Abstract
Technological evolution has led to the emergence of a set of solutions suitable to support mobility and ubiquity scenarios. Wireless computing and mobile devices together with the miniaturization of sensors and actuators, which are now embedded in physical spaces, are today's reality. This phenomenon opened the door to a set of opportunities for reengineering how we perceive a given fact or situation and how we act on it. With regard to the delivery of information to users of a given physical space, there is now the possibility of radically transforming the mechanisms of interaction between the space and the user, redesigning the entire experience of interaction. This change allows the user to see the physical space around him adapt to himself and provide him with contextualized and personalized information according to his profile of interest. This approach can improve the way we manage customer relationships in a given business context. This article presents an overview of the state of the art of intelligent spaces and analyzes the potential of indoor positioning systems and techniques, and proposes a conceptual model for the detection of users in physical spaces and the consequent adaptation of an intelligent physical space to provide information aligned with the user's interest profile and in accordance with their privacy rules.

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In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art.

Abstract
Near-surface optical waveguides were fabricated in alkaline earth boro-aluminosilicate glass (Eagle2000), by femtosecond laser direct writing, using two distinct approaches. First, the capability of directly inscribing optical waveguides close to the surface was tested, and then, compared to the adoption of post writing wet etching to bring to the surface waveguides inscribed at greater depths. Laser ablation was found to limit the minimum surface to core center distance to 6.5 mu m in the first method, with anisotropic wet etching limiting the latter to 3 mu m without any surface deformation; smaller separations can be achieved at the cost of the planar surface topography. Furthermore, the waveguide's cross-section was seen to vary for laser inscription nearing the surface, observations that were also corroborated by its distinct guiding characteristics when compared to the adoption of post writing wet etching. The spectral analysis (in the 500-1700 nm range) also evidenced an increase in insertion loss for longer wavelengths and smaller surface to core center separations, caused, most likely, by coupling loss due to the interaction between the propagating mode and the surface. Different lengths of waveguide exposed to the surface were also tested, revealing that scattering loss due to surface roughness is not an issue at the centimeter scale.

Abstract
In the last decades due to technological advances, Robotics has change its paradigm, as well as, its course of development. Therefore, a new generation of robots has emerged, the Socially Assistive Robotics. These robots aim to improve the assistance to human users through social rather than physical interaction. Consequently, many are the developments made through the use of social robots in mental healthcare scenarios, regarding the elderly population and children or young adults with mental disorders, in order to either prevent cognitive decline, improve psycho-social outcomes or the patient capabilities and lifetime. However, few breakthroughs have been made in the mental disorders field that differ from studies with people with Autism Spectrum Disorders or that are conducted with adults. This study aims to demonstrate that humanoid NAO can improve attention in adults with mental disorders during certain task and kept through several sessions over time.