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About
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About

Associate Professor with Habilitation at University of Trás-os-Montes e Alto Douro (UTAD) and Senior Researcher at INESC TEC.

He earned a doctorate in UTAD in 2002 in Electrical Engineering and held in 2008 the Habilitation in Informatics/Accessibility. I was Associate Professor in December 2012.

He was Pro-Rector for Innovation and Information Management at UTAD, from 23 July 2010 to 29 July 2013.

He produced over 150 scientific papers, including book chapters, journal articles and articles in proceedings of scientific events. He supervised 40 postgraduate students (masters and doctorates).
He was member of the research team in 35 research and development projects.

He was member of several organizing committees of the international scientific meetings. In 2006 he directed the team that created the conference "Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (www.dsai.ws/2016) and in 2016 the conference Technology and Innovation is Sports, Health and Wellbeing (www.tishw.ws/2016).
The main research interests are: Digital Image Processing, Accessibility and Human Computer Interaction.

Google Scholar: http://scholar.google.com/citations?user=HBVvNYQAAAAJ&hl=en

SCOPUS: http://www.scopus.com/authid/detail.url?authorId=20435746800

Interest
Topics
Details

Details

  • Name

    João Barroso
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st October 2012
007
Publications

2021

Web AR Solution for UAV Pilot Training and Usability Testing

Authors
Ribeiro, R; Ramos, J; Safadinho, D; Reis, A; Rabadao, C; Barroso, J; Pereira, A;

Publication
Sensors

Abstract
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.

2021

Distributed Architecture for Unmanned Vehicle Services

Authors
Ramos, J; Ribeiro, R; Safadinho, D; Barroso, J; Rabadao, C; Pereira, A;

Publication
Sensors

Abstract
The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the unmanned vehicles (UV), from the unmanned factor to the reduced size and costs, we found an opportunity to bring to users a wide variety of services supported by UV, through the Internet of Unmanned Vehicles (IoUV). Current solutions were analyzed and we discussed scalability and genericity as the principal concerns. Then, we proposed a solution that combines several services and UVs, available from anywhere at any time, from a cloud platform. The solution considers a cloud distributed architecture, composed by users, services, vehicles and a platform, interconnected through the Internet. Each vehicle provides to the platform an abstract and generic interface for the essential commands. Therefore, this modular design makes easier the creation of new services and the reuse of the different vehicles. To confirm the feasibility of the solution we implemented a prototype considering a cloud-hosted platform and the integration of custom-built small-sized cars, a custom-built quadcopter, and a commercial Vertical Take-Off and Landing (VTOL) aircraft. To validate the prototype and the vehicles’ remote control, we created several services accessible via a web browser and controlled through a computer keyboard. We tested the solution in a local network, remote networks and mobile networks (i.e., 3G and Long-Term Evolution (LTE)) and proved the benefits of decentralizing the communications into multiple point-to-point links for the remote control. Consequently, the solution can provide scalable UV-based services, with low technical effort, for anyone at anytime and anywhere.

2021

Engine labels detection for vehicle quality verification in the assembly line: A machine vision approach

Authors
Capela, S; Silva, R; Khanal, SR; Campaniço, AT; Barroso, J; Filipe, V;

Publication
Lecture Notes in Electrical Engineering

Abstract
The automotive industry has an extremely high-quality product standard, not just for the security risks each faulty component can present, but the very brand image it must uphold at all times to stay competitive. In this paper, a prototype model is proposed for smart quality inspection using machine vision. The engine labels are detected using Faster-RCNN and YOLOv3 object detection algorithms. All the experiments were carried out using a custom dataset collected at an automotive assembly plant. Eight engine labels of two brands (Citroën and Peugeot) and more than ten models were detected. The results were evaluated using the metrics Intersection of Union (IoU), mean of Average Precision (mAP), Confusion Matrix, Precision and Recall. The results were validated in three folds. The models were trained using a custom dataset containing images and annotation files collected and prepared manually. Data Augmentation techniques were applied to increase the image diversity. The result without data augmentation was 92.5%, and with it the value was up-to 100%. Faster-RCNN has more accurate results compared to YOLOv3. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

2021

Web AR Solution for UAV Pilot Training and Usability Testing

Authors
Ribeiro, R; Ramos, J; Safadinho, D; Reis, A; Rabadão, C; Barroso, J; Pereira, A;

Publication
Sensors

Abstract

2020

Correction to: A review of assistive spatial orientation and navigation technologies for the visually impaired

Authors
Fernandes, H; Costa, P; Filipe, V; Paredes, H; Barroso, J;

Publication
Universal Access in the Information Society

Abstract
The fourth author name was missed in the original publication. The correct list of authors should read as “Hugo Fernandes, Paulo Costa, Vitor Filipe, Hugo Paredes, João Barroso”. It has been corrected in this erratum. The original article has been updated. © 2017 Springer-Verlag GmbH Germany

Supervised
thesis

2020

IncWeb - Framework for a more accessible, usable and inclusive Web

Author
João Carlos Tomé Dias

Institution
UTAD

2020

Acessibilidade das plataformas de e-learning na perspectiva da experiência do aluno cego

Author
Isolda Veronese Moniz Vianna Lisboa

Institution
UTAD

2020

Um modelo para capacitação individual através da personalização intrínseca de tarefas de crowdsourcing

Author
Dennis Lourenço Paulino

Institution
UTAD

2019

Sistema de processamento e visualização de dados de atletas

Author
Nuno Miguel Ferreira Cerdeira Lopes

Institution
UTAD

2019

Plataforma de Controlo e Serviços de Veículos não Tripulados

Author
João Ramos

Institution
UTAD