Sensitivity analysis in a camera-LiDAR calibration model

Recientemente, la fusión de datos entre una cámara y un sensor de profundidad del tipo LiDAR se ha convertido en un problema de gran interés en la industria y en la ingeniería. La calidad de los modelos 3D producidos depende, en buena manera, de un proceso correcto de calibración entre ambos sensores. En este artículo, se realiza un análisis de sensibilidad en un modelo de calibración cámara-LiDAR. Se ha calculado individualmente la variabilidad de cada parámetro por el método de Sobol, basado en la técnica de ANOVA, y el método FAST, que se basa en el análisis de Fourier. Se han definido los parámetros más sensibles y con mayor tendencia a introducir errores en nuestra plataforma de reconstrucción. Se han simulado múltiples conjuntos de parámetros para su análisis y comparación utilizando los métodos de Monte Carlo e Hipercubo Latino. Se muestran estadísticas sobre la sensibilidad total y global de cada parámetro. Además, se presentan resultados sobre la relación de sensibilidad en la calibración cámara-LiDAR, el costo computacional, el tiempo de simulación, la discrepancia y la homogeneidad en los datos simulados.Recently the data fusion between a camera and a depth sensor of LiDAR type, has become an issue of major concern in industry and engineering. The quality of the delivered 3D models depends greatly on a proper calibration between sensors. This paper presents a sensitivity analysis in a camera-lidar calibration model. The variability of each parameter was calculated individually by the Sobol method, based on ANOVA technique, and the FAST method, which is based on Fourier analysis. Multiple sets of parameters were simulated using Monte Carlo and Latin Hypercube methods for the purpose of comparing the results of the sensitivity analysis. We defined which parameters are the most sensitive and prone to introduce error into our reconstruction platform. Statistics for the total and global sensibility analysis for each sensor and for each parameter are presented. Furthermore, results on the sensitivity ratio on camera-LiDAR calibration, computational cost, time simulation, discrepancy and homogeneity in the simulated data are presented.Peer Reviewe

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.</p

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions. © Copyright

Measurement of the top quark mass using a profile likelihood approach with the lepton + jets final states in proton–proton collisions at s=13 TeV\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V}

International audienceThe mass of the top quark is measured in 36.3$\,\text {fb}^{-1}$ of LHC proton–proton collision data collected with the CMS detector at $\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V}$. The measurement uses a sample of top quark pair candidate events containing one isolated electron or muon and at least four jets in the final state. For each event, the mass is reconstructed from a kinematic fit of the decay products to a top quark pair hypothesis. A profile likelihood method is applied using up to four observables per event to extract the top quark mass. The top quark mass is measured to be $171.77\pm 0.37\,\text {Ge}\hspace{-.08em}\text {V}$. This approach significantly improves the precision over previous measurements