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Mauricio Roberto Veronez
VIZLab (Advanced Visualization & Geoinformatics Lab), Unisinos, São Leopoldo 93022-750, Brazil

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Journal article
Published: 21 February 2021 in Remote Sensing
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The method of measuring the roughness of ceramic substrates is not consensual, with unsuccessful attempts to associate roughness with the adhesion of coatings because the ceramic blocks have different areas of contact, shapes, and dimensions of the roughness as well as the extrusion process influences the mechanical anisotropy of the block. The goal of this work is a quantification and comparison of roughness data obtained by 2D and 3D methods, evaluating the variations of results between the measurement methods and formulating a critical analysis regarding the quality of the information obtained with each method. For this propose, four sets of ceramic blocks with different firing temperature were produced, in order to provide groups of blocks with different surface topographies in which the roughness was estimated. The roughness measurements were made in 4608 regions, resulting in 1536 values using 2D method and 3072 values using 3D method. In the 2D method for ceramic blocks, the measurement orientation strongly influences the result, depending on the measurement position and orientation. The 3D method generates a higher average value and allows to identify roughness variations typical of the ceramic block. The roughness estimation of a ceramic block surface must be done using the 3D method.

ACS Style

Daiana Arnold; Valéria de Oliveira; Claudio Kazmierczak; Leandro Tonietto; Camila Menegotto; Luiz Gonzaga; Cristiano André da Costa; Maurício Veronez. A Critical Analysis of Red Ceramic Blocks Roughness Estimation by 2D and 3D Methods. Remote Sensing 2021, 13, 789 .

AMA Style

Daiana Arnold, Valéria de Oliveira, Claudio Kazmierczak, Leandro Tonietto, Camila Menegotto, Luiz Gonzaga, Cristiano André da Costa, Maurício Veronez. A Critical Analysis of Red Ceramic Blocks Roughness Estimation by 2D and 3D Methods. Remote Sensing. 2021; 13 (4):789.

Chicago/Turabian Style

Daiana Arnold; Valéria de Oliveira; Claudio Kazmierczak; Leandro Tonietto; Camila Menegotto; Luiz Gonzaga; Cristiano André da Costa; Maurício Veronez. 2021. "A Critical Analysis of Red Ceramic Blocks Roughness Estimation by 2D and 3D Methods." Remote Sensing 13, no. 4: 789.

Conference paper
Published: 01 December 2020 in Rio Oil and Gas Expo and Conference
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ACS Style

Ygor Rocha; Andre Luiz Durante Spigolon; Luiz Gonzaga Da Silveira Jr; Maurício Veronez; Pedro Rossa; Alysson Soares Aires; Vinicius Sales; Leonardo Bachi; Delano Ibanez. Geology 4.0: digital transformation applied at outcrop mapping activities. Rio Oil and Gas Expo and Conference 2020, 20, 462 -463.

AMA Style

Ygor Rocha, Andre Luiz Durante Spigolon, Luiz Gonzaga Da Silveira Jr, Maurício Veronez, Pedro Rossa, Alysson Soares Aires, Vinicius Sales, Leonardo Bachi, Delano Ibanez. Geology 4.0: digital transformation applied at outcrop mapping activities. Rio Oil and Gas Expo and Conference. 2020; 20 (2020):462-463.

Chicago/Turabian Style

Ygor Rocha; Andre Luiz Durante Spigolon; Luiz Gonzaga Da Silveira Jr; Maurício Veronez; Pedro Rossa; Alysson Soares Aires; Vinicius Sales; Leonardo Bachi; Delano Ibanez. 2020. "Geology 4.0: digital transformation applied at outcrop mapping activities." Rio Oil and Gas Expo and Conference 20, no. 2020: 462-463.

Research article
Published: 26 August 2020 in PLOS ONE
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Reliability analysis allows for the estimation of a system’s probability of detecting and identifying outliers. Failure to identify an outlier can jeopardize the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. System models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed where such functional relations can be slightly violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate their uncertainties to the model parameters during the data processing.

ACS Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira. On the effects of hard and soft equality constraints in the iterative outlier elimination procedure. PLOS ONE 2020, 15, e0238145 .

