550 resultados para Noninvasive temperature estimation


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Currently in Australia, there are no decision support tools for traffic and transport engineers to assess the crash risk potential of proposed road projects at design level. A selection of equivalent tools already exists for traffic performance assessment, e.g. aaSIDRA or VISSIM. The Urban Crash Risk Assessment Tool (UCRAT) was developed for VicRoads by ARRB Group to promote methodical identification of future crash risks arising from proposed road infrastructure, where safety cannot be evaluated based on past crash history. The tool will assist practitioners with key design decisions to arrive at the safest and the most cost -optimal design options. This paper details the development and application of UCRAT software. This professional tool may be used to calculate an expected mean number of casualty crashes for an intersection, a road link or defined road network consisting of a number of such elements. The mean number of crashes provides a measure of risk associated with the proposed functional design and allows evaluation of alternative options. The tool is based on historical data for existing road infrastructure in metropolitan Melbourne and takes into account the influence of key design features, traffic volumes, road function and the speed environment. Crash prediction modelling and risk assessment approaches were combined to develop its unique algorithms. The tool has application in such projects as road access proposals associated with land use developments, public transport integration projects and new road corridor upgrade proposals.

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Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed.Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.

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We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.

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OBJECTIVE: The accurate quantification of human diabetic neuropathy is important to define at-risk patients, anticipate deterioration, and assess new therapies. ---------- RESEARCH DESIGN AND METHODS: A total of 101 diabetic patients and 17 age-matched control subjects underwent neurological evaluation, neurophysiology tests, quantitative sensory testing, and evaluation of corneal sensation and corneal nerve morphology using corneal confocal microscopy (CCM). ---------- RESULTS: Corneal sensation decreased significantly (P = 0.0001) with increasing neuropathic severity and correlated with the neuropathy disability score (NDS) (r = 0.441, P < 0.0001). Corneal nerve fiber density (NFD) (P < 0.0001), nerve fiber length (NFL), (P < 0.0001), and nerve branch density (NBD) (P < 0.0001) decreased significantly with increasing neuropathic severity and correlated with NDS (NFD r = −0.475, P < 0.0001; NBD r = −0.511, P < 0.0001; and NFL r = −0.581, P < 0.0001). NBD and NFL demonstrated a significant and progressive reduction with worsening heat pain thresholds (P = 0.01). Receiver operating characteristic curve analysis for the diagnosis of neuropathy (NDS >3) defined an NFD of <27.8/mm2 with a sensitivity of 0.82 (95% CI 0.68–0.92) and specificity of 0.52 (0.40–0.64) and for detecting patients at risk of foot ulceration (NDS >6) defined a NFD cutoff of <20.8/mm2 with a sensitivity of 0.71 (0.42–0.92) and specificity of 0.64 (0.54–0.74). ---------- CONCLUSIONS: CCM is a noninvasive clinical technique that may be used to detect early nerve damage and stratify diabetic patients with increasing neuropathic severity. Established diabetic neuropathy leads to pain and foot ulceration. Detecting neuropathy early may allow intervention with treatments to slow or reverse this condition (1). Recent studies suggested that small unmyelinated C-fibers are damaged early in diabetic neuropathy (2–4) but can only be detected using invasive procedures such as sural nerve biopsy (4,5) or skin-punch biopsy (6–8). Our studies have shown that corneal confocal microscopy (CCM) can identify early small nerve fiber damage and accurately quantify the severity of diabetic neuropathy (9–11). We have also shown that CCM relates to intraepidermal nerve fiber loss (12) and a reduction in corneal sensitivity (13) and detects early nerve fiber regeneration after pancreas transplantation (14). Recently we have also shown that CCM detects nerve fiber damage in patients with Fabry disease (15) and idiopathic small fiber neuropathy (16) when results of electrophysiology tests and quantitative sensory testing (QST) are normal. In this study we assessed corneal sensitivity and corneal nerve morphology using CCM in diabetic patients stratified for the severity of diabetic neuropathy using neurological evaluation, electrophysiology tests, and QST. This enabled us to compare CCM and corneal esthesiometry with established tests of diabetic neuropathy and define their sensitivity and specificity to detect diabetic patients with early neuropathy and those at risk of foot ulceration.

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Background: A number of studies have examined the relationship between high ambient temperature and mortality. Recently, concern has arisen about whether this relationship is modified by socio-demographic factors. However, data for this type of study is relatively scarce in subtropical/tropical regions where people are well accustomed to warm temperatures. Objective: To investigate whether the relationship between daily mean temperature and daily all-cause mortality is modified by age, gender and socio-economic status (SES) in Brisbane, Australia. Methods: We obtained daily mean temperature and all-cause mortality data for Brisbane, Australia during 1996–2004. A generalised additive model was fitted to assess the percentage increase in all deaths with every one degree increment above the threshold temperature. Different age, gender and SES groups were included in the model as categorical variables and their modification effects were estimated separately. Results: A total of 53,316 non-external deaths were included during the study period. There was a clear increasing trend in the harmful effect of high temperature on mortality with age. The effect estimate among women was more than 20 times that among men. We did not find an SES effect on the percent increase associated with temperature. Conclusions: The effects of high temperature on all deaths were modified by age and gender but not by SES in Brisbane, Australia.

