Luiz Gonzaga

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.

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.

Faster seam carving with minimum energy windows

Content-aware image retargeting is the problem of adapting images to different display sizes and aspect ratios while minimizing distortions to the most important regions of those images. Seam carving is an operator for content-aware image retargeting that iteratively removes 8-connected pixel paths (seams) from an image until a target resolution is reached. Finding optimal seams for seam carving is computationally expensive. We have proposed the concept of minimum energy windows as an approach to reduce the computational load of seam finding. Our results demonstrate that it is possible to find nearly-optimal seams and obtain high quality results with a significant performance improvement.

Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R2 values of greater than 0.60, consistent with literature values.

Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil: genetic structure and possible correlation with autoimmune diseases

BackgroundHLA 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.MethodsIn 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.ResultsThe 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.ConclusionsGenetic 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.

A Survey of Sensors in Healthcare Workflow Monitoring

Activities of a clinical staff in healthcare environments must regularly be adapted to new treatment methods, medications, and technologies. This constant evolution requires the monitoring of the workflow, or the sequence of actions from actors involved in a procedure, to ensure quality of medical services. In this context, recent advances in sensing technologies, including Real-time Location Systems and Computer Vision, enable high-precision tracking of actors and equipment. The current state-of-the-art about healthcare workflow monitoring typically focuses on a single technology and does not discuss its integration with others. Such an integration can lead to better solutions to evaluate medical workflows. This study aims to fill the gap regarding the analysis of monitoring technologies with a systematic literature review about sensors for capturing the workflow of healthcare environments. Its main scientific contribution is to identify both current technologies used to track activities in a clinical environment and gaps on their combination to achieve better results. It also proposes a taxonomy to classify work regarding sensing technologies and methods. The literature review does not present proposals that combine data obtained from Real-time Location Systems and Computer Vision sensors. Further analysis shows that a multimodal analysis is more flexible and could yield better results.

Identification and quantification of kaolinite in mixtures with goethite using short-wave infrared (SWIR) reflectance spectroscopy

We investigate here the potential of the spectroscopy in the identification and quantification of mixtures of kaolinite and goethite from the Continuum Removal (CR) of the spectra in the short-wave infrared. For this purpose, spectral measurements of the kaolinite, goethite, and controlled mixtures of these minerals were systematically performed. The continuum of the results were removed, the depth of the kaolinite diagnostic absorption was calculated and compared with a spectral library. It was possible to identify the kaolinite with high determination coefficient (R2>0.95) when its proportion reaches at least 60% in the mixture. For quantification purposes, it was possible to quantify kaolinite using the diagnostic absorption feature depth in the CR with a coefficient of determination of 0.99.

Digital field book for geosciences

In this study we present a mobile application for geoscience. It refers to a digital field book for automating data collection and outcrop/core description, and optimizing the final data processing. Sensors were developed for semi-automatic data collection, real time calculations, measurements of dip angles and dip directions, geographic location, among others. Field tests were performed comparing the traditional method with the proposed digital method. The preliminary results show a good acceptance of the mobile application by geoscientists and an improvement in the time required to perform data collection. Field tests are still going on and the complete results will be used to improve the development of this important tool in geoscience field work.

Least trimmed squares estimator with redundancy constraint for outlier detection in GNSS networks

A new approach for detecting small and large outliers in GNSS networks.A novel robust estimator: Least Trimmed Squares with Redundancy Constraint (LTS-RC).A new definition of the search space for the metaheuristics.LTS-RC yielded a significant improvement in comparison with the classic LTS. Global navigation satellite system (GNSS) networks facilitate accurate positioning over short and long distances on the surface of the Earth and expand the range of high-precision measurement. These networks are the basis not only for mapping activities, geoinformation, land registry and other location-based services, but also provide an important role in society as infrastructure works (roads, bridges, tunnels, water supply, sewage, electricity networks, telecommunications, etc.) which are directly dependent on highly accurate three-dimensional control points. Constituted by a predetermined number of points, the GNSS networks have their points coordinates estimated from the relative distances between them, called observations, through adjustment processes. Given the importance of this information, a precise adjustment is highly necessary. The least squares (LS) method is often applied because it is the best linear unbiased estimator, assuming that no outliers and/or systematic errors exist. Outliers may occur in practice, however, and cause such estimation to fail and leading to unprecedented errors over many points in the network. Therefore, in this study, we propose a new approach for detecting small and large outliers in observations by examining the residuals vector. For this purpose, we apply a metaheuristic method along with a novel robust estimator, called the Least Trimmed Squares with Redundancy Constraint (LTS-RC). We also propose a definition of the search space for metaheuristics in order to attain the desired results at lower computational cost. Experiments confirmed the effectiveness of the proposed approach, even in the presence of correlated observations in GNSS networks. Furthermore, the robust estimator yielded a significant improvement in comparison with the classic LTS technique. The proposed method correctly detected all outliers with no false positives in most established scenarios, even with a reduced number of cycles in the metaheuristic algorithm, and recorded better detection accuracy for moderate and large outliers than for small errors.