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Details

  • Name

    Vitor Manuel Filipe
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st October 2012
002
Publications

2021

An Intelligent Predictive Maintenance Approach Based on End-of-Line Test Logfiles in the Automotive Industry

Authors
Vicêncio, D; Silva, H; Soares, S; Filipe, V; Valente, A;

Publication
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Industrial IoT Technologies and Applications

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

2020

Autonomous Driving Car Competition

Authors
Alves, JP; Fonseca Ferreira, NMF; Valente, A; Soares, S; Filipe, V;

Publication
Robotics in Education - Advances in Intelligent Systems and Computing

Abstract

2020

UAV Landing Using Computer Vision Techniques for Human Detection

Authors
Safadinho, D; Ramos, J; Ribeiro, R; Filipe, V; Barroso, J; Pereira, A;

Publication
SENSORS

Abstract
The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

2020

Vineyard trunk detection using deep learning – An experimental device benchmark

Authors
Pinto de Aguiar, ASP; Neves dos Santos, FBN; Feliz dos Santos, LCF; de Jesus Filipe, VMD; Miranda de Sousa, AJM;

Publication
Computers and Electronics in Agriculture

Abstract

Supervised
thesis

2020

Não robot applied to the development of cognitive skills

Author
Ana Maria da Cruz Freire

Institution
UP-FEUP

2020

Facial image processing to monitor physical exercise intensity

Author
Salik Ram Khanal

Institution
UTAD

2020

MODELO DE FATORES INFLUENCIADORES DA ADOÇÃO DE CRM EM MUNICÍPIOS

Author
Jorge Manuel Pereira Duque

Institution
UTAD

2020

Player market cap: aplicação móvel para negociação de ativos digitais usando tecnologia blockchain

Author
Nélson Miguel Alves Gomes

Institution
UP-FEUP

2019

Facial image processing to monitor physical exercise intensity

Author
Salik Ram Khanal

Institution
UTAD