
PhD in Electrical and Computer Engineering
Federal University of Rio Grande do Norte (UFRN)
Undergraduate · School of Science and Technology (ECT)
Computational intelligence, neuroscience, and robotics
Research covers computer vision, machine learning, and biosignal processing, focused on turning video and time series into measurements that today depend on manual observation and annotation. This involves object detection, segmentation, pose estimation, tracking, and semantic scene analysis, with applications ranging from the movement of newborns in neonatal ICUs to the monitoring of industrial processes.
Teaching covers Programming, Deep Learning, and Computer Vision. The material for these courses is organised into open books published on this site, combining theory, applied content, and exercises: Python Programming, Computer Vision, Deep Learning, and Mathematics for Machine Learning with Python.
Contact: helton.maia@ufrn.br.

Comprehensive materials for students and researchers
Comprehensive guide covering fundamental concepts and practical applications for beginners and university students.
ISBN (PT): 978-65-01-21697-3
ISBN (EN): 978-65-01-22606-4
Machine learning approaches to visual perception, including image processing, object detection, and tracking systems.
ISBN (PT): 978-65-01-22607-1
ISBN (EN): 978-65-02-12405-5
Theory and practice of neural networks, covering architectures, training techniques, and modern deep learning approaches.
ISBN: 978-65-01-22605-7
Mathematical foundations for machine learning, with every formula verified in Python.
Selected repositories on GitHub

Software for baby pose annotation
heltonmaia/neolabel
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Vision Terminal Kosmos
heltonmaia/vtkosmos
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Terminal SSH para acesso rápido ao NPAD/UFRN
heltonmaia/fishell
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Hand-gesture controlled robotic arm (UFRN/LAR)
heltonmaia/proj_roboticArm
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Interactive terminal UI for flashing ESP32 boards
heltonmaia/esspresso
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Tabular Q-learning agent that plays Tic-Tac-Toe by self-play
heltonmaia/qttt
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