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BACKGROUND AND PURPOSE It has been proposed that BRL37344, SR58611 and CGP12177 activate b3-adrenoceptors in human atrium to increase contractility and L-type Ca2+ current (ICa-L). b3-adrenoceptor agonists are potentially beneficial for the treatment of a variety of diseases but concomitant cardiostimulation would be potentially harmful. It has also been proposed that (-)-CGP12177 activates the low affinity binding site of the b1-adrenoceptor in human atrium. We therefore used BRL37344, SR58611 and (-)-CGP12177 with selective b-adrenoceptor subtype antagonists to clarify cardiostimulant b-adrenoceptor subtypes in human atrium. EXPERIMENTAL APPROACH Human right atrium was obtained from patients without heart failure undergoing coronary artery bypass or valve surgery. Cardiomyocytes were prepared to test BRL37344, SR58611 and CGP12177 effects on ICa-L. Contractile effects were determined on right atrial trabeculae. KEY RESULTS BRL37344 increased force which was antagonized by blockade of b1- and b2-adrenoceptors but not by blockade of b3-adrenoceptors with b3-adrenoceptor-selective L-748,337 (1 mM). The b3-adrenoceptor agonist SR58611 (1 nM–10 mM) did not affect atrial force. BRL37344 and SR58611 did not increase ICa-L at 37°C, but did at 24°C which was prevented by L-748,337. (-)-CGP12177 increased force and ICa-L at both 24°C and 37°C which was prevented by (-)-bupranolol (1–10 mM), but not L-748,337. CONCLUSIONS AND IMPLICATIONS We conclude that the inotropic responses to BRL37344 are mediated through b1- and b2-adrenoceptors. The inotropic and ICa-L responses to (-)-CGP12177 are mediated through the low affinity site b1L-adrenoceptor of the b1-adrenoceptor. b3-adrenoceptor-mediated increases in ICa-L are restricted to low temperatures. Human atrial b3-adrenoceptors do not change contractility and ICa-L at physiological temperature.

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This paper presents the development of a low-cost sensor platform for use in ground-based visual pose estimation and scene mapping tasks. We seek to develop a technical solution using low-cost vision hardware that allows us to accurately estimate robot position for SLAM tasks. We present results from the application of a vision based pose estimation technique to simultaneously determine camera poses and scene structure. The results are generated from a dataset gathered traversing a local road at the St Lucia Campus of the University of Queensland. We show the accuracy of the pose estimation over a 1.6km trajectory in relation to GPS ground truth.

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We aim to demonstrate unaided visual 3D pose estimation and map reconstruction using both monocular and stereo vision techniques. To date, our work has focused on collecting data from Unmanned Aerial Vehicles, which generates a number of significant issues specific to the application. Such issues include scene reconstruction degeneracy from planar data, poor structure initialisation for monocular schemes and difficult 3D reconstruction due to high feature covariance. Most modern Visual Odometry (VO) and related SLAM systems make use of a number of sensors to inform pose and map generation, including laser range-finders, radar, inertial units and vision [1]. By fusing sensor inputs, the advantages and deficiencies of each sensor type can be handled in an efficient manner. However, many of these sensors are costly and each adds to the complexity of such robotic systems. With continual advances in the abilities, small size, passivity and low cost of visual sensors along with the dense, information rich data that they provide our research focuses on the use of unaided vision to generate pose estimates and maps from robotic platforms. We propose that highly accurate (�5cm) dense 3D reconstructions of large scale environments can be obtained in addition to the localisation of the platform described in other work [2]. Using images taken from cameras, our algorithm simultaneously generates an initial visual odometry estimate and scene reconstruction from visible features, then passes this estimate to a bundle-adjustment routine to optimise the solution. From this optimised scene structure and the original images, we aim to create a detailed, textured reconstruction of the scene. By applying such techniques to a unique airborne scenario, we hope to expose new robotic applications of SLAM techniques. The ability to obtain highly accurate 3D measurements of an environment at a low cost is critical in a number of agricultural and urban monitoring situations. We focus on cameras as such sensors are small, cheap and light-weight and can therefore be deployed in smaller aerial vehicles. This, coupled with the ability of small aerial vehicles to fly near to the ground in a controlled fashion, will assist in increasing the effective resolution of the reconstructed maps.

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Purpose: To date, there have been no measuring techniques available that could clearly identify all phases of tear film surface kinetics in one interblink interval. ----- ----- Methods: Using a series of cases, we show that lateral shearing interferometry equipped with a set of robust parameter estimation techniques is able to characterize up to five different phases of tear film surface kinetics that include: (i) initial fast tear film build-up phase, (ii) further slower tear film build-up phase, (iii) tear film stability, (iv) tear film thinning, and (v), after a detected break-up, subsequent tear film deterioration. ----- ----- Results: Several representative examples are given for estimating tear film surface kinetics in measurements in which the subjects were asked to blink and keep their eyes open as long as they could. ----- ----- Conclusions: Lateral shearing interferometry is a noninvasive technique that provides means for temporal characterization of tear film surface kinetics and the opportunity for the analysis of the two-step tear film build-up process.