AMA Style

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira. On the effects of hard and soft equality constraints in the iterative outlier elimination procedure. PLOS ONE. 2020; 15 (8):e0238145.

Chicago/Turabian Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira. 2020. "On the effects of hard and soft equality constraints in the iterative outlier elimination procedure." PLOS ONE 15, no. 8: e0238145.

Review
Published: 28 June 2020 in Earth-Science Reviews
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The study of outcrop analogues of petroleum reservoirs is well established in the petroleum industry through the use of digital outcrop models (DOMs). These models, which are also known as virtual outcrop models (VOMs) or 3D outcrops, are of great importance for understanding the behavior of actual reservoirs. This topic has been reviewed by many authors, and the studies vary in detail according to the technologies involved. The present study applies systematic review methodology traversing a number of articles to find the trends in studies utilizing DOMs. The articles included in this review indicate that the technologies used to generate DOMs are still predominantly classified as Light Detection and Ranging (LiDAR) and digital photogrammetry, with the first being present in most of the works, and the latter attracting attention owing to the popularity of unmanned aerial vehicles (UAVs). These studies have attracted a significant amount of attention to outcrop analysis, and the information acquired can be used to better fit reservoir simulations. Furthermore, a trend is identified with a focus on outcrop geometry and structural data. This work also discusses some of the available opportunities related to the generation of DOMs as well as emerging technologies that can improve the quality of the outcrop models in order to provide better reservoir simulations. Finally, this work discusses the findings and highlights of the articles answering the initially raised research questions.

ACS Style

Ademir Marques; Rafael Kenji Horota; Eniuce Menezes de Souza; Lucas Kupssinskü; Pedro Rossa; Alysson Soares Aires; Leonardo Bachi; Mauricio Roberto Veronez; Luiz Gonzaga; Caroline Lessio Cazarin. Virtual and digital outcrops in the petroleum industry: A systematic review. Earth-Science Reviews 2020, 208, 103260 .

AMA Style

Ademir Marques, Rafael Kenji Horota, Eniuce Menezes de Souza, Lucas Kupssinskü, Pedro Rossa, Alysson Soares Aires, Leonardo Bachi, Mauricio Roberto Veronez, Luiz Gonzaga, Caroline Lessio Cazarin. Virtual and digital outcrops in the petroleum industry: A systematic review. Earth-Science Reviews. 2020; 208 ():103260.

Chicago/Turabian Style

Ademir Marques; Rafael Kenji Horota; Eniuce Menezes de Souza; Lucas Kupssinskü; Pedro Rossa; Alysson Soares Aires; Leonardo Bachi; Mauricio Roberto Veronez; Luiz Gonzaga; Caroline Lessio Cazarin. 2020. "Virtual and digital outcrops in the petroleum industry: A systematic review." Earth-Science Reviews 208, no. : 103260.

Journal article
Published: 23 June 2020 in Sensors
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Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset.

ACS Style

Ademir Marques Junior; Eniuce Menezes De Souza; Marianne Müller; Diego Brum; Daniel Capella Zanotta; Rafael Kenji Horota; Lucas Silveira Kupssinskü; Maurício Roberto Veronez; Jr. Luiz Gonzaga; Caroline Lessio Cazarin. Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks. Sensors 2020, 20, 3559 .

AMA Style

Ademir Marques Junior, Eniuce Menezes De Souza, Marianne Müller, Diego Brum, Daniel Capella Zanotta, Rafael Kenji Horota, Lucas Silveira Kupssinskü, Maurício Roberto Veronez, Jr. Luiz Gonzaga, Caroline Lessio Cazarin. Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks. Sensors. 2020; 20 (12):3559.

Chicago/Turabian Style

Ademir Marques Junior; Eniuce Menezes De Souza; Marianne Müller; Diego Brum; Daniel Capella Zanotta; Rafael Kenji Horota; Lucas Silveira Kupssinskü; Maurício Roberto Veronez; Jr. Luiz Gonzaga; Caroline Lessio Cazarin. 2020. "Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks." Sensors 20, no. 12: 3559.

