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Details

  • Name

    Vitor Manuel Filipe
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st October 2012
004
Publications

2022

Intelligent Monitoring and Management Platform for the Prevention of Olive Pests and Diseases, Including IoT with Sensing, Georeferencing and Image Acquisition Capabilities Through Computer Vision

Authors
Alves A.; Jorge Morais A.; Filipe V.; Alberto Pereira J.;

Publication
Lecture Notes in Networks and Systems

Abstract
Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

2022

Adaptive Recommendation in Online Environments

Authors
de Azambuja R.X.; Morais A.J.; Filipe V.;

Publication
Lecture Notes in Networks and Systems

Abstract
Recommender systems form a class of Artificial Intelligence systems that aim to recommend relevant items to the users. Due to their utility, it has gained attention in several applications domains and is high demanded for research. In order to obtain successful models in the recommendation problem in non-prohibitive computational time, different heuristics, architectures and information filtering techniques are studied with different datasets. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the sequential recommender systems development. This research focuses on models for managing sequential recommendation supported by session-based recommendation. This paper presents the characterization in the specific theme and the state-of-the-art towards study object of the thesis: the adaptive recommendation to mitigate the information overload in online environments.

2022

Data Integration in Shop Floor for Industry 4.0

Authors
Oliveira, A; Filipe, V; Amorim, EV;

Publication
Lecture Notes in Networks and Systems

Abstract
This research project consists of bringing innovation to the shop floor in such a way that it will allow its approach to the Industry 4.0 concept. The main aim includes integrating the present installed systems in order to provide its user with data as if it was a unique system. More concretely, this study intends to unify the information that comes from different systems: Manufacturing Execution System (MES); Enterprise Resource Planning (ERP); Supervisory Control and Data Acquisition (SCADA); Product Lifecycle Management (PLM); Computerized Maintenance Management Systems (CMMS); Quality Management System (QMS). Integrating this data will enable the creation of automatic procedures which can eliminate the existing gaps within the communication among the different systems. Furthermore, this will allow a real-time view of the whole plant so that immediate decisions can be made in case of any occurrence. In order to provide data fusion from the distinct systems previously mentioned, machine learning (ML) methodology will be applied. This document presents the research done and the reviewed literature, as well as the technologies and methodologies needed in this project. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

SCARA Self Posture Recognition Using a Monocular Camera

Authors
Tinoco, V; Silva, MF; Santos, FN; Morais, R; Filipe, V;

Publication
IEEE Access

Abstract

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

Supervised
thesis

2021

Recomendações adaptativas em ambientes on-line

Author
Rogério Xavier de Azambuja

Institution

2021

Sistema de visão computacional para monitorização do protocolo de higienização hospitalar

Author
João Pedro Fernandes Pereira

Institution
UTAD

2021

Preservação de herança cultural através do ensino: uso de sistema de recomendação educacional na educação superior

Author
Levi Bayde Ribeiro

Institution

2021

Multi-agent systems for student class allocation using coalition formation

Author
Joaquim Manuel Campos Costa

Institution
UTAD

2021

Plataforma digital para a prevenção, monitorização e gestão inteligente da "praga mosca da azeitona"

Author
Adília Isabel Domingues da Cruz Alves

Institution