Figure 3 can be seen as realistic when the real light sources are extracted from the environment correctly. In the existing AR applications using only camera sensors, the generation of these mapping is a challenging and has limitations due to a lack of information.
(a) (b) Figure 3. Realism enhanced by a virtual shadow: (a) flower augmentation without a shadow (b) the augmented shadow based on the light source
In u-Contents, from the viewpoint of the implementation, the meaningful contexts are collected from a hundred of sensors distributed in the real space. Then, the contexts are transformed to the existing parameters in AR space. Table 1 shows the context examples and how the contexts assist in realizing u-Realism in the applications. Note that the five senses should be considered in the ideal cases. These technologies enable contents to move freely among heterogeneous devices via network with predefined manners. In addition, contents with u-Mobility can transform themselves or move to other devices according to user’s contexts. This property also includes the transfers among different spaces such as VR to AR. Thus, u-Mobility adaptively yields a path to the contents. Table 2 shows the examples where the user’s contexts are required.
converted automatically.
Network resources are selected automatically to share contents.
The research on the intelligent contents was started with the questions, ‘Which actions are regarded as proper conducts?’ and ‘How the living subject can be imitated?’. The main approach in u-Intelligence is to put ‘Personality’, ‘Emotion’ and ‘Sociality’ into contents with helps of context-awareness. Figure 4 shows the low-level intelligence implemented with a virtual robot character.
(a) (b) (c) Figure 4. Example of a low-level intelligence: 3D running robot crashes with real blocks, this shows the simple reaction against the ‘crash’ event
In u-Contents, u-Intelligence has an important role in generating reactions. Contents act by themselves with self-motivations. However, the abundant sensor data is essential to let the contents decide their behaviors. To determine (or generate) responses, not only the user’s context, but also the environmental context are combined and parameterized.
The proposed concept toward the realization of u-Contents is going to be applied various AR applications. Especially, u-Mobility is deeply related to the design and development of the mobile AR services. And u-Realism can drastically reduce the computational burdens, also enhance reality in all domains. In addition, u-Intelligence will bring the mechanism for seamless interactions in AR applications.