Eduardo Silva

Abstract
This work presents a six degrees of freedom probabilistic scan matching method for registration of 3D underwater sonar scans. Unlike previous works, where local submaps are built to overcome measurement sparsity, our solution develops scan matching directly from the raw sonar data. Our method, based on the probabilistic Iterative Correspondence (pIC), takes measurement uncertainty into consideration while developing the registration procedure. A new probabilistic sensor model was developed to compute the uncertainty of each scan measurement individually. Initial displacement guesses are obtained from a probabilistic dead reckoning approach, also detailed in this document. Experiments, based on real data, demonstrate superior robustness and accuracy of our method with respect to the popular ICP algorithm. An improved trajectory is obtained by integration of scan matching updates in the localization data fusion algorithm, resulting in a substantial reduction of the original dead reckoning drift.

Abstract

Abstract
The atmospheric electric field is a key characteristic of the Earth system. Despite its relevance, oceanic measurements of the atmospheric electric field are scarce, as typically oceanic measurements tend to be focused on ocean properties rather than on the atmosphere above. This motivated the set-up of an innovative campaign on board the sail ship NRP Sagres focused on the measurement of the atmospheric electric field in the marine boundary layer. This paper describes the monitoring system that was developed to measure the atmospheric electric field during the planned circumnavigation expedition of the sail ship NRP Sagres. © 2022 IEEE.

Abstract
Blue Growth, a term first coined by the European Commission as "an initiative to harness the untapped potential of Europe's oceans, seas and coasts", identified rich marine resources as an unique asset for economic development in coastal regions and on islands. The European Commission has through the Blue Growth objectives for the first time highlighted marine sectors as unique market opportunities with high growth potential which carry socio-economic importance to the development of coastal regions. Particularly marine sectors such as aquaculture, marine robotics, and marine renewable energy which fulfil global needs in food safety and security, enable monitoring and exploration in harsh and remote conditions, and globally growing energy needs were recognized as catalysts to achieve sustainable development. Marine start-ups and small and medium-sized enterprises (SME) were identified as potential drivers in emerging marine sectors. However, they require support mechanisms tailored to their needs as they are competing for the same business and financial support as land-based SMEs, yet the research and development infrastructure is more difficult to access.ProtoAtlantic, an Interreg Atlantic Area funded project, provided marine-specific support mechanisms to marine start-ups and SMEs in emerging sectors, including business support through the accelerator and mentorship programs, enabling companies to fast track their product development through access to prototyping and testing facilities in all partner regions. The Interreg Atlantic Area encompasses partner regions in France, Ireland, Portugal, Scotland, and Spain. The consortium partners consist of Technopôle Brest Iroise (Brest, France), University College Cork - UCC (Cork, Ireland), County Council Cork (Cork, Ireland), INESC TEC (Porto, Portugal), the European Marine Energy Centre - EMEC (Orkney, Scotland), EMERGE (Canary Islands, Spain), and the lead partner, Innovalia Association (Canary Islands, Spain). The strategic collaboration between the partners provided marine start-ups access to testing facilities in the Atlantic Ocean. The extreme living laboratories provided by EMEC, the LiR National Ocean Testing Facilities at UCC's Centre of Marine and Renewable Energy (MaREI centre), and INESC TEC promise harsh real-life conditions which test the suitability of marine technologies to the limit thereby providing start-ups and SMEs with an extra layer of confidence in developing their technologies. This cross-regional collaboration puts the ProtoAltantic program in a unique position, as it is the first of its kind to dedicate marine-specific support to marine start-ups and SMEs which have benefited from the opportunities that ProtoAtlantic has provided.ProtoAtlantic developed a holistic model for the prototyping and exploitation of innovative ideas in emerging maritime sectors. After the identification of ideas from the research community, start-ups, and SMEs with product innovation capacity in the maritime sector, an acceleration program with a normed and structured process was implemented, thus creating a unique ecosystem in the Atlantic that is addressing a co-creation paradigm with the local European start-ups communities and all the stakeholders. © 2022 IEEE.

Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.