One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Over-the-top (OTT) content is being delivered, as the Internet has now become the `de facto` path for video transmission, with video data representing over 75% of the entire network traffic, and the users' desires to consume more content continuing to show exponential growth. This paper proposes a new approach to improve the clients' QoE by using network awareness models as a way to forecast the number of future video content chunks to be cached. Once cached, these chunks become closer to the client, which mitigates the consequences of suboptimal network performance on bitrate-adaptive HTTP video streaming. Two prefetching approaches are proposed: an Analytic Hierarchy Process (AHP) through a weighted function on the measured quality of service metrics, and a Neural Network to estimate and maximize the user experience without the normal service delivery degradation. Our evaluation shows that AHP provides a suboptimal behavior under constrained bandwidth, while a neural network model is able to infer network behavior and predict an optimized MOS, allowing it to better optimize the caching system and the required prefetching to adequately improve QoE.

Bike sharing systems offer a convenient, ecologic, and economic transport mode that has been increasingly adopted. However, the distribution of bikes is often unbalanced, which decreases user satisfaction and potential revenues. Moreover, bike sharing literature is mostly focused on the prediction of demand on large scale systems and uses simulations for the assessment of relocation operations to increase the number of utilizations. We propose prediction models based on machine learning approaches to improve the bike sharing re-balancing in a small city of Portugal. The algorithm aims to improve three metrics, namely (1) increase the number of utilizations, (2) reduce the number of stations without bikes, (3) reduce the time without available bikes in the stations. The relocation operations are validated using real data. Our findings show that (a) the estimated number of utilizations created by this system is substantially higher than the current system by 223%, (b) our model allows the correct identification of more 70%, 165%, 249% empty stations with the same or substantially higher precision than the existing approach, (c) the total time of bike unavailability reduced by the predictive model is 283% higher than the time reduced by current approach (1,394,454 vs 363,971 minutes).

This paper presents the positioning, navigation and awareness (PNA) system developed for the Underwater Robotic Mining System of the !VAMOS! project [1]. It describes the main components of the !VAMOS! system, the PNA sensors in each of those components, the global architecture of the PNA system, and its main subsystems: Position and Navigation, Realtime Mine Modeling, 3D Virtual reality HMI and Real-time grade system. General results and lessons learn during the first mining field trial in Lee Moor, Devon, UK during the months of September and October 2017 are presented.

This paper describes the UX-1 underwater mine exploration robotic system under development in the context of the UNEXMIN project. UNEXMIN is an international innovation action funded under the EU H2020 program, aiming to develop new technologies and services allowing the exploration of flooded underground mines. The system is comprised by the UX-1 robot prototype, launch and recovery system, command and control subsystem and a data management and post-processing computational infrastructure. The UX-1 robot is a small spherical robot equipped with a multibeam sonar, five digital cameras and rotating laser line structured light systems. It is capable of obtaining an accurate point cloud of the surrounding environment along with high resolution imagery. A set of mineralogy, water parameters and geophysical sensors was also developed in order to obtain a more comprehensive mine model. These comprise a multi-spectral camera, electro-conductivity, pH, magnetic field sensors, a subbottom sonar, total natural gamma-ray detector, UV-light for fluorescent observation and a water sampling unit. The design of the system is presented along with the robot design. Some preliminary results are also presented and discussed.

The Internet is increasingly considered an essential and integral part of people's lives. With its massification and the constant increase of the bandwidth offered to the consumers, services like Over-The-Top (OTT) have grown in recent years - e.g. YouTube and Netflix. OTT services refer to the delivery of audio, video and other data over the Internet without network operators' control. This trend leads to Quality-of-Experience (QoE) challenges in the presence of large delays or quality breaks in the visualization of a video stream. To overcome these issues, new approaches are needed to improve the content delivery reception to the users, and consequently, the services' QoE. This paper proposes a novel prefetching mechanism aiming to reduce the content delivery response, bringing the content as close as possible to consumers before being explicitly requested. This approach is based on the distribution of OTT contents in wireless networks, that can be integrated in network equipment: it is able to predict the type and quality of content that consumers connected to - or nearby - an access point may request, and cache it before being requested, thereby improving their quality perception of the service. Given the lack of information in the literature on management and control of proxy caches for embedded systems, the first step is to analyze and compare different proxy cache solutions - Nginx and Squid - and then evaluate two developed prefetching algorithms in mobile scenarios, given the characteristics of wireless networks. The results show that the proposed content-aware prefetching mechanism is capable of boosting the network's performance, from a technical perspective - higher hit-ratios, reduced data transfers and request latency - and from a user perspective, with significant improvements in the Mean Opinion Score (MOS) QoE metric.

Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.

This work presents a hybrid coordinated manoeuvre for docking an autonomous surface vehicle with an autonomous underwater vehicle. The control manoeuvre uses visual information to estimate the AUV relative position and attitude in relation to the ASV and steers the ASV in order to dock with the AUV. The AUV is assumed to be at surface with only a small fraction of its volume visible. The system implemented in the autonomous surface vehicle ROAZ, developed by LSA-ISEP to perform missions in river environment, test autonomous AUV docking capabilities and multiple AUV/ASV coordinated missions is presented. Information from a low cost embedded robotics vision system (LSAVision), along with inertial navigation sensors is fused in an extended Kalman filter and used to determine AUV relative position and orientation to the surface vehicle The real time vision processing system is described and results are presented in operational scenario.