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Nowadays we are living the apogee of the Internet based technologies and consequently web 2.0 communities, where a large number of users interact in real time and share opinions and knowledge, is a generalized phenomenon. This type of social networks communities constitute a challenge scenario from the point of view of Group Decision Making approaches, because it involves a large number of agents coming from different backgrounds and/or with different level of knowledge and influence. In these type of scenarios there exists two main key issues that requires attention.Firstly, the large number of agents and their diverse background may lead to uncertainty and or inconsistency and so, it makes difficult to assess the quality of the information provided as well as to merge this information. Secondly, it is desirable, or even indispensable depending on the situation, to obtain a solution accepted by the majority of the members or at least to asses the existing level of agreement. In this contribution we address these two main issues by bringing together both decision Making approaches and opinion dynamics to develop a similarity-confidence-consistency based Social network that enables the agents to provide their opinions with the possibility of allocating uncertainty by means of the Intuitionistic fuzzy preference relations and at the same time interact with like-minded agents in order to achieve an agreement.

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Low-level computer vision algorithms have high computational requirements. In this study, we present two realtime architectures using resource constrained FPGA and GPU devices for the computation of a new algorithm which performs tone mapping, contrast enhancement, and glare mitigation. Our goal is to implement this operator in a portable and battery-operated device, in order to obtain a low vision aid specially aimed at visually impaired people who struggle to manage themselves in environments where illumination is not uniform or changes rapidly. This aid device processes in real-time, with minimum latency, the input of a camera and shows the enhanced image on a head mounted display (HMD). Therefore, the proposed operator has been implemented on batteryoperated platforms, one based on the GPU NVIDIA ION2 and another on the FPGA Spartan III, which perform at rates of 30 and 60 frames per second, respectively, when working with VGA resolution images (640 × 480).