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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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Publications

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.

2016

Motion Descriptor for Human Gesture Recognition in Low Resolution Images

Authors
Ferreira, A; Silva, G; Dias, A; Martins, A; Campilho, A;

Publication
ROBOT 2015: SECOND IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

Abstract
A great variety of human gesture recognition methods exist in the literature, yet there is still a lack of solutions to encompass some of the challenges imposed by real life scenarios. In this document, a gesture recognition for robotic search and rescue missions in the high seas is presented. Themethod aims to identify shipwrecked people by recognizing the hand waving gesture sign. We introduce a novelmotion descriptor, through which high recognition accuracy can be achieved even for low resolution images. The method can be simultaneously applied to rigid object characterization, hence object and gesture recognition can be performed simultaneously. The descriptor has a simple implementation and is invariant to scale and gesture speed. Tests, preformed on a maritime dataset of thermal images, proved the descriptor ability to reach a meaningful representation for very low resolution objects. Recognition rates with 96.3% of accuracy were achieved.

2016

Multiple robot operations for maritime search and rescue in euRathlon 2015 competition

Authors
Matos, A; Martins, A; Dias, A; Ferreira, B; Almeida, JM; Ferreira, H; Amaral, G; Figueiredo, A; Almeida, R; Silva, F;

Publication
OCEANS 2016 - SHANGHAI

Abstract
This paper presents results of the INESC TEC participation in the maritime environment (both at surface and underwater) integrated in the ICARUS team in the euRathlon 2015 robotics search and rescue competition. These relate to the marine robots from INESC TEC, surface (ROAZ USV) and underwater (MARES AUV) autonomous vehicles participation in multiple tasks such as situation assessment, underwater mapping, leak detection or victim localization. This participation was integrated in the ICARUS Team resulting of the EU funded project aimed to develop robotic tools for large scale disasters. The coordinated search and rescue missions were performed with an initial surface survey providing data for AUV mission planning and execution. A situation assessment bathymetry map, sidescan sonar imaging and location of structures, underwater leaks and victims were achieved, with the global ICARUS team (involving sea, air and land coordinated robots) participating in the final grand Challenge and achieving the second place.

2016

UAV trials for multi-spectral imaging target detection and recognition in maritime environment

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

Publication
OCEANS 2016 MTS/IEEE Monterey, OCE 2016

Abstract
This paper addresses the use of heterogeneous sensors for target detection and recognition in maritime environment. An Unmanned Aerial Vehicle payload was assembled using hyperspectral, infrared, electro-optical, AIS and INS information to collect synchronized sensor data with vessel ground-truth position for conducting air and sea trials. The data collected is used to develop automated robust methods for detect and recognize vessels based on their exogenous physical characteristics and their behaviour across time. Data Processing preliminary results are also presented. © 2016 IEEE.