Marcelo Becker

Prof. Marcelo Becker

orcid

Research Focus & Metrics

Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 December 2018 in Journal of Field Robotics
Verified
Journal Article
Closed access
PDF file
VHVitor A. H. Higuti
Vitor A. H. Higuti
AVAndres Eduardo Baquero Velasquez
Andres Eduardo Baquero Velasquez
DMDaniel Varela Magalhães
Daniel Varela Magalhães
MBMarcelo Becker
Marcelo Becker
GCGirish Chowdhary
Girish Chowdhary

1 September 2007 in IEEE Robotics & Automation Magazine
Verified
Journal Article
Closed access
SBSamir Bouabdallah
Samir Bouabdallah
MBMarcelo Becker
Marcelo Becker
RSRoland Siegwart
Roland Siegwart
The exponential growth of the interest and investigations in UAVs is strongly pushing the emergence of autonomous MFRs. This article presented some developments in the ASL-MFR project. A new design methodology was introduced and applied to a quadrotor and a coaxial helicopter enhancing appreciably the robots characteristics by allowing 100% thrust margin and 30 min autonomy (respectively, 40% and 20 min for CoaX). An original concept of hybrid active and passive control is introduced for CoaX. A simulation software permitting rapid MFR reconfiguration and various testing conditions was shown. Finally, a simulation of an obstacle avoidance controller was presented. The numerous developments presented in this article reinforce our conviction in the emergence of autonomous MFRs.

20 November 2025 in IEEE Access
Verified
Journal Article
Open access
JPJoão Manoel Herrera Pinheiro
João Manoel Herrera Pinheiro
SOSuzana Vilas Boas de Oliveira
Suzana Vilas Boas de Oliveira
TSThiago Henrique Segreto Silva
Thiago Henrique Segreto Silva
PSPedro Antonio Rabelo Saraiva
Pedro Antonio Rabelo Saraiva
ESEnzo Ferreira de Souza
Enzo Ferreira de Souza
MBMarcelo Becker
Marcelo Becker
This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine learning algorithms and 16 datasets covering both classification and regression tasks. The impact of feature scaling was evaluated in terms of predictive performance (accuracy, MAE, MSE, R²) and computational efficiency (training time, inference time, memory usage). The results show that the ensemble methods (Random Forest, XGBoost, CatBoost, LightGBM) remain robust regardless of scaling, while models such as Logistic Regression, SVM, TabNet, and MLP are highly sensitive to the chosen scaler. By making all codes, results, and model parameters publicly available, this work provides reproducible, model-specific guidance for selecting scaling strategies in practical machine learning applications.

23 June 2015 in The International Journal of Advanced Manufacturing Technology
Verified
Journal Article
Closed access
PDF file
KVKelen Vivaldini
Kelen Vivaldini
LRLuis Rocha
Luis Rocha
NMNádia Junqueira Martarelli
Nádia Junqueira Martarelli
MBMarcelo Becker
Marcelo Becker
Antonio Paulo MoreiraAntonio Paulo Moreira
Antonio Paulo Moreira

2 June 2021 in Frontiers in Robotics and AI
Verified
Journal Article
Open access
SMSergio D. Sierra Marín
Sergio D. Sierra Marín
Daniel Gomez-VargasDaniel Gomez-Vargas
Daniel Gomez-Vargas
NCNathalia Céspedes
Nathalia Céspedes
MMMarcela Múnera
Marcela Múnera
FRFlavio Roberti
Flavio Roberti
Carlos A. CifuentesCarlos A. Cifuentes
Carlos A. Cifuentes
Several challenges to guarantee medical care have been exposed during the current COVID-19 pandemic. Although the literature has shown some robotics applications to overcome the potential hazards and risks in hospital environments, the implementation of those developments is limited, and few studies measure the perception and the acceptance of clinicians. This work presents the design and implementation of several perception questionnaires to assess healthcare provider's level of acceptance and education toward robotics for COVID-19 control in clinic scenarios. Specifically, 41 healthcare professionals satisfactorily accomplished the surveys, exhibiting a low level of knowledge about robotics applications in this scenario. Likewise, the surveys revealed that the fear of being replaced by robots remains in the medical community. In the Colombian context, 82.9% of participants indicated a positive perception concerning the development and implementation of robotics in clinic environments. Finally, in general terms, the participants exhibited a positive attitude toward using robots and recommended them to be used in the current panorama.

20 July 2022 in IEEE Robotics and Automation Letters
Verified
Journal Article
Closed access
RGRicardo V. Godoy
Ricardo V. Godoy
GLGustavo José Giardini Lahr
Gustavo José Giardini Lahr
ADAnany Dwivedi
Anany Dwivedi
TRTharik J. S. Reis
Tharik J. S. Reis
PPPaulo H. Polegato
Paulo H. Polegato
MLMinas Liarokapis
Minas Liarokapis
With an increasing use of robotic and bionic devices for the execution of everyday life, complex tasks, Electromyography (EMG) based interfaces are being explored as candidate technologies for facilitating an intuitive interaction with such devices. However, EMG-based interfaces typically require appropriate features to be extracted from the raw EMG signals using a plethora of feature extraction methods to achieve excellent performance in practical applications. To select an appropriate feature set that will lead to significant EMG-based decoding performance, a deep understanding of available methods and the human musculoskeletal system is needed. To overcome this issue, researchers have proposed the use of deep learning methods for automatically extracting complex features directly from the raw EMG data. In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands. The performance of this method is evaluated and compared with other well-known methods, employing the open-access Ninapro dataset.