128 resultados para Airborne laser bathymetry
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YBCO thin films were fabricated by laser deposition, in situ on MgO substrates, using both O2 and N2O as process gas. Films with Tc above 90 K and jc of 106 A/cm2 at 77 K were grown in oxygen at a substrate temperature of 765 °C. Using N2O, the optimum substrate temperature was 745 °C, giving a Tc of 87 K. At lower temperatures, the films made in N2O had higher Tc (79 K) than the films made in oxygen (66 K). SEM and STM investigations of the film surfaces showed the films to consist of a comparatively smooth background surface and a distribution of larger particles. Both the particle size and the distribution density depended on the substrate temperature.
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Despite the existence of air quality guidelines in Australia and New Zealand, the concentrations of particulate matter have exceeded these guidelines on several occasions. To identify the sources of particulate matter, examine the contributions of the sources to the air quality at specific areas and estimate the most likely locations of the sources, a growing number of source apportionment studies have been conducted. This paper provides an overview of the locations of the studies, salient features of the results obtained and offers some perspectives for the improvement of future receptor modelling of air quality in these countries. The review revealed that because of its advantages over alternative models, Positive Matrix Factorisation (PMF) was the most commonly applied model in the studies. Although there were differences in the sources identified in the studies, some general trends were observed. While biomass burning was a common problem in both countries, the characteristics of this source varied from one location to another. In New Zealand, domestic heating was the highest contributor to particle levels on days when the guidelines were exceeded. On the other hand, forest back-burning was a concern in Brisbane while marine aerosol was a major source in most studies. Secondary sulphate, traffic emissions, industrial emissions and re-suspended soil were also identified as important sources. Some unique species, for example, volatile organic compounds and particle size distribution were incorporated into some of the studies with results that have significant ramifications for the improvement of air quality. Overall, the application of source apportionment models provided useful information that can assist the design of epidemiological studies and refine air pollution reduction strategies in Australia and New Zealand.
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PURPOSE To investigate the utility of using non-contact laser-scanning confocal microscopy (NC-LSCM), compared with the more conventional contact laser-scanning confocal microscopy (C-LSCM), for examining corneal substructures in vivo. METHODS An attempt was made to capture representative images from the tear film and all layers of the cornea of a healthy, 35 year old female, using both NC-LSCM and C-LSCM, on separate days. RESULTS Using NC-LSCM, good quality images were obtained of the tear film, stroma, and a section of endothelium, but the corneal depth of the images of these various substructures could not be ascertained. Using C-LSCM, good quality, full-field images were obtained of the epithelium, subbasal nerve plexus, stroma, and endothelium, and the corneal depth of each of the captured images could be ascertained. CONCLUSIONS NC-LSCM may find general use for clinical examination of the tear film, stroma and endothelium, with the caveat that the depth of stromal images cannot be determined when using this technique. This technique also facilitates image capture of oblique sections of multiple corneal layers. The inability to clearly and consistently image thin corneal substructures - such as the tear film, subbasal nerve plexus and endothelium - is a key limitation of NC-LSCM.
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This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.
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Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.
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This paper presents an approach to promote the integrity of perception systems for outdoor unmanned ground vehicles (UGV) operating in challenging environmental conditions (presence of dust or smoke). The proposed technique automatically evaluates the consistency of the data provided by two sensing modalities: a 2D laser range finder and a millimetre-wave radar, allowing for perceptual failure mitigation. Experimental results, obtained with a UGV operating in rural environments, and an error analysis validate the approach.
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Camera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods.
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In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.
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Polymer biomaterials have been widely used for bone replacement/regeneration because of their unique mechanical properties and workability. Their inherent low bioactivity makes them lack osseointegration with host bone tissue. For this reason, bioactive inorganic particles have been always incorporated into the matrix of polymers to improve their bioactivity. However, mixing inorganic particles with polymers always results in inhomogeneity of particle distribution in polymer matrix with limited bioactivity. This study sets out to apply the pulsed laser deposition (PLD) technique to prepare uniform akermanite (Ca2MgSi2O7, AKT) glass nanocoatings on the surface of two polymers (non-degradable polysulfone (PSU) and degradable polylactic acid (PDLLA)) in order to improve their surface osteogenic and angiogenic activity. The results show that a uniform nanolayer composed of amorphous AKT particles (∼30nm) of thickness 130nm forms on the surface of both PSU and PDLLA films with the PLD technique. The prepared AKT-PSU and AKT-PDLLA films significantly improved the surface roughness, hydrophilicity, hardness and apatite mineralization, compared with pure PSU and PDLLA, respectively. The prepared AKT nanocoatings distinctively enhance the alkaline phosphate (ALP) activity and bone-related gene expression (ALP, OCN, OPN and Col I) of bone-forming cells on both PSU and PDLLA films. Furthermore, AKT nanocoatings on two polymers improve the attachment, proliferation, VEGF secretion and expression of proangiogenic factors and their receptors of human umbilical vein endothelial cells (HUVEC). The results suggest that PLD-prepared bioceramic nanocoatings are very useful for enhancing the physicochemical, osteogenic and angiogenic properties of both degradable and non-degradable polymers for application in bone replacement/regeneration.
