Carlos Dias Maciel
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Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC) (Institutional affiliation for the last research proposal) Birthplace: Brazil
Graduado em Eletrônica pelo IME Brasil (1989) e Doutor em Engenharia Biomédica pela COPPE / UFRJ Brasil (2000). Atualmente sou Professor Associado da USP no Brasil e tenho experiência na área de Processamento de Sinais, Instrumentação e Análise de Sistemas em Grande Escala. Atuo principalmente nos seguintes temas: processamento de sinais, modelos probabilísticos (Dynamic Bayes Networks), algoritmos computacionalmente intensos, resiliência e planejamento de sistemas. Eu ensino Sinal e Sistemas e Processamento Digital de Sinais para estudantes de graduação e Reconhecimento de Padrões e Processamento Estatístico de Sinais para o programa de pós-graduação. (Source: Lattes Curriculum)
- INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment, AP.TEM
Abstract
This proposal aims the creation of a National Institute of Science and Technology to deal with cooperative autonomous systems (INCT- SAC). Motivated by Brazil’s large territory and population size, wide-ranging geographic features, and economic and social issues, we intend to solve problems related to security and environment. This INCT will address some of these challenges by proposing...
- Analysis of the resilience on electrical systems using complex networks, BP.IC
Abstract
Complex systems resilience became an extremely important subject lately, and it is already applied on different knowledge fields. In this project, it is intended to apply those concepts in the electric power systems, using complex networks, along with graph theory, to represent an electrical grid, and to analyses its resilience when failure is detected. Those failures can change in numb...
- Structural learning of dynamic Bayesian Networks using multiobjective parallel evolutionary algorithm, BP.DD
Abstract
Now a days there is a need for a statistical theory for signal analysis. To meet this need, in recent years Bayesian inference has been used, in which data is used to infer the structure and parameters of the Bayesian Network (which is a graphical description of the conditional probability among random variables). The Signal Processing Laboratory has a long history of work involving Bay...
- Resilience analysis on self-healing systems, BP.IC
Abstract
The study of system resilience is of great importance today. Within this study, it is highlighted the use of complex network models to analyze the resilience of real systems. In this project, we intend to use this representation to make the resilience analysis of self-healing systems. These systems have the ability to change their topology to recover themselves from a failure. In this w...
- Stress data analysis with EEG using machine learning, BP.IC
Abstract
The electroencephalogram is a device that allows obtaining brain signals of the specific skull region, one of these informations that can be taken from it is the stress level of anyone. in that can be done by signal filtrage and clustering with machine learning algorithms, more specifically using Neural Networks with multilayer Perceptrons. Besides, this research uses the Montreal Imagi...
- Structure learning of non-stationary dynamic Bayesian networks, BP.DD
Abstract
As the use of Bayesian approaches grow in signal processing and electrophysiology researches, there is a need for study and development of optimization and machine learning techniques for the elaboration of probabilistic models known as dynamic Bayesian networks. Some application domains reveal limitations of these models, and this requires either adaptations in the original concepts su...
- Resilience of complex systems with the use of dynamic Bayesian Networks: a probabilistic approach, BE.PQ
Abstract
Complex systems are composed of different elements, but in opposition to other systems, their overall behavior appears as a non-trivial combination of their parts. As they are organized without any general rule, such phenomena are seen both in nature and in modern engineering systems, and their behavior is related to the connectivity and dynamics of the elements. Resilience is related t...
- Dynamic Bayesian Networks for multimodal brain imaging data, BE.EP.DD
Abstract
Modeling effective connectivity in the brain is always a task limited by the size of the model intended and data required for training. In recent years, however, advances have been achieved with MRI equipment able to obtain images with better signal to noise ratio, tissue contrast and spatial/spectral resolution, such as the 7 tesla MRI equipment at the University of Pittsburgh. Further...
| 1 / 1 | Ongoing research grants |
| 4 / 2 | Completed research grants |
| 2 / 2 | Ongoing scholarships in Brazil |
| 6 / 3 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 15 / 10 | All research grants and scholarships |
| Associated processes | |
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
| Publications | 3 |
| Citations | 2 |
| Cit./Article | 0.7 |
| Data from Web of Science | |
SANTOS, FERNANDO P.; MACIEL, CARLOS D.; NEWLAND, PHILIP L.. Pre-processing and transfer entropy measures in motor neurons controlling limb movements. Journal of Computational Neuroscience, v. 43, n. 2, p. 159-171, OCT 2017. Web of Science Citations: 1. (12/24272-7)
DOURADO, JONAS R.; DE OLIVEIRA JUNIOR, JORDAO NATAL; MACIEL, CARLOS D.. Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation. ALGORITHMS, v. 12, n. 9, SEP 2019. Web of Science Citations: 1. (14/50851-0, 18/19150-6)
FANUCCHI, RODRIGO Z.; BESSANI, MICHEL; CAMILLO, MARCOS H. M.; SOARES, ANDERSON DA S.; LONDON JR, JOAO B. A.; DESUO, LUIZ; MACIEL, CARLOS D.. Stochastic indexes for power distribution systems resilience analysis. IET GENERATION TRANSMISSION & DISTRIBUTION, v. 13, n. 12, p. 2507-2516, JUN 18 2019. Web of Science Citations: 0. (14/50851-0, 18/19150-6)
(References retrieved automatically from State of São Paulo Research Institutions)
SANTOS, Fernando Pasquini. Identicação de sistemas neurais com redes bayesianas dinâmicas e transferência de entropia. Tese (Doutorado) - Escola de Engenharia de São Carlos. Universidade de São Paulo (USP). São Carlos. (12/24272-7)