Navegando por Autor "LEITE, Jandecy Cabral."

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Characterization of the Specific Absorption Rate of the Cell Phone in the Human Head, Using the LN-FDTD Method
(INSTITUTO DE TECNOLOGIA, 2015) MEDEIROS, Adelson Bezerra de; LEITE, Jandecy Cabral.; Eduardo de Magalhães Braga
This work investigates the absorption of electromagnetic energy in biological tissues of the human head from cell phones in the 900MHz band, and the development of a program to calculate the Specific Absorption Rate (SAR) in the user's head. The Local Non-Orthogonal Finite-Difference Time Domain (LN-FDTD) method is implemented, using the Uniaxial Perfectly-Matched Layer (UPML), and the results are compared with existing literature. Findings show that SAR values for 1W and 0.6W exceed safety limits set by international agencies, while 0.25W complies with established standards.
Diferentes métodos para a análise da previsão do índice de emissões de uma usina geradora de eletricidade
(INSTITUTO DE TECNOLOGIA, 2014) GOMES, Paulo Cesar Rocha; RODRIGUEZ, Jorge Laureano Moya; LEITE, Jandecy Cabral.; MAGALHÃES, Edilson Marques
The study evaluated different methods for forecasting the emission index of a thermoelectric power plant, using data from 2001 to 2013 and projecting values until 2020. The ARIMA, exponential, and logarithmic methods were applied, supported by tools such as Excel and SPSS20. Results showed that the ARIMA method provides more accurate forecasts for long and well-behaved time series, while linear and logarithmic methods are suitable for clear growth or decline trends. The study concluded that the choice of method depends on data characteristics and analysis objectives.
Metodologia de avaliação da qualidade do ruído no posto de trabalho baseada em lógica fuzzy
(INSTITUTO DE TECNOLOGIA, 2010) NOGUEIRA, Maria Amélia Costa Saraiva; COSTA JÚNIOR, Carlos Tavares da; LEITE, Jandecy Cabral.; Prof. Dr. Rui Guilherme Cavaleiro de Macedo Alves
This paper proposes a methodology based on fuzzy logic to evaluate noise quality in industrial workplaces, considering variables such as noise level and working hours. The model uses membership functions and fuzzy rules to classify the environment as "satisfactory" or "at risk." The application of the method in a metalworking industry demonstrated its effectiveness in identifying unhealthy conditions, contributing to the prevention of occupational health problems.