Abstract
In this work the performance assessment of a digitised radio-over-fibre (DRoF) system is presented considering the transmission of orthogonal frequency division multiplexing (OFDM) signals. Specifically, the impact of the high peak-to-average-ratio (PAPR) in the ADC/DAC quantization noise is addressed by means of simulation results. In order to minimize the impact of the digitisation process, we present a study of the improvement of the system performance when commonly used pre-distortion techniques suitable for PAPR reduction are considered. Additionally, the performance of the selected pre-distortion techniques is assessed experimentally, in terms of the modulation error ratio (MER) in an OFDM-DRoF link transmission, considering the impact of optical attenuation. It is observed that the transmission of OFDM signals in DRoF is severely affected by the inherent high PAPR levels and that, by using pre-distortion techniques with ADC/DAC with 7 bits, it is possible to increase the modulation error ratio (MER) by 15 dB or to increase the optical fibre by 6.5 km (8.5% increase) without the need of additional complexity at the OFDM receiver.

Abstract
The Web is rapidly becoming the prime medium for human socialization. As it evolves towards an information-centric operation, it records everything and forgets nothing, assuming that every online resource disclosed by people (photos, posts, multimedia files, etc.) is permanently valid and is to be stored forever. However, throughout their lives, people tend to change, both in their habits as well as in their views and opinions. In many situations, as the years go by, information released loses relevance or people may decide they no longer want others to access information they have previously published. The work presented in this paper strives for a new information persistence paradigm, whereby the enforcement of "digital forgetting" is implemented over an information-centric model for the Internet. The defined solution enables the definitive elimination of digital objects, either on-demand or on a pre-scheduled basis, and, hence, their "forgetting." The solution, conceived within the framework of the European project CONVERGENCE, is based on the employment of metadata descriptions about resources, which unambiguously identify their rightful owners. This additional data is efficiently bound to the resource through the use of an extended version of the MPEG-21 Digital Item specification, and its prescriptions are enforced by CONVERGENCE's distributed provisions.

Abstract

Abstract
Media content recommendations for a mobile user based on his changing contextual preferences, otherwise called context-aware media recommendations, constitute a very important challenge. Context-aware media recommendation systems take context information such as user preferences, activities, time, location, device, and network capabilities as inputs for media recommendations, whereas the traditional recommendation systems use only user preferences in the form of ratings to deliver media recommendations. This paper presents a generic high-level architecture of context-aware recommendations, discussing its key techniques and solutions, which are based on context acquisition, recognition, and representations, using MPEG-21 and ontology model, and a contextual user profiling process, as well as MPEG-7 for media description model and media presentation adaptation.

Abstract
The unprecedented advancements in broadband and mobile networks, the proliferation and the incredible appeal of smart devices such as smartphones, and the recent emergence of cloud computing are poised to drive the next generation of ubiquitous media delivery and consumption. As more media services become available, mobile users will waste invaluable time, seeking relevant media items. Therefore, to deliver relevant media services, with rich experience to mobile users, media service providers must consider the services that match user's contextual consumption choices. This paper proposes context-aware recommendation techniques to support the delivery of contextually relevant cloud-based media items to mobile users. The recommendation service works with a contextual user profile service, which relates user preferences to contexts in which such preferences are expressed, relying on a context recognition service, which identifies the user's dynamic contextual situation from smartphone built-in sensors. Experimental evaluations, using real world user and online movie data, established that the context-aware recommendation techniques are promising. © 2014 IEEE.

Abstract
The incredible appeals of smartphones and the unprecedented progress in the development of mobile and wireless networks in recent years have enabled ubiquitous availability of myriad media contents. Consequently, it has become problematic for mobile users to find relevant media items. However, context awareness has been proposed as a means to help mobile users find relevant media items anywhere and at any time. The contribution of this paper is the presentation of a context-aware media recommendation framework for smart devices (CAMR). CAMR supports the integration of context sensing, recognition, and inference, using classification algorithms, an ontology-based context model and user preferences to provide contextually relevant media items to smart device users. This paper describes CAMR and its components, and demonstrates its use to develop a context-aware mobile movie recommendation on Android smart devices. Experimental evaluations of the framework, via an experimental context-aware mobile recommendation application, confirm that the framework is effective, and that its power consumption is within acceptable range. © 2014 Springer International Publishing Switzerland.