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Sobre
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Sobre

Professor Associado com Agregação da UTAD e Investigador Sénior do INESC TEC.

Doutorou-se, na UTAD, em 2002, em Engenharia Eletrotécnica e realizou, em 2007, as provas Públicas de Agregação em Informática/Acessibilidade. Passou a Professor Associado da UTAD em dezembro de 2012.

Foi Pró-reitor para a Inovação e Gestão da Informação da UTAD, de 23 Julho de 2010 a 29 Julho de 2013.

Produziu mais de 150 trabalhos académicos, entre capítulos de livros, artigos em revistas e artigos em atas de eventos ciêntificos. Orientou 40 trabalhos de pós-graduação (mestrados e doutoramentos).

Participou em 35 projetos de investigação e desenvolvimento (foi investigador principal em 15 destes projetos).

Participou na organização de vários encontros científicos de natureza internacional, em 2006 coordenou a equipa que criou a conferência “Software Development for Enhancing Accessibility and Fighting Info-exclusion (www.dsai.ws/2016) e em 2016 a conferência Technology and Innovation is Sports, Health and Wellbeing (www.tishw.ws/2016).

As áreas principais de investigação são: Processamento Digital de Imagem, Acessibilidade e Interação pessoa Computador.

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

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

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Barroso
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 outubro 2012
008
Publicações

2021

Web AR Solution for UAV Pilot Training and Usability Testing

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

Publicação
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

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

Publicação
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

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

Publicação
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

Supervised physical exercise therapy of peripheral artery disease patients: M-health challenges and opportunities

Autores
Paredes, H; Paulino, D; Barroso, J; Abrantes, C; Machado, I; Silva, I;

Publicação
Proceedings of the 54th Hawaii International Conference on System Sciences

Abstract

2021

Using Expert Crowdsourcing to Annotate Extreme Weather Events

Autores
Paulino, D; Correia, A; Barroso, J; Liberato, M; Paredes, H;

Publicação
Advances in Intelligent Systems and Computing - Trends and Applications in Information Systems and Technologies

Abstract

Teses
supervisionadas

2020

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

Autor
João Carlos Tomé Dias

Instituição
UTAD

2020

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

Autor
Isolda Veronese Moniz Vianna Lisboa

Instituição
UTAD

2020

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

Autor
Dennis Lourenço Paulino

Instituição
UTAD

2019

Plataforma de Controlo e Serviços de Veículos não Tripulados

Autor
João Ramos

Instituição
UTAD

2019

Um modelo para a capacitação individual através da personalização intrinseca de tarefas de crowdsourcing

Autor
Dennis Lourenço Paulino

Instituição
UTAD