Maurício Roberto Veronez

Admixture in Latin America: Geographic Structure, Phenotypic Diversity and Self-Perception of Ancestry Based on 7,342 Individuals

The current genetic makeup of Latin America has been shaped by a history of extensive admixture between Africans, Europeans and Native Americans, a process taking place within the context of extensive geographic and social stratification. We estimated individual ancestry proportions in a sample of 7,342 subjects ascertained in five countries (Brazil, Chile, Colombia, México and Perú). These individuals were also characterized for a range of physical appearance traits and for self-perception of ancestry. The geographic distribution of admixture proportions in this sample reveals extensive population structure, illustrating the continuing impact of demographic history on the genetic diversity of Latin America. Significant ancestry effects were detected for most phenotypes studied. However, ancestry generally explains only a modest proportion of total phenotypic variation. Genetically estimated and self-perceived ancestry correlate significantly, but certain physical attributes have a strong impact on self-perception and bias self-perception of ancestry relative to genetically estimated ancestry.

Regional Mapping of the Geoid Using GNSS (GPS) Measurements and an Artificial Neural Network

The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of Sao Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model.

Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil

Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R 2 = 0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state.

Spectral Pattern Classification in Lidar Data for Rock Identification in Outcrops

The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

Combining SRP-PHAT and two Kinects for 3D Sound Source Localization

The Kinect(TM) has been developed to recognize gestures and voice commands, through a set of cameras and microphones, respectively. This paper proposes and evaluates low-cost Sound Source Localization (SSL) solution based this off-the-shelf equipment. It consists of employing a pair of Kinect devices as an alternative for microphone array, and executing the Steered Response Power using the PHAse Transform (SRP-PHAT) localization algorithm over acquired sound data. A fully functional prototype has been implemented and put to test under a realistic scenario. Experimental results indicate that although our approach is capable of achieving limited position estimation, and it can accurately point towards the sources direction. Two different high performance versions of the algorithm have been implemented to improve overall system performance under 3D Sound Source Localization setup.

Remaining phosphorus estimated by pedotransfer function

Although the determination of remaining phosphorus (Prem) is simple, accurate values could also be estimated with a pedotransfer function (PTF) aiming at the additional use of soil analysis data and/or Prem replacement by an even simpler determination. The purpose of this paper was to develop a pedotransfer function to estimate Prem values of soils of the State of Sao Paulo based on properties with easier or routine laboratory determination. A pedotransfer function was developed by artificial neural networks (ANN) from a database of Prem values, pH values measured in 1 mol L-1 NaF solution (pH NaF) and soil chemical and physical properties of samples collected during soil classification activities carried out in the State of Sao Paulo by the Agronomic Institute of Campinas (IAC). Furthermore, a pedotransfer function was developed by regressing Prem values against the same predictor variables of the ANN-based PTF. Results showed that Prem values can be calculated more accurately with the ANN-based pedotransfer function with the input variables pH NaF values along with the sum of exchangeable bases (SB) and the exchangeable aluminum (Al3+) soil content. In addition, the accuracy of the Prem estimates by ANN-based PTF were more sensitive to increases in the experimental database size. Although the database used in this study was not comprehensive enough for the establishment of a definitive pedotrasnfer function for Prem estimation, results indicated the inclusion of Prem and pH NaF measurements among the soil testing evaluations as promising ind order to provide a greater database for the development of an ANN-based pedotransfer function for accurate Prem estimates from pH NaF, SB, and Al3+ values.

An algorithm for automatic detection and orientation estimation of planar structures in LiDAR-scanned outcrops

The spatial orientation of linear and planar structures in geological fieldwork is still obtained using simple hand-held instruments such as a compass and clinometer. Despite their ease of use, the amount of data obtained in this way is normally smaller than would be considered as representative of the area available for sampling. LiDAR-based remote sensors are capable of sampling large areas and providing huge sets of digitized spatial points. However, the visual identification of planes in sets of points on geological outcrops is a difficult and time-consuming task. An automatic method for detecting and estimating the orientation of planar structures has been developed to reduce analysis and processing times, and to fit the best plane for each surface represented by a set of points and thus to increase the sampled area. The algorithm detects clusters of points that are part of the same plane based on the principal component analysis (PCA) technique. When applied to real cases, it has shown high precision in both the detection and orientation of fractures planes. HighlightsWe propose a method for plane detection and orientation in LiDAR point clouds.The method, simple and automatic, is statistical in its essence, using PCA.The whole point cloud is sequentially sub-divided until planar patches are found.It opposes other methods that search for small planer patches and expand it outwards.

On evaluation of different methods for quality control of correlated observations

Abstract This paper evaluates, compares, and discusses different methods for quality control in geodetic data analysis in the general scenario of correlated observations and multiple outliers. The investigated methods are the data snooping procedure, the statistical tests for multiple outliers, the recently proposed quasi-accurate detection of outliers method for correlated observations, the Danish method for correlated observations, the robust estimator for correlated observations based on bifactor equivalent weights, and the robust estimator for correlated observations based on a local sensitivity downweighting strategy. To evaluate these methods, outliers between 3σ and 9σ magnitude (positives and/or negatives) are randomly generated and added to some observations (σ being the respective standard deviation of the observation) in two different global navigation satellite system (GNSS) networks that contain correlated observations. For each network, 15 000 scenarios are performed, 5000 with one outlier, 5000 with two outliers, and 5000 with three outliers, using Monte–Carlo simulations. The investigated methods have advantages and limitations, and the discussions and conclusions about the experiments are accurately presented.

Potencialidades do serviço on-line de Posicionamento por Ponto Preciso (CSRS-PPP) em aplicações geodésicas

O metodo de Posicionamento por Ponto Preciso (PPP) com a utilizacao de GPS ( Global Positioning System ) vem se popularizando nos ultimos anos, principalmente, com o surgimento de servicos gratuitos e de processamento on-line , tais como, o Natural Resource Canada (NRCan), denominado Canadian Spatial Reference System – Precise Point Positioning (CSRS-PPP). Neste metodo, o posicionamento utiliza dados de somente um receptor e, fundamentalmente, requer apenas o uso de efemerides e correcoes dos relogios dos satelites precisos. Neste artigo, avaliou-se seu desempenho mediante a utilizacao de um longo periodo de dados (1.596 dias), obtidos na estacao POAL da Rede Brasileira de Monitoramento ContInuo (RBMC), localizada em Porto Alegre, RS, Brasil. A analise das series temporais das coordenadas diarias estimadas pelo CSRS-PPP mostraram discrepâncias de poucos centimetros, quando comparadas com os valores oficiais adotados para a estacao POAL. Palavras-chave: GPS, Posicionamento por Ponto Preciso (PPP), servicos on-line de PPP, CSRS-PPP.

Model Comparison: a Systematic Mapping Study

Context: Model comparison plays a central role in many software engineering activities. However, a comprehensive understanding about the state-of-art is still required. Goal: This paper, therefore, aims at classifying, identifying publication fora, and performing thematic analysis of the current literature in model comparison for creating an extensive and detailed understanding about this area, thereby determining gaps by graphing and pinpointing in which research areas and for which study types a shortage of publications still exits. Method: We have conducted a systematic mapping study to scrutinize those contributions produced over time, which research topics have most investigated, and which research methods that have been applied. For this, we have followed well-established empirical guidelines to define and apply a systematic mapping study. Results: The results are: (1) majority of studies (14 out of 40) provide generic model comparison techniques, rather than comparison techniques for UML diagrams; (2) a categorization and quantification of the current studies in a variety of dimensions; and (3) an overview of current research topics and trends.