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MAURICIO ROBERTO VERONEZ   Dr.  Institute, Department or Faculty Head 
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MAURICIO ROBERTO VERONEZ published an article in December 2018.
Top co-authors See all
M. Scaioni

74 shared publications

Dept. of Architecture, Built environment and Construction engineering (ABC), Politecnico di Milano, Via Ponzio 31, Milan, Italy

Sílvio César Cazella

43 shared publications

Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil

J. R. Ducati

27 shared publications

Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Ana Paula C. LaRocca

20 shared publications

Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, Brazil

Fabiane Bordin

19 shared publications

Advanced Visualization & Geoinformatics Laboratory, Unisinos University, São Leopoldo, Brazil

47
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Publication Record
Distribution of Articles published per year 
( - 2018)
Total number of journals
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31
 
Publications See all
Article 1 Read 0 Citations Análise Direcional de Erros Sistemáticos em Ortomosaico gerado por meio de RPAS Dalva M. Castro Vitti, Frederico Fábio Mauad, Ademir Marques... Published: 31 December 2018
Revista Brasileira de Cartografia, doi: 10.14393/rbcv70n5-44563
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A estatística circular é uma importante ferramenta para avaliar a tendência em mapeamentos a partir do imageamento com sensores embarcados. Este trabalho visou avaliar erros sistemáticos e em dois ortomosaicos obtidos do imageamento simultâneo com dois sensores embarcados no Hexator XFly 800. Foram gerados dois ortomosaicos pela técnica Structure from Motion. As discrepâncias foram obtidas pela comparação entre pontos homólogos observados nos ortomosaicos e coletados com GNSS RTK. Primeiramente, foram realizadas análises quanto a normalidade e aleatoriedade das discrepâncias observadas nos pontos de controle. De acordo com a função de Shapiro-Wilk as amostras não apresentaram distribuição normal e pelo teste de sequências foram consideradas aleatórias. Devido a não normalidade, foi realizada a análise direcional dos dados, obtendo-se assim as direções dos vetores resultantes de acordo com a função de von Mises. Para os ortomosaicos do sensor Canon ELPH 110S + SfM e sequoia + SfM, os vetores direcionais resultantes ocorreram no azimute 47o e 116o, com variâncias de 0,880 m e 0,810 m, respectivamente, mostrando grande dispersão dos pontos e por consequência, concluindo pela não tendência de ambos ortomosaicos. O RMSE foi calculado para ambos ortomosaicos e comparados com o PEC-PCD, que conduziu ao enquadramento na Classe B da escala 1:2000.
Article 0 Reads 0 Citations High-resolution spectroscopy for detecting stratigraphic surfaces and stacking patterns in sedimentary basins Marcelo Kehl De Souza, Francisco Manoel Wohnrath Tognoli, Ma... Published: 01 December 2018
Journal of South American Earth Sciences, doi: 10.1016/j.jsames.2018.08.022
DOI See at publisher website
PROCEEDINGS-ARTICLE 1 Read 0 Citations Artificial neural network–based method to classify sedimentary rocks Rodrigo Marques Figueiredo, Mauricio Roberto Veronez, Franci... Published: 01 December 2018
2018 12th International Conference on Sensing Technology (ICST), doi: 10.1109/icsenst.2018.8603576
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Article 0 Reads 0 Citations Geometry accuracy of DSM in water body margin obtained from an RGB camera with NIR band and a multispectral sensor embed... Dalva Maria De Castro Vitti, Ademir Marques Junior, Taina Th... Published: 26 November 2018
European Journal of Remote Sensing, doi: 10.1080/22797254.2018.1547989
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The photogrammetry techniques are known to be accessible due to its low cost, while the geometric accuracy is a key point to ensure that models obtained from photogrammetry are a feasible solution. This work evaluated the discrepancies in 3D (DSM) and 2D (orthomosaic) models obtained from photogrammetry using control points (GCPs) near a reflective/refractive area (water body), where the objective was to evaluate these points, analysing the independence, normality and randomness and other basic statistic. The images were obtained with a 16 MP Canon PowerShot ELPH 110S with a modified NiR band and a multispectral sensor Parrot Sequoia, both embedded in a hex-rotor UAV in flight over the Unisinos University’s artificial lake in the city of São Leopoldo, Rio Grande do Sul, Brazil. Due the distribution of the data found to be not normal, we applied non-parametric tests Chebyshev’s Theorem and the Mann–Whitney’s U test, where it showed that the values obtained from Sequoia DSM presented significant similarities with the values obtained from the GCP’s considering the confidence level of 95%; however, this was not confirmed for the model generated from a Canon camera, showing that we found better results using the multispectral Parrot Sequoia.
