Abstract
Achieving affordable high speed fiber optic communication networks for airplane systems has proved to be challenging. In this paper we describe a summary of the EU Framework 7 project DAPHNE (Developing Aircraft Photonic Networks). DAPHNE aimed to exploit photonic technology from terrestrial communications networks, and then develop and optimize aircraft photonic networks to take advantage of the potential cost savings. The main areas of emphasis were on: multiplexing networks; providing standard components; simplifying installation; and reducing through life support costs. DAPHNE (fifteen partners from seven nations) finished in February 2013; and was supported by the European Commission?s Seventh Framework Programme, although the consortium members are continuing with in-house developments. © 2013 SPIE.

Abstract

Abstract
The present phenomenon of technology convergence is blurring away the frontiers between the Internet and the TV, operating a shift on the way TV is consumed. TV viewers have now access to a huge selection of TV programming as well as online contents, either previously broadcasted or natively produced for the Internet. This reality creates new necessities whilst opening new opportunities for the creation of services capable of filtering this information and presenting the user with the most relevant content. This article describes an innovative hybrid strategy for delivering recommendations of TV content to individual users. It was developed specifically for the TV entertainment services of hotels, but it can be applied to any multimedia consumption service. Without requiring users to explicitly rate the programs they have watched, it is still able to recommend similar programs to similar users. It adopts an improved Pearson correlation method to establish similarities between different users, comparing profiles that have been automatically generated based on the user viewing history. It builds a predicted user profile, which is then used within a content-based approach to generate recommendations.

Abstract
Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate recognition of user activity contexts, using smart-phone built-in sensors. We describe and illustrate our context recognition model and then demonstrate its application in a context aware mobile multimedia recommendation system.

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Abstract