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This paper investigates the use of time-frequency techniques to assist in the estimation of power system modes which are resolvable by a Digital Fourier Transform (DFT). The limitations of linear estimation techniques in the presence of large disturbances which excite system non-linearities, particularly the swing equation non-linearity are shown. Where a nonlinearity manifests itself as time varying modal frequencies the Wigner-Ville Distribution (WVD) is used to describe the variation in modal frequencies and construct a window over which standard linear estimation techniques can be used. The error obtained even in the presence of multiple resolvable modes is better than 2%.

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In this paper, a method has been developed for estimating pitch angle, roll angle and aircraft body rates based on horizon detection and temporal tracking using a forward-looking camera, without assistance from other sensors. Using an image processing front-end, we select several lines in an image that may or may not correspond to the true horizon. The optical flow at each candidate line is calculated, which may be used to measure the body rates of the aircraft. Using an Extended Kalman Filter (EKF), the aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and the location of the horizon. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To test the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42◦ and 0.71◦ respectively when compared with a truth attitude source. The Cessna flight resulted in pitch and roll error standard deviations of 1.79◦ and 1.75◦ respectively. The benefits of selecting and tracking the horizon using a motion model and optical flow rather than naively relying on the image processing front-end is also demonstrated.

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This paper presents a method for calculating the in-bucket payload volume on a dragline for the purpose of estimating the material’s bulk density in real-time. Knowledge of the bulk density can provide instant feedback to mine planning and scheduling to improve blasting and in turn provide a more uniform bulk density across the excavation site. Furthermore costs and emissions in dragline operation, maintenance and downstream material processing can be reduced. The main challenge is to determine an accurate position and orientation of the bucket with the constraint of real-time performance. The proposed solution uses a range bearing and tilt sensor to locate and scan the bucket between the lift and dump stages of the dragline cycle. Various scanning strategies are investigated for their benefits in this real-time application. The bucket is segmented from the scene using cluster analysis while the pose of the bucket is calculated using the iterative closest point (ICP) algorithm. Payload points are segmented from the bucket by a fixed distance neighbour clustering method to preserve boundary points and exclude low density clusters introduced by overhead chains and the spreader bar. A height grid is then used to represent the payload from which the volume can be calculated by summing over the grid cells. We show volume calculated on a scaled system with an accuracy of greater than 95 per cent.

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This paper presents a method of recovering the 6 DoF pose (Cartesian position and angular rotation) of a range sensor mounted on a mobile platform. The method utilises point targets in a local scene and optimises over the error between their absolute position and their apparent position as observed by the range sensor. The analysis includes an investigation into the sensitivity and robustness of the method. Practical results were collected using a SICK LRS2100 mounted on a P&H electric mining shovel and present the errors in scan data relative to an independent 3D scan of the scene. A comparison to directly measuring the sensor pose is presented and shows the significant accuracy improvements in scene reconstruction using this pose estimation method.

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The success rate of carrier phase ambiguity resolution (AR) is the probability that the ambiguities are successfully fixed to their correct integer values. In existing works, an exact success rate formula for integer bootstrapping estimator has been used as a sharp lower bound for the integer least squares (ILS) success rate. Rigorous computation of success rate for the more general ILS solutions has been considered difficult, because of complexity of the ILS ambiguity pull-in region and computational load of the integration of the multivariate probability density function. Contributions of this work are twofold. First, the pull-in region mathematically expressed as the vertices of a polyhedron is represented by a multi-dimensional grid, at which the cumulative probability can be integrated with the multivariate normal cumulative density function (mvncdf) available in Matlab. The bivariate case is studied where the pull-region is usually defined as a hexagon and the probability is easily obtained using mvncdf at all the grid points within the convex polygon. Second, the paper compares the computed integer rounding and integer bootstrapping success rates, lower and upper bounds of the ILS success rates to the actual ILS AR success rates obtained from a 24 h GPS data set for a 21 km baseline. The results demonstrate that the upper bound probability of the ILS AR probability given in the existing literatures agrees with the actual ILS success rate well, although the success rate computed with integer bootstrapping method is a quite sharp approximation to the actual ILS success rate. The results also show that variations or uncertainty of the unit–weight variance estimates from epoch to epoch will affect the computed success rates from different methods significantly, thus deserving more attentions in order to obtain useful success probability predictions.

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The common approach to estimate bus dwell time at a BRT station platform is to apply the traditional dwell time methodology derived for suburban bus stops. Current dwell time models are sensitive towards bus type, fare collection policy along with the number of boarding and alighting passengers. However, they fall short in accounting for the effects of passenger/s walking on a relatively longer BRT station platform. Analysis presented in this paper shows that the average walking time of a passenger at BRT platform is 10 times more than that of bus stop. The requirement of walking to the bus entry door at the BRT station platform may lead to the bus experiencing a higher dwell time. This paper presents a theory for a BRT network which explains the loss of station capacity during peak period operation. It also highlights shortcomings of present available bus dwell time models suggested for the analysis of BRT operation.