Abstract
In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

Abstract
This paper presents the results from search and rescue missions performed with the aerial robot OTUS in the the context of the ISEP/INESC TEC aerial robotics team participation on the euRathlon 2015 robotics competition. The multi-domain (land, sea and air) search and rescue scenario is described and technical solution adopted is presented with emphasis on the perception system. The calibration of the image based system is addressed. Results from the operational missions performed are also discussed. The aerial autonomous vehicle was able to successfully perform multiple tasks from the aerial reconnaissance and 3D mapping to the identification of leaking pipes, obstructed passages and missing workers. The system was validated a realistic operational scenario and won the Grand Challenge in cooperation with land and marine robotics partner teams. This challenge was the first time that a real time collaborative team of aerial, land and marine robots was deployed successfully in a search and rescue mission. © 2018 Springer Science+Business Media B.V., part of Springer Nature

Abstract
This paper presents the modeling and simulation of a spherical autonomous underwater vehicle. The robot was developed under the European Union H2020 innovation action UNEXMIN for the exploration of underground flooded mines, and is a small spherical robot with thrusters and an internal pendulum for pitch control. A model of the vehicle is presented, initially without the pendulum, then an extended formulation is derived accounting for a multibody dynamic description of the system. Experimental identification results for the determination of drag parameters are presented as well. A Modelica based simulator is developed for dynamic simulation of the vehicle, and is integrated with the Matlab/Simulink environment. The simulator is then validated based on preliminary experimental results. © 2018 IEEE.

Abstract
Crowdsourcing has become an essential source of information for tourism stakeholders. Every day, tourists leave large volumes of feedback data in the form of posts, likes, textual reviews, and ratings in dedicated crowdsourcing platforms. This behavior makes the analysis of crowdsourced information strategic, allowing the discovery of important knowledge regarding tourists and tourism resources. This paper presents a survey on the analysis and prediction of hotel ratings from crowdsourced data, covering both off-line (batch) and on-line (stream-based) processing. Specifically, it reports multiple rating-based profiling, recommendation, and evaluation techniques. While most of the surveyed works adopt entity-based multicriteria profiling, prerecommendation filtering, and off-line processing, the latest hotel rating prediction trends include feature-based, trust and reputation modeling, postrecommendation filtering, and on-line processing. Additionally, since the volume of crowdsourced ratings tends to increase, the deployment of profiling and recommendation algorithms on high-performance computing resources should be further explored. This article is categorized under: Application Areas > Internet and Web-Based Applications. © 2018 Wiley Periodicals, Inc.

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