Journal article
Published: 28 May 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper proposes a technique named Printgrammetry, a structured workflow that allows the extraction of 3D models from Google Earth platform through the combination of image captures from the screen monitor with Structure from Motion algorithms. This technique was develop to help geologists and other geoscientists in acquiring 3D photo-realistic models of outcrops and natural landscapes of big proportions without the need of field mapping and expensive equipment. The methodology is detailed aiming to permit easy reproducibility and focused on achieving the highest resolution possible by working with the best images that the platform can provide. The results have shown that it is possible to obtain visually high quality models from natural landscapes from Google Earth by acquiring images at high Level of Detail regions of the software, using a 4K monitor, multi-directional screenshots and by marking homogeneously spaced targets for georeferencing and scaling. The geometric quality assessment performed using Light Detection and Ranging ground truth data as comparison shows that the Printgrammetry dense point clouds have reached 98.1\% of the total covered area under 5 meters of distance for the Half Dome case study and 96.7\% for the Raplee Ridge case study. The generated 3D models were then visualized and interacted through an immersive virtual reality software that allowed geologists to manipulate this virtual field environment in different scales. This technique is considered by the authors to have a promising potential for research, industrial and educational projects that doesn't requires highly precision models.

ACS Style

Rafael Kenji Horota; Alysson Soares Aires; Ademir Marques; Pedro Rossa; Eniuce Menezes De Souza; Luiz Gonzaga; Mauricio Roberto Veronez. Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2819 -2830.

AMA Style

Rafael Kenji Horota, Alysson Soares Aires, Ademir Marques, Pedro Rossa, Eniuce Menezes De Souza, Luiz Gonzaga, Mauricio Roberto Veronez. Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):2819-2830.

Chicago/Turabian Style

Rafael Kenji Horota; Alysson Soares Aires; Ademir Marques; Pedro Rossa; Eniuce Menezes De Souza; Luiz Gonzaga; Mauricio Roberto Veronez. 2020. "Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 2819-2830.

Journal article
Published: 25 May 2020 in International Journal of Environmental Research and Public Health
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The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.

ACS Style

Lucas Schroeder; Mauricio Roberto Veronez; Eniuce Menezes De Souza; Diego Brum; Jr. Luiz Gonzaga; Vinicius Francisco Rofatto. Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining. International Journal of Environmental Research and Public Health 2020, 17, 3718 .

AMA Style

Lucas Schroeder, Mauricio Roberto Veronez, Eniuce Menezes De Souza, Diego Brum, Jr. Luiz Gonzaga, Vinicius Francisco Rofatto. Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining. International Journal of Environmental Research and Public Health. 2020; 17 (10):3718.

Chicago/Turabian Style

Lucas Schroeder; Mauricio Roberto Veronez; Eniuce Menezes De Souza; Diego Brum; Jr. Luiz Gonzaga; Vinicius Francisco Rofatto. 2020. "Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining." International Journal of Environmental Research and Public Health 17, no. 10: 3718.

Journal article
Published: 09 April 2020 in Sensors
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Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.

ACS Style

Lucas Silveira Kupssinskü; Tainá Thomassim Guimarães; Eniuce Menezes De Souza; Daniel C. Zanotta; Mauricio Roberto Veronez; Jr. Luiz Gonzaga; Frederico Fábio Mauad. A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors 2020, 20, 2125 .

AMA Style

Lucas Silveira Kupssinskü, Tainá Thomassim Guimarães, Eniuce Menezes De Souza, Daniel C. Zanotta, Mauricio Roberto Veronez, Jr. Luiz Gonzaga, Frederico Fábio Mauad. A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors. 2020; 20 (7):2125.

Chicago/Turabian Style

Lucas Silveira Kupssinskü; Tainá Thomassim Guimarães; Eniuce Menezes De Souza; Daniel C. Zanotta; Mauricio Roberto Veronez; Jr. Luiz Gonzaga; Frederico Fábio Mauad. 2020. "A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning." Sensors 20, no. 7: 2125.

Preprint
Published: 08 April 2020
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The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.

ACS Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Jr. Luiz Gonzaga Da Silveira. Effects of Hard and Soft Equality Constraints on Reliability Analysis. 2020, 1 .

AMA Style

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Jr. Luiz Gonzaga Da Silveira. Effects of Hard and Soft Equality Constraints on Reliability Analysis. . 2020; ():1.

Chicago/Turabian Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Jr. Luiz Gonzaga Da Silveira. 2020. "Effects of Hard and Soft Equality Constraints on Reliability Analysis." , no. : 1.

