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About

About

I'm Antonio Valente and I was graduated in Electrical Engineering from University of Trás-os-Montes and Alto Douro (UTAD), Portugal in 1994, and in 1999 I've taked a MsC degree in Industrial Electronics from University of Minho, Portugal. I've obtained in 2003 a PhD degree at UTAD, working in the field of micro-systems for agriculture. Presently, I'm an Associate Professor with Habilitation in the Department of Engineering, UTAD, and director of the same department. I'm a senior researcher at Institute for Systems and Computer Engineering - Technology and Science (INESC TEC). I was chairman of ICARSC 2015 and local organizer of Robótica 2015, Vila Real, Portugal. I'm also the organizer of Portuguese Micromouse Contest (robotics competition organized annually). My professional interests are in sensors, MEMS sensors, microcontrollers, and embedded systems, with application focus to agriculture.

Interest
Topics
Details

Details

  • Name

    António Valente
  • Role

    Senior Researcher
  • Since

    01st June 2012
002
Publications

2024

Automated Detection of Refilling Stations in Industry Using Unsupervised Learning

Authors
Ribeiro, J; Pinheiro, R; Soares, S; Valente, A; Amorim, V; Filipe, V;

Publication
Lecture Notes in Mechanical Engineering

Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2024

Automatic Fall Detection with Thermal Camera

Authors
Kalbermatter, RB; Franco, T; Pereira, AI; Valente, A; Soares, SP; Lima, J;

Publication
Communications in Computer and Information Science - Optimization, Learning Algorithms and Applications

Abstract

2023

Modelling of a Vibration Robot Using Localization Ground Truth Assisted by ArUCo Markers

Authors
Matos, D; Lima, J; Rohrich, R; Oliveira, A; Valente, A; Costa, P; Costa, P;

Publication
ROBOTICS IN NATURAL SETTINGS, CLAWAR 2022

Abstract
Simulators have been increasingly used on development and tests on several areas. They allow to speed up the development without damage and no extra costs. On realistic simulators, where kinematics play an important role, the modelling process should be imported for each component to be accurately simulated. Some robots are not yet modelled, as for example the Monera. This paper presents a model of a small vibration robot (Monera) that is acquired in a developed test-bed. A localisation ground truth is used to acquire the position of the Monera with actuating it. Linear and angular speeds acquired from real experiments allow to validate the proposed methodology.

2023

Nano Aerial Vehicles for Tree Pollination

Authors
Pinheiro, I; Aguiar, A; Figueiredo, A; Pinho, T; Valente, A; Santos, F;

Publication
APPLIED SCIENCES-BASEL

Abstract
Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command.

2023

Position Estimator for a Follow Line Robot: Comparison of Least Squares and Machine Learning Approaches

Authors
Matos, D; Mendes, J; Lima, J; Pereira, AI; Valente, A; Soares, S; Costa, P; Costa, P;

Publication
ROBOTICS IN NATURAL SETTINGS, CLAWAR 2022

Abstract
Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of localisation solutions such as LiDAR, Radio-frequency and acoustic among others. The well-known line follower has been a solution used for a long time ago and still remains its application, especially in competitions for young researchers that should be captivated to the scientific and technological areas. This paper describes two methodologies to estimate the position of a robot placed on a gradient line and compares them. The Least Squares and the Machine Learning methods are used and the results applied to a real robot allow to validate the proposed approach.

Supervised
thesis

2022

Immersive VR eHealth Strategies: VR Games to Treat Schizophrenia Negative Symptoms

Author
Beatriz Almeida Miranda

Institution
UP-FEUP

2020

Efeitos de 12 meses de exercício físico supervisionado versus monitorizado com Wearables em ambulatório, na doença arterial periférica, nos seus fatores de risco associados e na aptidão física

Author
Isabel Maria Luís Machado

Institution
UTAD

2020

Advanced 2.5D Path Planning for agricultural robots

Author
Luís Carlos Feliz Santos

Institution
UTAD

2018

Desenvolvimento de um robô de baixo custo para o concurso Micromouse

Author
Samir Pinto Mehmeti

Institution
UTAD

2018

Detection of lung nodules in computed tomography images

Author
Guilherme Moreira Aresta

Institution
UP-FEUP