Abstract
The goal of this work is to develop a computational model of the human retina and simulate light scattering through its structure aiming to shed light on data obtained by optical coherence tomography in human retinas. Currently, light propagation in scattering media is often described by Mie's solution to Maxwell's equations, which only describes the scattering patterns for homogeneous spheres, thus limiting its application for scatterers of more complex shapes. In this work, we propose a discontinuous Galerkin method combined with a low-storage Runge-Kutta method as an accurate and efficient way to numerically solve the time-dependent Maxwell's equations. In this work, we report on the validation of the proposed methodology by comparison with Mie's solution, a mandatory step before further elaborating the numerical scheme towards the propagation of electromagnetic waves through the human retina.

Abstract
We describe the use of a phase-sensitive optical time domain reflectometer (phi OTDR) over an ultra-long Raman fiber laser cavity allowing fully distributed detection of vibrations over 125 km. Compared to a first-order Raman-assisted phi OTDR, this scheme shows an enhanced signal-to-noise ratio (SNR). This is due to the fact that the relative intensity noise introduced by the Raman amplification is mostly transferred to a lower frequency range, where the balanced detection implemented in the setup provides better suppression of the common-mode noise. The sensor was able to measure vibrations of up to 380 Hz (limit set by the time of flight of light pulses) in a distance of 125 km with a resolution of 10 m and an average SNR of 8 dB with no postprocessing. This implies a > 3 dB improvement in SNR over a first-order Raman-assisted setup with similar characteristics.

Abstract
The Ada language has for long provided support for the development of reliable real-time systems, with a model of computation amenable for real-time analysis. To complement the already existent multiprocessor support in the language, an ongoing effort is underway to extend Ada with a fine-grained parallel programming model also suitable for real-time systems. This paper overviews the model which is being proposed, pointing out the main issues still open and road ahead. © 2015 IEEE.

Abstract
User modelling has become a central subject for anybody interested in understanding how users interact with technology. Personalization is a key issue in an era when there is so much information and so many people interacting in so many ways. Modern users desire a customized experience that adapts itself to their requirements and understands what they need even before they notice it. In order to morph any system into an adapting one, every relevant interaction with its users has to be maintained. Then, a mathematical structure capable of discovering patterns amongst that information is necessary, being able to classify users according to the roles they play. With a correct user categorization, the system knows when, how and what to do to adapt its content, via a mixed-initiative approach. In this paper, an artificial neural network is selected as classifier and users are divided in three roles, from beginners to experts. ADSyS, the target system of this proposal, adapts its content based on who is operating it, providing a higher usability. This guide on how to adapt a system to its users is built as part of ADSyS, but is intended to be generalized as a foundation to other systems.

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Abstract