Preprint
Published: 31 March 2020
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In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

ACS Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Jr. Luiz Gonzaga Da Silveira. Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure? 2020, 1 .

AMA Style

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Jr. Luiz Gonzaga Da Silveira. Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure? . 2020; ():1.

Chicago/Turabian Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Jr. Luiz Gonzaga Da Silveira. 2020. "Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?" , no. : 1.

Journal article
Published: 16 March 2020 in Journal of Biosocial Science
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Several studies have shown that the Brazilian Northeast is a region with high rates of inbreeding as well as a high incidence of autosomal recessive diseases. The elaboration of public health policies focused on the epidemiological surveillance of congenital anomalies and rare genetic diseases in this region is urgently needed. However, the vast territory, socio-demographic heterogeneity, economic difficulties and low number of professionals with expertise in medical genetics make strategic planning a challenging task. Surnames can be compared to a genetic system with multiple neutral alleles and allow some approximation of population structure. Here, surname analysis of more than 37 million people was combined with health and socio-demographic indicators covering all 1794 municipalities of the nine states of the region. The data distribution showed a heterogeneous spatial pattern (Global Moran Index, GMI = 0.58; p < 0.001), with higher isonymy rates in the east of the region and the highest rates in the Quilombo dos Palmares region – the largest conglomerate of escaped slaves in Latin America. A positive correlation was found between the isonymy index and the frequency of live births with congenital anomalies (r = 0.268; p < 0.001), and the two indicators were spatially correlated (GMI = 0.50; p < 0.001). With this approach, quantitative information on the genetic structure of the Brazilian Northeast population was obtained, which may represent an economical and useful tool for decision-making in the medical field.

ACS Style

Augusto César Cardoso-Dos-Santos; Virginia Ramallo; Marcelo Zagonel-Oliveira; Maurício Roberto Veronez; Pablo Navarro; Isabella L. Monlleó; Victor Hugo Valiati; José Edgardo Dipierri; Lavinia Schuler-Faccini. An invincible memory: what surname analysis tells us about history, health and population medical genetics in the Brazilian Northeast. Journal of Biosocial Science 2020, 53, 183 -198.

AMA Style

Augusto César Cardoso-Dos-Santos, Virginia Ramallo, Marcelo Zagonel-Oliveira, Maurício Roberto Veronez, Pablo Navarro, Isabella L. Monlleó, Victor Hugo Valiati, José Edgardo Dipierri, Lavinia Schuler-Faccini. An invincible memory: what surname analysis tells us about history, health and population medical genetics in the Brazilian Northeast. Journal of Biosocial Science. 2020; 53 (2):183-198.

Chicago/Turabian Style

Augusto César Cardoso-Dos-Santos; Virginia Ramallo; Marcelo Zagonel-Oliveira; Maurício Roberto Veronez; Pablo Navarro; Isabella L. Monlleó; Victor Hugo Valiati; José Edgardo Dipierri; Lavinia Schuler-Faccini. 2020. "An invincible memory: what surname analysis tells us about history, health and population medical genetics in the Brazilian Northeast." Journal of Biosocial Science 53, no. 2: 183-198.

Journal article
Published: 06 March 2020 in Remote Sensing
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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

ACS Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Maurício Roberto Veronez; Jr. Luiz Gonzaga da Silveira. A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis. Remote Sensing 2020, 12, 860 .

AMA Style

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Maurício Roberto Veronez, Jr. Luiz Gonzaga da Silveira. A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis. Remote Sensing. 2020; 12 (5):860.

Chicago/Turabian Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Maurício Roberto Veronez; Jr. Luiz Gonzaga da Silveira. 2020. "A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis." Remote Sensing 12, no. 5: 860.

Preprint
Published: 25 January 2020
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An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of the modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions, but must be computed numerically by means of Monte Carlo. This paper provides the first results about Monte Carlo-based critical value inserted to different scenarios of correlation between the outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, overidentifications and statistical overlap associated with IDS in the presence of a single outlier. Based on such probability levels we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for the case where IDS is in play. MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances that the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection, but the lower the rates of outlier identification. For that case, the larger the Type I Error, the larger the ratio between MIB and MDB. We also highlight that an outlier becomes identifiable when the contribution of the measures to the wrong exclusion rate decline simultaneously. In that case, we verify that the effect of the correlation between the outlier statistics on the wrong exclusion rates becomes insignificant from a certain outlier magnitude, which increases the probability of identification.

