Experimental validation of a 3-D vision-based measurement system applied to robot calibration

One of the problems that slows the development of off-line programming is the low static and dynamic positioning accuracy of robots. Robot calibration improves the positioning accuracy and can also be used as a diagnostic tool in robot production and maintenance. A large number of robot measurement systems are now available commercially. Yet, there is a dearth of systems that are portable, accurate and low cost. In this work a measurement system that can fill this gap in local calibration is presented. The measurement system consists of a single CCD camera mounted on the robot tool flange with a wide angle lens, and uses space resection models to measure the end-effector pose relative to a world coordinate system, considering radial distortions. Scale factors and image center are obtained with innovative techniques, making use of a multiview approach. The target plate consists of a grid of white dots impressed on a black photographic paper, and mounted on the sides of a 90-degree angle plate. Results show that the achieved average accuracy varies from 0.2mm to 0.4mm, at distances from the target from 600mm to 1000mm respectively, with different camera orientations.

Tracking of physical activity from adolescence to adulthood: a population-based study

OBJECTIVE: To assess the association between regular physical activity in adolescence and leisure-time physical activity in adulthood, with emphasis on gender differences. METHODS: A population-based cross-sectional study was carried out in Pelotas, Southern Brazil, in 2003. A representative sample of households was selected in multiple stages and subjects aged 20-59 years were interviewed. Leisure-time physical activity was evaluated using the International Physical Activity Questionnaire. Data on adolescent physical activity were based on subjects' recall. RESULTS: Of 2,577 subjects interviewed, 27.5% were classified as adequately active, and 54.9% reported regular physical activity in adolescence. Subjects who engaged in regular physical activity during adolescence were more likely to be adequately active in adulthood (adjusted prevalence ratio 1.42; 95% CI: 1.23; 1.65). This effect was stronger in women (adjusted prevalence ratio: 1.51; 95% CI: 1.22; 1.86) than men (adjusted prevalence ratio: 1.35; 95% CI: 1.10; 1.67). CONCLUSIONS: Promoting physical activity in school age may be a successful intervention against the epidemic of adult inactivity. Although women were less likely to report regular physical activity in adolescence, the effect of this experience on adult behavior was stronger than in men.

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

APOE and LDLR Gene Polymorphisms and Dyslipidemia Tracking. Rio de Janeiro Study

Background:Studies show an association between changes in apolipoprotein E (ApoE) and LDLR receptor with the occurrence of dyslipidemia.Objectives:To investigate the association between polymorphisms of the APOE (ε2, ε3, ε4) and LDLR (A370T) genes with the persistence of abnormal serum lipid levels in young individuals followed up for 17 years in the Rio de Janeiro Study.Methods:The study included 56 individuals (35 males) who underwent three assessments at different ages: A1 (mean age 13.30 ± 1.53 years), A2 (22.09 ± 1.91 years) and A3 (31.23 ± 1.99 years). Clinical evaluation with measurement of blood pressure (BP) and body mass index (BMI) was conducted at all three assessments. Measurement of waist circumference (WC) and serum lipids, and analysis of genetic polymorphisms by PCR-RFLP were performed at A2 and A3. Based on dyslipidemia tracking, three groups were established: 0 (no abnormal lipid value at A2 and A3), 1 (up to one abnormal lipid value at A2 or A3) and 2 (one or more abnormal lipid values at A2 and A3).Results:Compared with groups 0 and 1, group 2 presented higher mean values of BP, BMI, WC, LDL-c and TG (p < 0.01) and lower mean values of HDL-c (p = 0.001). Across the assessments, all individuals with APOE genotypes ε2/ε4 and ε4/ε4 maintained at least one abnormal lipid variable, whereas those with genotype ε2/ε3 did not show abnormal values (χ2 = 16.848, p = 0.032). For the LDLR genotypes, there was no significant difference among the groups.Conclusions:APOE gene polymorphisms were associated with dyslipidemia in young individuals followed up longitudinally from childhood.

A Novel Algorithm to Quantify Coronary Remodeling Using Inferred Normal Dimensions

Background:Vascular remodeling, the dynamic dimensional change in face of stress, can assume different directions as well as magnitudes in atherosclerotic disease. Classical measurements rely on reference to segments at a distance, risking inappropriate comparison between dislike vessel portions.Objective:to explore a new method for quantifying vessel remodeling, based on the comparison between a given target segment and its inferred normal dimensions.Methods:Geometric parameters and plaque composition were determined in 67 patients using three-vessel intravascular ultrasound with virtual histology (IVUS-VH). Coronary vessel remodeling at cross-section (n = 27.639) and lesion (n = 618) levels was assessed using classical metrics and a novel analytic algorithm based on the fractional vessel remodeling index (FVRI), which quantifies the total change in arterial wall dimensions related to the estimated normal dimension of the vessel. A prediction model was built to estimate the normal dimension of the vessel for calculation of FVRI.Results:According to the new algorithm, “Ectatic” remodeling pattern was least common, “Complete compensatory” remodeling was present in approximately half of the instances, and “Negative” and “Incomplete compensatory” remodeling types were detected in the remaining. Compared to a traditional diagnostic scheme, FVRI-based classification seemed to better discriminate plaque composition by IVUS-VH.Conclusion:Quantitative assessment of coronary remodeling using target segment dimensions offers a promising approach to evaluate the vessel response to plaque growth/regression.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.