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About

I was born in Porto in 1981 and I lived my childhood in my parents home village called S. Paio de Oleiros. After I conclude my high school in technological course of electronics at Escola Dr. Manuel Gomes de Almeida in 1999, I joined ISEP - Higher Institute of Engineering of Porto in Bachelor's degree of Electrical Engineering, branch of Electronics and Computers. In 2001 I joined the Autonomous System Laboratory that just started of research in robotics. In 2007 I concluded the Bachelor's degree and, two years later, at the same institution, I obtained the Master's degree in Electrical Engineering, branch of Autonomous Systems. In 2010 I was invited by ISEP to teach real time operating systems in the Electric Engineering Department (as Invited Assistant). In 2016 I was contracted as a researcher by INESCTEC to work in the Center for Autonomous Systems (CRAS). I'm now working on the H2020 UNEXMIN Project doing research on science and technology in robotics to be applied on flooded mines.

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Details

Details

006
Publications

2018

Supervised classification for hyperspectral imaging in UAV maritime target detection

Authors
Freitas, S; Almeida, C; Silva, H; Almeida, J; Silva, E;

Publication
18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018

Abstract
This paper addresses the use of a hyperspectral image system to detect vessels in maritime operational scenarios. The developed hyperspectral imaging classification methods are based on supervised approaches and allow to detect the presence of vessels using real hyperspectral data. We implemented two different methods for comparison purposes: SVM and SAM. The SVM method, which can be considered one of most utilized methods for image classification, was implemented using linear, RBF, sigmoid and polynomial kernels with PCA for dimensionality reduction, and compared with SAM using a two classes definition, namely vessel and water. The obtained results using real data collected from a UAV allow to conclude that the SVM approach is suitable for detecting the vessel presence in the water with a precision and recall rates favorable when compared to SAM. © 2018 IEEE.

2018

EVA a hybrid ROV/AUV for underwater mining operations support

Authors
Martins, A; Almeida, J; Almeida, C; Matias, B; Kapusniak, S; Silva, E;

Publication
2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018

Abstract
This paper presents EVA, a new concept for an hybrid ROV/AUV designed to support the underwater operation of an underwater mining machine, developed in the context of the European H2020 R&D ¡VAMOS! Project. This project is briefly presented, introducing the main components and concepts, providing the reader with clear picture of the operational scenario and allowing to understand better the functionality requirements of the support robotic vehicle developed. The design of EVA is detailed presented, addressing the mechanical design, hardware architecture, sensor system and navigation and control. The results of EVA both in water test tank, in the !VAMOS! Field trials in Lee Moor, UK, and in an harbor scenario are presented and discussed © 2018 IEEE

2017

Simulation environment for underground flooded mines robotic exploration

Authors
Pereira, R; Rodrigues, J; Martins, A; Dias, A; Almeida, J; Almeida, C; Silva, E;

Publication
2017 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2017

Abstract
This paper presents the work performed in the implementation of an underwater simulation environment for the development of an autonomous underwater vehicle for the exploration of flooded underground tunnels. In particular, the implementation of a laser based structured light system, multibeam sonar and other robot details were addressed. The simulation was used as a relevant tool in order to study and specify the robot multiple sensors characteristics and placement in order to adequately survey a realistic environment. A detailed description of the research and development work is presented along with the analysis of obtained results and the benefits this work brings to the project. © 2017 IEEE.

2017

STRONGMAR Summer School 2016 — Joining theory with a practical application in Underwater Archeology

Authors
Marques, MM; Salgado, A; Lobo, V; Carapau, RS; Rodrigues, AV; Carreras, M; Roca, J; Palomeras, N; Hurtos, N; Candela, C; Martins, A; Matos, A; Ferreira, B; Almeida, C; de Sa, FA; Almeida, JM; Silva, E;

Publication
OCEANS 2017 - Aberdeen

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