Allan Gonçalves de Oliveira
Universidade Federal de Mato Grosso
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Publication
Featured researches published by Allan Gonçalves de Oliveira.
Expert Systems With Applications | 2015
Thiago Meirelles Ventura; Allan Gonçalves de Oliveira; Todor Ganchev; Josiel Maimone de Figueiredo; Olaf Jahn; Marinêz Isaac Marques; Karl-L. Schuchmann
Audio parameterization method with robust frame selection.Automated acoustic recognition of 40 bird species.HMM-based bird identification. A major challenge in the automated acoustic recognition of bird species is the audio segmentation, which aims to select portions of audio that contain meaningful sound events and eliminates segments that contain predominantly background noise or sound events of other origin. Here we report on the development of an audio parameterization method with integrated robust frame selection that makes use of morphological filtering applied on the spectrogram seen as an image. The morphological filtering allows to exclude from further processing certain audio events, which otherwise could cause misclassification errors. The Mel Frequency Cepstral Coefficients (MFCCs) computed for the selected audio frames offer a good representation of the spectral information for dominant vocalizations because the morphological filtering eliminates short bursts of noise and suppresses weak competing signals. Experimental validation of the proposed method on the identification of 40 bird species from Brazil demonstrated superior accuracy and faster operation than three traditional and recent approaches. This is expressed as reduction of the relative error rate by 3.4% and the overall operational time by 7.5% when compared to the second best result. The improved frame selection robustness, precision, and operational speed facilitate applications like multi-species identification of real-field recordings.
Air, Soil and Water Research | 2013
Jonathan Willian Zangeski Novais; Thiago Rangel Rodrigues; Leone Francisco Amorim Curado; Allan Gonçalves de Oliveira; Sérgio Roberto de Paulo; José de Souza Nogueira; Renan Gonçalves de Oliveira
Research involving the thermal soil dynamics of wetland areas has not yet been explored in a way that promotes a deeper understanding of the dynamics of this region. This makes it necessary for further studies to contribute to the understanding of this biome. In the present work, we studied the thermal dynamics of the soil contrasting seasonal conditions in the Vochysia Divergens Forest. The Fourier equation was used to analyze the influence of the thermal conductivity and thermal gradient on the soil heat flux. We determined how variable water content causes the system to behave differently in the four seasons, observing seasonality in soil when completely dry and when completely flooded.
international conference on machine learning and applications | 2015
Thiago Meirelles Ventura; Allan Gonçalves de Oliveira; Claudia Aparecida Martins; Josiel Maimone de Figueiredo; Raphael de Souza Rosa Gomes
Artificial Neural Networks (ANN) have been widely used to model several types of data. The precision of ANN models is dependent upon their configuration, i.e., input parameters, training algorithm and architecture configurations. The problem lies in the amount of possible combinations of these parameters which results in countless unique ANNs. One method of finding a good combination of ANN parameters is to use a Genetic Algorithm (GA). Several studies combine a GA with an ANN to solve problems, however, it is not clear which parameters of an ANN the GA should determine. This work performed thousands of tests to verify the best combinations of parameters to use in integrations between GA and ANN especially in modeling meteorological data. Results have shown that the best approach is to use GA to define the input variables, activation function and the number of neurons of the ANN. Other tests showed that this same combination had similar results with different types of data indicating that this work can perhaps be applied to several types of problems.
Environmental Modelling and Software | 2014
Raphael de Souza Rosa Gomes; Josiel Maimone de Figueiredo; Claudia Aparecida Martins; Allan Gonçalves de Oliveira; José de Souza Nogueira
Environmental research and scientific simulations use information acquired by sensors to validate the modeling and representation of environmental behaviors. The computational processing cost of this context tends to be extremely high due to the amount of information and the models calculation complexities which demand the use of computational parallel solutions. This paper presents JSeriesCL, a framework for parallel processing of spatiotemporal series using graphics processors (GPGPU), more specifically OpenCL. GPU is cheaper than other solutions for parallel processing, such as clusters or grid, and JSeriesCL changes the way that GPU are used because it automates the configuration and management aspects of such devices. Fractal dimension and SEBS were used to validate the application of JSeriesCL over environmental data. Our framework automates the overall management and configuration aspects of GPU.All source codes which use the framework are portable over distincts GPU.Programming errors and software maintenance are minimized.The maximum GPU process power is achieved with the framework.
Revista Brasileira De Meteorologia | 2013
Leone Francisco Amorim Curado; Thiago Rangel Rodrigues; Allan Gonçalves de Oliveira; Jonathan Willian Zangeski Novais; Iramaia Jorge Cabral de Paulo; Marcelo Sacardi Biudes; José de Souza Nogueira
Research involving the flux of energy in the soil has been intensified in order to increase the understanding of the geophysical behavior of the Pantanal-Brazil. In present study was examined the seasonal variation of the thermal soil conductivity in the Pantanal for the study of energy flow in the soil to Pantanal region. The average values obtained by the Fourier equation showed that the soil thermal conductivity in the wet and dry seasons was 8.69 W.m-1.oC-1 and 6.65 W.m-1.oC-1 respectively. The seasonal variation of the thermal conductivity of the soil was 30.68% higher in the wet season than in the dry season due to soil moisture in the wet season. It was also noted that the seasonal variation of temperature in the soil layer was higher in the wet season than in the dry season due to a lower incidence of solar radiation in this season.
Applied Acoustics | 2015
Allan Gonçalves de Oliveira; Thiago Meirelles Ventura; Todor Ganchev; Josiel Maimoni de Figueiredo; Olaf Jahn; Marinêz Isaac Marques; Karl-L. Schuchmann
Semina-ciencias Agrarias | 2012
Jonathan Willian Zangeski Novais; Thiago Rangel Rodrigues; Leone Francisco Amorim Curado; Allan Gonçalves de Oliveira; S.R. Paulo; José de Souza Nogueira
Archive | 2011
Leone Francisco Amorim Curado; Thiago Rangel Rodrigues; Jonathan Willian; Allan Gonçalves de Oliveira; Thiago Meirelles Ventura; Carlo Ralph de Musis; José de Souza Nogueira
Semina-ciencias Agrarias | 2012
Jonathan Willian Zangeski Novais; Thiago Rangel Rodrigues; Leone Francisco Amorim Curado; Allan Gonçalves de Oliveira; S.R. Paulo; José de Souza Nogueira
Revista Brasileira De Meteorologia | 2013
Leone Francisco Amorim Curado; Thiago Rangel Rodrigues; Allan Gonçalves de Oliveira; Jonathan Willian Zangeski Novais; Iramaia Jorge Cabral de Paulo; Marcelo Sacardi Biudes; José de Souza Nogueira