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This work investigated the impact of the HVAC filtration system and indoor particle sources on the relationship between indoor and outdoor airborne particle size and concentrations in an operating room. Filters with efficiency between 65% and 99.97% were used in the investigation and indoor and outdoor particle size and concentrations were measured. A balance mass model was used for the simulation of the impact of the surgical team, deposition rate, HVAC exhaust and air change rates on indoor particle concentration. The experimental results showed that high efficiency filters would not be expected to decrease the risk associated with indoor particles larger than approximately 1 µm in size because normal filters are relatively efficient for these large particles. A good fraction of outdoor particles were removed by deposition on the HVAC system surfaces and this deposition increased with particle size. For particles of 0.3-0.5 µm in diameter, particle reduction was about 23%, while for particles >10 µm the loss was about 78%. The modelling results showed that depending on the type of filter used, the surgical team generated between 93-99% of total particles, while the outdoor air contributed only 1-6%.
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Although both the size and chemical composition of ambient particles are important parameters in determining their toxicities, their relative contributions are unclear (Heal et al., 2012). Children are particularly at risk to the detrimental health effects that have been linked to long term exposure to airborne particles (See e.g. Ruckerl et al., 2011). However, there is currently limited understanding of the health effects in children due to long term exposure to airborne particles. Schools are locations within an urban environment where children experience significant exposure to vehicle emissions, and to date there is limited information assessing children’s exposure at school. This study is a part of a large project aimed at gaining a holistic picture of the exposure of children to traffic related pollutants. In the current paper, results from the investigation of the elemental composition of airborne particle at urban schools are presented.
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This paper presents a method for the estimation of thrust model parameters of uninhabited airborne systems using specific flight tests. Particular tests are proposed to simplify the estimation. The proposed estimation method is based on three steps. The first step uses a regression model in which the thrust is assumed constant. This allows us to obtain biased initial estimates of the aerodynamic coeficients of the surge model. In the second step, a robust nonlinear state estimator is implemented using the initial parameter estimates, and the model is augmented by considering the thrust as random walk. In the third step, the estimate of the thrust obtained by the observer is used to fit a polynomial model in terms of the propeller advanced ratio. We consider a numerical example based on Monte-Carlo simulations to quantify the sampling properties of the proposed estimator given realistic flight conditions.
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The development of global navigation satellite systems (GNSS) provides a solution of many applied problems with increasingly higher quality and accuracy nowadays. Researches that are carried out by the Bavarian Academy of Sciences and Humanities in Munich (BAW) in the field of airborne gravimetry are based on sophisticated data processing from high frequency GNSS receiver for kinematic aircraft positioning. Applied algorithms for inertial acceleration determination are based on the high sampling rate (50Hz) and on reducing of such factors as ionosphere scintillation and multipath at aircraft /antenna near field effects. The quality of the GNSS derived kinematic height are studied also by intercomparison with lift height variations collected by a precise high sampling rate vertical scale [1]. This work is aimed at the ways of more accurate determination of mini-aircraft altitude by means of high frequency GNSS receivers, in particular by considering their dynamic behaviour.
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In January 2011, Brisbane, Australia, experienced a major river flooding event. We aimed to investigate its effects on air quality and assess the role of prompt cleaning activities in reducing the airborne exposure risk. A comprehensive, multi-parameter indoor and outdoor measurement campaign was conducted in 41 residential houses, 2 and 6 months after the flood. The median indoor air concentrations of supermicrometer particle number (PN), PM10, fungi and bacteria 2 months after the flood were comparable to those previously measured in Brisbane. These were 2.88 p cm-3, 15 µg m-3, 804 cfu m-3 and 177 cfu m-3 for flood-affected houses (AFH), and 2.74 p cm-3, 15 µg m-3, 547 cfu m-3 and 167 cfu m-3 for non-affected houses (NFH), respectively. The I/O (indoor/outdoor) ratios of these pollutants were 1.08, 1.38, 0.74 and 1.76 for AFH and 1.03, 1.32, 0.83 and 2.17 for NFH, respectively. The average of total elements (together with transition metals) in indoor dust was 2296 ± 1328 µg m-2 for AFH and 1454 ± 678 µg m-2 for NFH, respectively. In general, the differences between AFH and NFH were not statistically significant, implying the absence of a measureable effect on air quality from the flood. We postulate that this was due to the very swift and effective cleaning of the flooded houses by 60,000 volunteers. Among the various cleaning methods, the use of both detergent and bleach was the most efficient at controlling indoor bacteria. All cleaning methods were equally effective for indoor fungi. This study provides quantitative evidence of the significant impact of immediate post-flood cleaning on mitigating the effects of flooding on indoor bioaerosol contamination and other pollutants.