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Dr. Alexandre Carlos Brandão Ramos
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(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications18 July 2017
Book Chapter
RBRafael G. Braga
Rafael G. BragaRSRoberto C. da Silva
Roberto C. da Silva
ARAlexandre Carlos Brandão Ramos
Alexandre Carlos Brandão RamosFMFelix Mora-Camino
Felix Mora-Camino
1 December 2020 in IAES International Journal of Robotics and Automation (IJRA)
Journal Article
WMWander Mendes Martins
Wander Mendes MartinsAFAntonio J. Dantas Filho
Antonio J. Dantas FilhoLJLeandro D. De Jesus
Leandro D. De JesusASAdler Diniz de Souza
Adler Diniz de SouzaARAlexandre Carlos Brandão Ramos
Alexandre Carlos Brandão RamosTPTales Cleber Pimenta
Tales Cleber PimentaPower transmission lines are of great importance for the operation of all sectors of society, such as commerce, industry and public agencies. To ensure reliability and availability of power supply, regular and occasional inspections are conducted, mostly using patrol with binoculars, helicopters or truck cranes. Research is being developed using unmanned aerial vehicle (UAV) to make this activity autonomous, faster, safer, and less costly. The present work aims to analyze research related to the autonomous control of the UAV along the transmission lines through a systematic review of the literature (SRL), apply a viable solution and to verify the possible lacuna in this state of the art. Improvements in safety, computational process and energy efficiency with low-cost were identified. The results presented can help the research community to perform the working this state of art, from the suggestions of autonomous tracking of transmission lines.
1 July 2017
Conference Paper
RBRafael G. Braga
Rafael G. Braga
Carlos Roberto Lyra da SilvaARAlexandre Carlos Brandão Ramos
Alexandre Carlos Brandão RamosFMFelix Mora-Camino
Felix Mora-CaminoDisasters involving gas leakage can cause serious damage to properties, to the environment and even endanger human lives. In these situations it is very important to locate the source of gas leakage as fast as possible. A swarm of robots is a solution specially efficient in missions involving exploration and search in unknown environments. This work proposes a strategy using behavioral rules and an idea inspired by the Particle Swarm Optimization algorithm to control a group of Unmanned Aerial Vehicles (UAVs) while searching for the source of a gas leakage. The solution was implemented in C++ using the ROS platform and was tested in simulations using the Pixhawk SITL simulator. Results show that after some time the UAVs converge to the source of leaking gas.
13 April 2018
Book Chapter
WMWander Mendes Martins
Wander Mendes MartinsRBRafael Gomes Braga
Rafael Gomes Braga
ARAlexandre Carlos Brandão Ramos
Alexandre Carlos Brandão RamosFMFelix Mora-Camino
Felix Mora-Camino
Journal Article
VCVinícius Henrique Geraldo Correa
Vinícius Henrique Geraldo CorreaPFPeter Funk
Peter FunkNSNils Sundelius
Nils SundeliusRSRickard Sohlberg
Rickard SohlbergARAlexandre Carlos Brandão Ramos
Alexandre Carlos Brandão RamosResearch on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of targets in the images and videos taken by them. Generative adversarial networks (GANs), introduced by Ian Goodfellow, among their variations, can offer excellent solutions for improving the quality of sensors, regarding super-resolution, noise removal, and other image processing issues. To identify new insights and guidance on how to apply GANs to detect living beings in SAR operations, a PRISMA-oriented systematic literature review was conducted to analyze primary studies that explore the usage of GANs for edge or object detection in images captured by drones. The results demonstrate the utilization of GAN algorithms in the realm of image enhancement for object detection, along with the metrics employed for tool validation. These findings provide insights on how to apply or modify them to aid in target identification during search stages.
1 December 2013
Conference Paper
LFLuiz F. Felizardo
Luiz F. Felizardo
RMRodrigo L. Mota
Rodrigo L. Mota
ESElcio Shiguemori
Elcio ShiguemoriMNMarcos T. Neves
Marcos T. Neves
ARAlexandre Carlos Brandão Ramos
Alexandre Carlos Brandão RamosFMFelix Mora-Camino
Felix Mora-Camino
Autonomous Unmanned Aerial Vehicles (UAVs) provide an effective alternative for surveillance in urban areas due to their cost and safety when compared to other traditional methods. The objective of this study is to report the development of a system capable of analyzing digital images of the terrain and identifying potential invasion, unauthorized changes in land and deforestation in some special urban areas. Images are captured by a camera attached to an autonomous helicopter, flying it around the area. For processing the images, an Artificial Neural Network (ANN) technique called Self Organizing Map (SOM) is used.
About Me
Alexandre Carlos Brandão Ramos holds a Ph.D. and Master's degree in Electronic and Computer Engineering from the Aeronautics Institute of Technology (ITA), earned in 1996 and 1992, respectively. He completed his undergraduate studies at Vale do Paraíba University (UNIVAP) and sandwiched a Ph.D. at the Laboratory for Analysis and Architecture of Systems (LAAS) in France. Dr. Ramos pursued postdoctoral studies at Ecole Nationale de l'Aviation Civile (ENAC) in France (2013-2014) and was a visiting professor at Mälardalen University (MDU) in Sweden (2023-2024). He is an IEEE Senior member and recipient of the CNPq-DT2 productivity scholarship since 2006. As a full professor at the Federal University of Itajubá (UNIFEI), he coordinates the Specialization Course in Remotely Piloted Aircraft Systems Engineering and leads the research group VISCAP. His research focuses on process automation (Petrobras, 1983-1995) and computer science (ITA/Motorola, 1997-2001), specializing in simulators for training with intelligent tutoring systems, hybrid intelligent systems, neural networks in remotely piloted aircraft trajectory control, and image processing. He founded and leads the Black Bee team, achieving notable placements in international drone competitions.