ACS Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira Jr.. A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis. 2020, 1 .

AMA Style

Vinicius Francisco Rofatto, Marcelo Tomio Matsuoka, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira Jr.. A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis. . 2020; ():1.

Chicago/Turabian Style

Vinicius Francisco Rofatto; Marcelo Tomio Matsuoka; Ivandro Klein; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira Jr.. 2020. "A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis." , no. : 1.

Journal article
Published: 18 January 2020 in Applied Sciences
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A set of stable and identifiable points—known as control points—are interconnected by direction, distance or height differences measurements form a geodetic network. Geodetic networks are used in various branches of modern science, such as monitoring the man-made structures, analysing the crustal deformation of the Earth, establishing and maintaining a geospatial reference frame, mapping, civil engineering projects and others. One of the most crucial components for ensuring the network quality is Geodetic Network Design. The design of a geodetic network depends on its purpose. In this paper, an automatic procedure for selection of control points is proposed. The goal is to find the optimum control points location so that the maximum influence of an anomaly measurement (outlier) on the coordinates of the network is minimum. Here, the concept of Minimal Detectable Bias defines the size of the outlier and its propagation on the network coordinates is used to describe the external reliability. The proposed procedure was applied to design a levelling network. Two scenarios were investigated: design of a network with one control point (minimally constrained levelling network) and another with two control points (over-constrained levelling network). The centre of the network was the optimum position to set the control point. Results for that network reveal that the centre of the network was the optimum position to set the control point for the minimal constraint case, whereas the over-constraint case were those with less line connections. We highlight that the procedure is a generally applicable method.

ACS Style

Marcelo Tomio Matsuoka; Vinicius Francisco Rofatto; Ivandro Klein; Maurício Roberto Veronez; Jr. Luiz Gonzaga Da Silveira; João Batista Silva Neto; Ana Cristina Ramos Alves. Control Points Selection Based on Maximum External Reliability for Designing Geodetic Networks. Applied Sciences 2020, 10, 687 .

AMA Style

Marcelo Tomio Matsuoka, Vinicius Francisco Rofatto, Ivandro Klein, Maurício Roberto Veronez, Jr. Luiz Gonzaga Da Silveira, João Batista Silva Neto, Ana Cristina Ramos Alves. Control Points Selection Based on Maximum External Reliability for Designing Geodetic Networks. Applied Sciences. 2020; 10 (2):687.

Chicago/Turabian Style

Marcelo Tomio Matsuoka; Vinicius Francisco Rofatto; Ivandro Klein; Maurício Roberto Veronez; Jr. Luiz Gonzaga Da Silveira; João Batista Silva Neto; Ana Cristina Ramos Alves. 2020. "Control Points Selection Based on Maximum External Reliability for Designing Geodetic Networks." Applied Sciences 10, no. 2: 687.

Articles
Published: 01 January 2020 in European Journal of Remote Sensing
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Geometric accuracy is an important attribute of cartographic products and UAV photogrammetry has been gaining market in topographic mapping thanks to high spatial and temporal resolution, however, they need proper evaluation following accuracy standards and protocols. Regarding this, this work evaluates products from digital photogrammetry from images acquired with a fixed-wing UAV (18Mpixel camera) in a 300-380m height flight over a Hydroelectric Power Plant (HPP) in Brazil. A dataset of 23 ground control points assessed with an RTK-GNSS (using natural targets) was validated with its homologous in the Digital Surface Model (DSM) and the orthomosaic, following a workflow in which the appropriate statistics were applied. Following parametric tests like the Students t-test and the Chi-square, we compared the results with the Brazilian Cartographic Standard for digital cartography, achieving minimum scale of 1: 20,000 (RMSE of 1.04 m) for the orthomosaic, and minimum scale of 1: 10,000 (RMSE of 1.31 m) for the elevation in the DSM, although, no special targets were used. As the 3D mapping generated using the photogrammetry still needs a protocol to evaluate the accuracy, this work applied a proposed workflow respecting the quality of the data to meet the requirements of the cartographic standard.

