Health behaviour counselling in primary care : general practitioner : reported rate and confidence

Aims: The study aimed to identify variables associated with General Practitioners’ (GPs’) self-reported rate of health behaviour change counselling and confidence in counselling abilities. Methodology: This study was a repeat of a similar study carried out at the Mayo Clinic in 2007. The same tool and methodology were used with the permission of the authors. Variables measured by the questionnaire included: participants’ characteristics, physical activity, smoking status, healthy eating behaviour, self-reported rate of counselling behaviour, extent of training in counselling, perceived importance of counselling, confidence for health behaviour change counselling. A comparative analysis of the results was made.peer-reviewe

Composition and luminescence of AlInGaN layers grown by plasma-assisted molecular beam epitaxy

A study of AlInGaN epilayers, grown by plasma-assisted molecular beam epitaxy, was performed using spatially resolved x-ray microanalysis and luminescence spectroscopy in order to investigate competition between the incorporation of In, Al, and Ga as a function of the growth temperature in the 565-660 °C range and the nominal AlN mole fraction. The samples studied have AlN and InN mole fractions in the ranges of 4%-30% and 0%-16%, respectively. Composition measurements show the effect of decreasing temperature to be an increase in the incorporation of InN, accompanied by a small but discernible decrease in the ratio of GaN to AlN mole fractions. The incorporation of In is also shown to be significantly increased by decreasing the Al mole fraction. Optical emission peaks, observed by cathodoluminescence mapping and by photoluminescence, provide further information on the epilayer compositions as a function of substrate temperature, and the dependencies of peak energy and linewidth are plotted

Raman-scattering study of the InGaN alloy over the whole composition range

We present Raman-scattering measurements on InxGa1−xN over the entire composition range of the alloy. The frequencies of the A1(LO) and E2 modes are reported and show a good agreement with the one-mode behavior dispersion predicted by the modified random-element isodisplacement model. The A1(LO) mode displays a high intensity relative to the E2 mode due to resonant enhancement. For above band-gap excitation, the A1(LO) peak displays frequency shifts as a function of the excitation energy due to selective excitation of regions with different In contents, and strong multiphonon scattering up to 3LO is observed in outgoing resonance conditions

Robust Incremental SLAM under Constrained Optimization Formulation

© 2016 IEEE. In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loop-closure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima and perform χ 2 difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost

IN2LAMA: INertial lidar localisation and mapping

© 2019 IEEE. In this paper, we introduce a probabilistic framework for INertial Lidar Localisation And MApping (IN2LAMA). Most of today's lidars are based on spinning mechanisms that do not capture snapshots of the environment. As a result, movement of the sensor can occur while scanning. Without a good estimation of this motion, the resulting point clouds might be distorted. In the lidar mapping literature, a constant velocity motion model is commonly assumed. This is an approximation that does not necessarily always hold. The key idea of the proposed framework is to exploit preintegrated measurements over upsampled inertial data to handle motion distortion without the need for any explicit motion-model. It tightly integrates inertial and lidar data in a batch on-manifold optimisation formulation. Using temporally precise upsampled preintegrated measurement allows frame-to-frame planar and edge features association. Moreover, features are re-computed when the estimate of the state changes, consolidating front-end and back-end interaction. We validate the effectiveness of the approach through simulated and real data

3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction

© 2018 IEEE. In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans. If these points are assumed to be expressed in a common frame, this becomes an issue when the sensor moves rapidly in the environment causing motion distortion. The fundamental idea of our proposed framework is to use preintegration over interpolated inertial measurements to characterise the motion distortion in each lidar scan. Moreover, by using a set of planes as a calibration target, the proposed method makes use of lidar point-to-plane distances to jointly calibrate and localise the system using on-manifold optimisation. The calibration does not rely on a predefined target as arbitrary planes are detected and modelled in the first lidar scan. Simulated and real data are used to show the effectiveness of the proposed method

Predicting Objective Function Change in Pose-Graph Optimization

Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are insufficient to detect outliers. The optimal value of the objective function is a better choice to detect outliers but cannot be computed unless the problem is solved. In this paper, we show how the objective function change can be predicted in an incremental pose-graph optimization scheme, without actually solving the problem. The predicted objective function change can be used to guide online decisions or detect outliers. Experiments validate the accuracy of the predicted objective function, and an application to outlier detection is also provided, showing its advantages over M-estimators