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Your search keyword '"Marcato, André L. M."' showing total 26 results
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26 results on '"Marcato, André L. M."'

2. A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Author

Pinto, Milena F., Marcato, André L. M., Melo, Aurélio G., Honório, Leonardo M., and Urdiales, Cristina

Subjects
Computer Science - Robotics, Computer Science - Networking and Internet Architecture
Abstract

Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures., Comment: Volume 2019, Article ID 7497924, 14 pages

Published
2019
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6. Coverage Path Planning Optimization for Slopes and Dams Inspection

Author

Biundini, Iago Z., Melo, Aurelio G., Pinto, Milena F., Marins, Guilherme M., Marcato, Andre L. M., Honorio, Leonardo M., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Silva, Manuel F., editor, Luís Lima, José, editor, Reis, Luís Paulo, editor, Sanfeliu, Alberto, editor, and Tardioli, Danilo, editor

Published
2020
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14. A novel sEMG data augmentation based on WGAN-GP.

Author

Coelho, Fabrício, Pinto, Milena F., Melo, Aurélio G., Ramos, Gabryel S., and Marcato, André L. M.

Subjects
DATA augmentation, CONVOLUTIONAL neural networks, GENERATIVE adversarial networks, DATABASES, SIGNAL classification
Abstract

The classification of sEMG signals is fundamental in applications that use mechanical prostheses, making it necessary to work with generalist databases that improve the accuracy of those classifications. Therefore, synthetic signal generation can be beneficial in enriching a database to make it more generalist. This work proposes using a variant of generative adversarial networks to produce synthetic biosignals of sEMG. A convolutional neural network (CNN) was used to classify the movements. The results showed good performance with an increase of 4.07% in a set of movement classification accuracy when 200 synthetic samples were included for each movement. We compared our results to other methodologies, such as Magnitude Warping and Scaling. Both methodologies did not have the same performance in the classification. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle / Arquitetura de vigilância para reconhecimento de atividade humana usando veículo aéreo não tripulado

Author

Pinto, Milena F., Melo, Aurelio Gouvêa de, Marins, Guilherme, Biundini, Iago Z., and Marcato, André L. M.

Subjects
Semi-Autonomous Mission, Surveillance, UAV, Robotic Systems, Intelligent Systems
Abstract

There is intensive growth in researches regarding surveillance and threat detection. Surveillance tasks often involve several actors with multiple interactions. Thus, modeling a complex activity becomes challenging. This work proposes an architecture comprised of low, middle, and high levels. The low-level recognizes characteristics, positioning of objects, and time of occurrences utilizing a camera and Unmanned Aerial Vehicle (UAV) sensors. The middle-level is responsible for structuring the information from the low-level using Deterministic Finite Automata (DFA). An expert system attached in the high-level module performs inference over the organized information to enables the system to have simple reasoning modules, assisting the operator decision. The architecture is embedded in a UAV to reduce the number of cameras and to reach difficult areas. The experiments showed that the proposed system updated the grammatical structure effectively, given a sequence of information computed by the vision modules.

Published
2020
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24. Hybrid Methodology for Path Planning and Computational Vision Applied to Autonomous Mission: A New Approach.

Author

Coelho, Fabrício O., Pinto, Milena F., Souza, João Pedro C., and Marcato, André L. M.

Subjects
MOBILE robots, AUTONOMOUS robots, OLDER people, ONLINE algorithms, EVALUATION methodology
Abstract

SUMMARY: In recent years, mobile robots have become increasingly frequent in daily life applications, such as cleaning, surveillance, support for the elderly and people with disabilities, as well as hazardous activities. However, a big challenge arises when the robotic system must perform a fully autonomous mission. The main problems of autonomous missions include path planning, localisation, and mapping. Thus, this research proposes a hybrid methodology for mobile robots on an autonomous mission involving an offline approach that uses the Direct-DRRT* algorithm and the artificial potential fields algorithm as the online planner. The experimental design covers three scenarios with an increasing degree of accuracy in respect of the real world. Additionally, an extensive evaluation of the proposed methodology is reported. [ABSTRACT FROM AUTHOR]

Published
2020
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