ACS Style

Ademir Marques Junior; Dalva Maria De Castro; Taina Thomassin Guimarães; Leonardo Campos Inocencio; Maurício Roberto Veronez; Frederico Fábio Mauad; Luis Gonzaga Jr. Statistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition. European Journal of Remote Sensing 2020, 53, 27 -39.

AMA Style

Ademir Marques Junior, Dalva Maria De Castro, Taina Thomassin Guimarães, Leonardo Campos Inocencio, Maurício Roberto Veronez, Frederico Fábio Mauad, Luis Gonzaga Jr. Statistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition. European Journal of Remote Sensing. 2020; 53 (1):27-39.

Chicago/Turabian Style

Ademir Marques Junior; Dalva Maria De Castro; Taina Thomassin Guimarães; Leonardo Campos Inocencio; Maurício Roberto Veronez; Frederico Fábio Mauad; Luis Gonzaga Jr. 2020. "Statistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition." European Journal of Remote Sensing 53, no. 1: 27-39.

Preprint
Published: 18 December 2019
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Geodetic networks are essential for most geodetic, geodynamics and civil projects, such as monitoring the position and deformation of man-made structures, monitoring the crustal deformation of the Earth, establishing and maintaining a geospatial reference frame, mapping, civil engineering projects and so on. Before the installation of geodetic marks and gathering of survey data, geodetic networks need to be designed according to the pre-established quality criteria. In this study, we present a method for designing geodetic networks based on the concept of reliability. We highlight that the method discards the use of the observation vector of Gauss-Markov model. In fact, the only needs are the geometrical network configuration and the uncertainties of the observations. The aim of the proposed method is to find the optimum configuration of the geodetic control points so that the maximum influence of an outlier on the coordinates of the network is minimum. Here, the concept of Minimal Detectable Bias defines the size of the outlier and its propagation on the parameters is used to describe the external reliability. The proposed method is demonstrated by practical application of one simulated levelling network. We highlight that the method can be applied not only for geodetic network problems, but also in any branch of modern science.

ACS Style

Marcelo Tomio Matsuoka; Vinicius Francisco Rofatto; Ivandro Klein; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira; João Batista Silva Neto; Ana Cristina Ramos Alves. Control Points Selection based on Maximum External Reliability for Designing Geodetic Networks. 2019, 1 .

AMA Style

Marcelo Tomio Matsuoka, Vinicius Francisco Rofatto, Ivandro Klein, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira, João Batista Silva Neto, Ana Cristina Ramos Alves. Control Points Selection based on Maximum External Reliability for Designing Geodetic Networks. . 2019; ():1.

Chicago/Turabian Style

Marcelo Tomio Matsuoka; Vinicius Francisco Rofatto; Ivandro Klein; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira; João Batista Silva Neto; Ana Cristina Ramos Alves. 2019. "Control Points Selection based on Maximum External Reliability for Designing Geodetic Networks." , no. : 1.

Journal article
Published: 18 October 2019 in Sensors
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Geodetic networks provide accurate three-dimensional control points for mapping activities, geoinformation, and infrastructure works. Accurate computation and adjustment are necessary, as all data collection is vulnerable to outliers. Applying a Least Squares (LS) process can lead to inaccuracy over many points in such conditions. Robust Estimator (RE) methods are less sensitive to outliers and provide an alternative to conventional LS. To solve the RE functions, we propose a new metaheuristic (MH), based on the Vortex Search (IVS) algorithm, along with a novel search space definition scheme. Numerous scenarios for a Global Navigation Satellite Systems (GNSS)-based network are generated to compare and analyze the behavior of several known REs. A classic iterative RE and an LS process are also tested for comparison. We analyze the median and trim position of several estimators, in order to verify their impact on the estimates. The tests show that IVS performs better than the original algorithm; therefore, we adopted it in all subsequent RE computations. Regarding network adjustments, outcomes in the parameter estimation show that REs achieve better results in large-scale outliers' scenarios. For detection, both LS and REs identify most outliers in schemes with large outliers.

ACS Style

Ismael Érique Koch; Ivandro Klein; Jr. Luiz Gonzaga; Marcelo Tomio Matsuoka; Vinicius Francisco Rofatto; Maurício Roberto Veronez. Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search. Sensors 2019, 19, 4535 .

