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About

About

Guilherme Marques Amaral Silva was born on June 17, 1986 in Rio de Janeiro, Brazil. In 1993 he has emigrated to Portugal where he lives nowadays. After conclude the basic and high school in Colégio dos Cavalhos, in 2004, he has joined ISEP in the Bachelor’s degree in Electrical Engineering, branch of Electronics and Computers. In 2006 he has started to collaborate with the Autonomous System Laboratory. In 2007 he has concluded the Bachelor’s degree and, two years later, at the same institution, he has obtained the Master degree in Electrical Engineering, branch of Autonomous Systems. In 2010 he was invited by ISEP to teach some classes in the Electric Engineering Department (as Invited Assistant), position that occupies until present. In 2013 he has joined INESCTEC. In 2014 he has started his PhD at FEUP. In the present he is research fellow at INESCTEC, working on several robotics/autonomous systems projects. He develops formation control algorithms for unmanned aerial vehicles and contributes actively in the SUNNY FP7 project.

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007
Publications

2023

Precipitation-driven gamma radiation enhancement over the Atlantic Ocean

Authors
Barbosa, S; Dias, N; Almeida, C; Silva, G; Ferreira, A; Camilo, A; Silva, E;

Publication
Journal of Geophysical Research: Atmospheres

Abstract

2022

An holistic monitoring system for measurement of the atmospheric electric field over the ocean - The SAIL campaign

Authors
Barbosa, S; Dias, N; Almeida, C; Amaral, G; Ferreira, A; Lima, L; Silva, I; Martins, A; Almeida, J; Camilo, M; Silva, E;

Publication
OCEANS 2022

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.

2019

Low Cost Underwater Acoustic Positioning System with a Simplified DoA Algorithm

Authors
Guedes, P; Viana, N; Silva, J; Amaral, G; Ferreira, H; Dias, A; Almeida, JM; Martins, A; Silva, EP;

Publication
OCEANS 2019 MTS/IEEE SEATTLE

Abstract

2017

UAV cooperative perception for target detection and tracking in maritime environment

Authors
Amaral, G; Silva, H; Lopes, F; Ribeiro, JP; Freitas, S; Almeida, C; Martins, A; Almeida, J; Silva, E;

Publication
OCEANS 2017 - Aberdeen

Abstract

2016

Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy

Authors
Martins, C; da Silva, NM; Silva, G; Rozanski, VE; Silva Cunha, JPS;

Publication
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Abstract
Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the 'Advanced Brain Imaging Lab' (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician. © 2016 IEEE.