The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A.

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

The development of floating photovoltaic systems (FPV) for coastal and offshore locations requires a solid understanding of a design’s hydrodynamic performance through reliable methods. This study aims to extend insights into the hydrodynamic behavior of a superficial multi-body FPV system in mild and harsh wave conditions through basin tests at scale 1:10, with specific interest in the performance of hinges that interconnect the PV panels. Particular effort is put into correctly scaling the elasticity of the flexible hinges that interconnect the PV modules. Tests of a 5 × 3 FPV matrix are performed, with and without shelter, by external floating breakwater (FBW). The results show that the PV modules move horizontally in the same phase when the wave length exceeds the length of the FPV system, but shorter waves result in relative motions between modules and, for harsh seas, in hinge buckling. Relative motions suggest that axial loads are highest for the hinges that connect the center modules in the system and for normal wave incidence, while shear loads are highest on the outward hinges and for oblique incidence. The FBW reduces hinge loads as it attenuates the high-frequency wave energy that largely drives relative motions between PV modules.

In this paper, image processing algorithms designed in Zynq SoC using the Vivado HLS tool are presented and compared with hand-coded designs. In Vivado HLS, the designer has the opportunity to employ libraries similar to OpenCV, a library that is well-known and wide used by software designers. The algorithms are compared in terms of area resources in two conditions: using the libraries and not using the libraries. The case studies are Data Binning, a Step Row Filter and a Sobel Filter. These algorithms have been selected because they are very common in the field of image processing and they have high computational complexity. The main benefit of the Vivado HLS tool is the reduction in time-to-market. On the other hand, when a software designer hand-codes the design, the use of image processing libraries similar to OpenCV helps to reduce development time even further because software designers are familiar with them. However, using these kinds of libraries significantly increases the necessary FPGA resources.

In this paper, we propose a flexible Fog Computing architecture in which the main features are that it allows us to select among two different communication links (WiFi and LoRa) on the fly and offers a low-power solution, thanks to the applied power management strategies at hardware and firmware level. The proposed Fog Computing architecture is formed by sensor nodes and an Internet of Things (IoT) gateway. In the case of LoRa, we have the choice of implementing the LoRaWAN and Application servers on the cloud or on the IoT gateway, avoiding, in this case, to send data to the Cloud. Additionally, we have presented an specific setup and methodology with the aim of measuring the sensor node’s power consumption and making sure there is a fair comparison between the different alternatives among the two selected wireless communication links by varying the duty cycle, the size of the payload, and the Spreading Factor (SF). This research work is in the scope of the STARPORTS Interconnecta Project, where we have deployed two sensor nodes in the offshore platform of PLOCAN, which communicate with the IoT gateway located in the PLOCAN premises. In this case, we have used LoRa communications due to the required large distance between the IoT gateway and the nodes in the offshore platform (in the range of kilometers). This deployment demonstrates that the proposed solution operates in a real environment and that it is a low-power and robust approach since it is sending data to the IoT gateway during more than one year and it continues working.