Abstract
To gather hydrological measurements is a difficult task for Autonomous Surface Vessels. It is necessary for precise navigation considering underwater obstacles, shallow and fast water flows, and also mitigate misreadings due to disturbs caused by their propulsion system. To deal with those problems, this paper presents a new topology of an Autonomous Surface Vessel (ASV) based on a catamaran boat with an aerial propulsion system with azimuth control. This set generates an over-actuated 3 Degree of Freedom (DoF) ASV, highly maneuverable and able of operating over the above-mentioned situations. To deal with the high computational cost of the over-actuated control allocation (CA) problem, this paper also proposes a Fast CA (FCA) approach. The FCA breaks the initial nonlinear system into partially-dependent linear subsystems. This approach generates smaller connected systems with overlapping solution spaces, generating fast and robust convergence, especially attractive for embedded control devices. Both proposals, i.e., ASV and FCA, are assessed through mathematical simulations and real scenarios.

Abstract
In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

Abstract
The growth of undersea exploration is pushing both the length and the complexity of propeller-driven autonomous underwater vehicles (AUVs) missions, leading to more stringent energy requirements. One approach to decrease the energy consumption of a hovering capable AUV is to use variable buoyancy systems (VBS) as a complement to the propeller actuators. These devices only require energy consumption during limited periods of time, taking into advantage the fact that whenever buoyancy is different from zero, the vehicle will continuously ascend or descend. Nevertheless, literature is scarce regarding the choice of the type of the VBS and of its constitutive elements, and regarding their effects on the energy required for buoyancy changes. This work presents structured and detailed static models of electromechanical and electrohydraulic VBSs that allow the calculation of the power required to actuate them. Based on the VBS desired characteristics and on manufacturer's data, the power consumption in each element of the VBS can be pinpointed to determine critical elements. Furthermore, a direct energy comparison with propeller-based solutions can be performed, allowing an easy evaluation of the energy gains provided by the VBS in different scenarios. This work also presents the preliminary development of an electromechanical and electrohydraulic VBS for an existing AUV at the University of Porto, Porto, Portugal. Based on the developed VBS and the developed model, numerical examples are provided for typical mission profiles. It is shown that the use of a VBS in the case of the existing AUV at the University of Porto leads to considerable energetic improvements.

Abstract
It was a recognized challenge of lack of ventilators needed to face COVID-19 worldwide. Although ventilators are sparse, self-inflating manual resuscitators are widely available in-hospital services, providing a rapid response to respiratory depression. Based on this, a device (PNEUMA) This work describes the use of a simulation environment to test the usability of a novel device to automate self-inflating manual resuscitators.The usability study was divided into two parts: (1) participants followed a protocol with instructions for assembling and using the system in a non-clinical context (Usability testing. Left panel – assembly of the system (part I); right panel – use of the system in an immersive clinical simulation environment (part II).A convenience sample (two MDs and six RNs) from an intensive care unit of a tertiary Portuguese hospital participated in the test. Usability testing showed that the system was easy and timely assembled, with low complexity of use (e.g. not requiring external help). The clinical scenario tested the transition between spontaneous and mechanical ventilation, and ventilatory parameters’ control, using PNEUMA. All participants reported that the controllable parameters (I:E, RR, Vol, PIP, Plat, and PEEP) were relevant and easy to change. Participants suggested the inclusion of patient parameters such as the tidal volume and lung compliance. Participants also suggested improvements, such as the inclusion of pressure alarms and a more user-friendly interface. All participants reported that they would be willing to use the device for emergency use.The reported study resulted in recommendations and ameliorations of the device, before its use in real settings, in the context of the COVID-19 pandemic. The use of simulation environments for device/systems’ testing provides a timely and standardized approach, enabling a safer clinical practice.

Abstract

Abstract
The offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.