Abstract
The herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
An alternative approach to compute the signal to noise ratio of analogue to digital converters based on the computation of the cross-correlation coefficient of the captured response is proposed here. It is shown, after simulation and experimental results, that this approach allows obtaining good accuracy results with the added advantages of not requiring coherent sampling and high purity sine wave stimuli.

Abstract

Abstract
The aim of this paper is to propose a recommendation system that considers users functionality levels regarding physical or psychological limitations. This paper describes a set of models and algorithms used under a tourism recommendation system based in users and points-of-interest (POI) profiles. Also, this proposal considers a different manner to classify POI including their accessibility levels, mapped with similar physical and psychological issues. In this study, based on the Design Science Research methodology, an architecture is proposed and a touristic recommendation system prototype where users are modelled with new types of information in addition to traditional approaches such as their levels of functionality regarding a set of physical and intellectual issues, is also presented. POIs are also modelled with the same information structure and maintain knowledge on their limitations against some health conditions.

Abstract
Atmospheric profiling is a requirement for controlling wide-field adaptive optics (AO) instruments, analysing the AO performance with respect to the observing conditions and predicting the point spread function (PSF) spatial variations. We present PEPITO, a new concept for profiling atmospheric turbulence from post facto tip-tilt (TT) corrected shortexposure images. PEPITO utilizes the anisokinetism effect in the images between several stars separated from a reference star, and then produces the profile estimation using a model-fitting methodology, by fitting to the long-exposure TT-corrected PSF. PEPITO has a high sensitivity to bothC2 n(h) and L0(h) by relying on the full telescope aperture and a large field of view(FOV). It then obtains a high vertical resolution (1-400 m) configurable by the camera pixel scale, taking advantage of fast statistical convergence (of the order of tens of seconds). With only a short-exposure capable large format detector and a numerical complexity independent of the telescope diameter, PEPITO perfectly suits accurate profiling for night optical turbulence site characterization or AO instruments operations. We demonstrate, in simulation, that the C2 n(h) and L0(h) can be estimated to better than 1 per cent accuracy, from fitted PSFs of magnitude V = 11 on a D = 0.5m telescope with a 10 arcmin FOV.