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About

About

Brief Biographical History: 1994 concluded the BSc degree in Electrical Engineering, Institute if Engineering of Coimbra, Polytechnic Institute of Coimbra, Portugal. 1996 concluded the Licenciatura degree in Electrical and Computer Engineering, Faculty of Engineering, the University of Porto, Portugal. 1999 concluded the MSc degree in Electrical and Computer Engineering, Faculty of Engineering, the University of Porto, Portugal. 2006 concluded the Ph.D. degree in Electrical Engineering, Faculty of Engineering, the University of Trás-dos-Montes e Alto Douro, Portugal.

Interest
Topics
Details

Details

  • Name

    Nuno Miguel Ferreira
  • Role

    External Research Collaborator
  • Since

    01st January 2018
Publications

2024

Prototype for the Application of Production of Heavy Steel Structures

Authors
Bulganbayev, MA; Suliyev, R; Ferreira, NMF;

Publication
ELECTRONICS

Abstract
This study provides a comprehensive overview of the automated assembly process of large-scale metal structures using industrial robots. Our research reveals that the utilization of industrial robots significantly enhances precision, speed, and cost-effectiveness in the assembly process. The main findings suggest that integrating industrial robots in metal structure assembly holds substantial promise for optimizing manufacturing processes and elevating the quality of the final products. Additionally, the research demonstrates that robotic automation in assembly operations can lead to significant improvements in resource utilization and operational consistency. This automation also offers a viable solution to the challenges of manual labor shortages and ensures a higher standard of safety and accuracy in the manufacturing environment.

2024

Robots for Forest Maintenance

Authors
Gameiro T.; Pereira T.; Viegas C.; Di Giorgio F.; Ferreira N.F.;

Publication
Forests

Abstract
Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system’s robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers.

2024

Vision System for a Forestry Navigation Machine

Authors
Pereira, T; Gameiro, T; Pedro, J; Viegas, C; Ferreira, NMF;

Publication
SENSORS

Abstract
This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

2024

Evaluation of Different Filtering Methods Devoted to Magnetometer Data Denoising

Authors
Pereira, T; Santos, V; Gameiro, T; Viegas, C; Ferreira, N;

Publication
ELECTRONICS

Abstract
In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

2024

Incorporating an Intelligent System Based on a Quantum Algorithm into Predictive Analysis for Screening COVID-19 Patients

Authors
Saraiva, AA; da Silva, JPO; Moura Sousa, JV; Fonseca Ferreira, NM; Soares, SP; Valente, A;

Publication
Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024, Volume 1, Rome, Italy, February 21-23, 2024.

Abstract