Article 0 Reads 0 Citations Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil: genetic structure and possible correlation with autoimm... Juliano André Boquett, Marcelo Zagonel-Oliveira, Luis Fernan... Published: 14 September 2018
International Journal of Health Geographics, doi: 10.1186/s12942-018-0154-8
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HLA genes are the most polymorphic of the human genome and have distinct allelic frequencies in populations of different geographical regions of the world, serving as genetic markers in ancestry studies. In addition, specific HLA alleles may be associated with various autoimmune and infectious diseases. The bone marrow donor registry in Brazil is the third largest in the world, and it counts with genetic typing of HLA-A, -B, and -DRB1. Since 1991 Brazil has maintained the DATASUS database, a system fed with epidemiological and health data from compulsory registration throughout the country. In this work, we perform spatial analysis and georeferencing of HLA genetic data from more than 86,000 bone marrow donors from Rio Grande do Sul (RS) and data of hospitalization for rheumatoid arthritis, multiple sclerosis and Crohn’s disease in RS, comprising the period from 1995 to 2016 obtained through the DATASUS system. The allele frequencies were georeferenced using Empirical Bayesian Kriging; the diseases prevalence were georeferenced using Inverse Distance Weighted and cluster analysis for both allele and disease were performed using Getis-Ord Gi* method. Spearman’s test was used to test the correlation between each allele and disease. The results indicate a HLA genetic structure compatible with the history of RS colonization, where it is possible to observe differentiation between regions that underwent different colonization processes. Spatial analyzes of autoimmune disease hospitalization data were performed revealing clusters for different regions of the state for each disease analyzed. The correlation test between allelic frequency and the occurrence of autoimmune diseases indicated a significant correlation between the HLA-B*08 allele and rheumatoid arthritis. Genetic mapping of populations and the spatial analyzes such as those performed in this work have great economic relevance and can be very useful in the formulation of public health campaigns and policies, contributing to the planning and adjustment of clinical actions, as well as informing and educating professionals and the population.
Article 0 Reads 0 Citations A new relationship between the quality criteria for geodetic networks Ivandro Klein, Marcelo Tomio Matsuoka, Matheus Pereira Guzat... Published: 04 August 2018
Journal of Geodesy, doi: 10.1007/s00190-018-1181-8
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Conference papers
CONFERENCE-ARTICLE 4 Reads 0 Citations Amazonian Forest Deforestation Detection Tool in Real Time Using Artificial Neural Networks and Satellite Images Silvio Cazella, Mauricio Veronez, Viviane Todt, Thiago Kehl Published: 02 November 2011
doi: 10.3390/wsf-00651
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The main purpose of this work was the development of a tool to detect in real time (daily) deforestation in the Amazon rainforest, using satellite images from the MODIS/TERRA sensor and Artificial Neural Networks. The developed tool provides the parameterization of the configuration for the neural network training to enable finding the best neural architecture to address the problem and makes use of confusion matrixes to determine the degree of success of the network. Part of the city of Porto Velho, in Rondônia state, makes up the tile H11V 09 of the MODIS/TERRA sensor, which was used as the study area. A spectrum-temporal analysis of this area was made on 57 images from 20 of May to 15 of July 2003 using the trained neural network. This analysis allowed verifying the quality of the implemented neural network classification as well as helped the understanding of the dynamics of deforestation in the Amazon rainforest. The great potential of neural networks for image classification was perceived with this work. However, the generation of consistent alarms, in other words, detecting predatory actions at the beginning; instead of firing false alarms is a complex task that is not yet solved. Therefore, the major contribution of this paper is to provide a theoretical basis and practical use of neural networks and satellite images to combat illegal deforestation.
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