The Virtual Materials Marketplace (VIMMP) project, which develops an open platform for providing and accessing services related to materials modelling, is presented with a focus on its ontology development and data technology aspects. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace frontend.

A wearable navigation system for visually impaired and blind people in unknown indoor and outdoor environments is presented. This system will map and track the position of the pedestrian during the exploration of the unknown environment. In order to build this system the well known Simultaneous Localization and Mapping (SLAM) from mobile robotics will be implemented. Once a map is created the user can be guided efficiently by a route selecting method. The user will be equipped with a short range laser, an inertial measurement unit (IMU), a wearable computer for data processing and an audio bone headphones. This system does not intent to replace the use of the white cane. However, the purpose is to gather contextual information to aid the user in navigating with the white cane.

The practical adaption of interface solutions for visual impaired and blind people is limited by simplicity and usability in practical scenarios. Different solutions (e.g. Drishti [1]) focuses upon speech or keyboard interfaces, which are not efficient or transparent in every-day environments. As an easy and practical way to achieve human-computerinteraction, in this paper hand gesture recognition was used to facilitate the reduction of hardware components. Additionally a qualitative user study was performed to compare learning curves of different subjects with and without prior knowledge of gesture recognition devices, interpreting the readings from a sensitive surface by machine learning algorithms. The user study was made using well-known machine learning algorithms applied to recognizing symbols from the graffiti handwriting system [2] and the WEKA data mining software [3] for comparing individual machine learning approaches.

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