AMA Style

Ismael Érique Koch, Ivandro Klein, Jr. Luiz Gonzaga, Marcelo Tomio Matsuoka, Vinicius Francisco Rofatto, Maurício Roberto Veronez. Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search. Sensors. 2019; 19 (20):4535.

Chicago/Turabian Style

Ismael Érique Koch; Ivandro Klein; Jr. Luiz Gonzaga; Marcelo Tomio Matsuoka; Vinicius Francisco Rofatto; Maurício Roberto Veronez. 2019. "Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search." Sensors 19, no. 20: 4535.

Journal article
Published: 05 May 2019 in Sustainability
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The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.

ACS Style

Tainá T. Guimarães; Maurício R. Veronez; Emilie C. Koste; Eniuce M. Souza; Diego Brum; Jr. Luiz Gonzaga; Frederico F. Mauad. Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images. Sustainability 2019, 11, 2580 .

AMA Style

Tainá T. Guimarães, Maurício R. Veronez, Emilie C. Koste, Eniuce M. Souza, Diego Brum, Jr. Luiz Gonzaga, Frederico F. Mauad. Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images. Sustainability. 2019; 11 (9):2580.

Chicago/Turabian Style

Tainá T. Guimarães; Maurício R. Veronez; Emilie C. Koste; Eniuce M. Souza; Diego Brum; Jr. Luiz Gonzaga; Frederico F. Mauad. 2019. "Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images." Sustainability 11, no. 9: 2580.

Journal article
Published: 31 December 2018 in Revista Brasileira de Cartografia
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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.

ACS Style

Dalva M. Castro Vitti; Frederico Fábio Mauad; Ademir Marques Jr.; Leonardo Campos Inocêncio; Maurício Roberto Veronez. Análise Direcional de Erros Sistemáticos em Ortomosaico gerado por meio de RPAS. Revista Brasileira de Cartografia 2018, 70, 1566 -1594.

AMA Style

Dalva M. Castro Vitti, Frederico Fábio Mauad, Ademir Marques Jr., Leonardo Campos Inocêncio, Maurício Roberto Veronez. Análise Direcional de Erros Sistemáticos em Ortomosaico gerado por meio de RPAS. Revista Brasileira de Cartografia. 2018; 70 (5):1566-1594.

Chicago/Turabian Style

Dalva M. Castro Vitti; Frederico Fábio Mauad; Ademir Marques Jr.; Leonardo Campos Inocêncio; Maurício Roberto Veronez. 2018. "Análise Direcional de Erros Sistemáticos em Ortomosaico gerado por meio de RPAS." Revista Brasileira de Cartografia 70, no. 5: 1566-1594.

Conference paper
Published: 01 December 2018 in 2018 12th International Conference on Sensing Technology (ICST)
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In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field.

ACS Style

Rodrigo Marques Figueiredo; Mauricio Roberto Veronez; Francisco Manoel Wohnrath; Marcio Rosa Da Silva; Luiz Gonzaga; Lucas Kupssinskü; Fabiane Bordin; Diego Brum; Caroline Lessio Cazarin. Artificial neural network–based method to classify sedimentary rocks. 2018 12th International Conference on Sensing Technology (ICST) 2018, 282 -286.

AMA Style

Rodrigo Marques Figueiredo, Mauricio Roberto Veronez, Francisco Manoel Wohnrath, Marcio Rosa Da Silva, Luiz Gonzaga, Lucas Kupssinskü, Fabiane Bordin, Diego Brum, Caroline Lessio Cazarin. Artificial neural network–based method to classify sedimentary rocks. 2018 12th International Conference on Sensing Technology (ICST). 2018; ():282-286.

Chicago/Turabian Style

Rodrigo Marques Figueiredo; Mauricio Roberto Veronez; Francisco Manoel Wohnrath; Marcio Rosa Da Silva; Luiz Gonzaga; Lucas Kupssinskü; Fabiane Bordin; Diego Brum; Caroline Lessio Cazarin. 2018. "Artificial neural network–based method to classify sedimentary rocks." 2018 12th International Conference on Sensing Technology (ICST) , no. : 282-286.