998 resultados para Airborne radio-echo sounding


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Radio Program. Talkin with Tiga Bayles, 98.9 AM National Indigenous Radio Service (NIRS), 9.00-10.00am, Wednesday 21 July 2010. (1 hour program).----- Bronwyn Fredericks discssed the National Aboriginal and Torres Strait Islander Women’s Health Strategy was launched at the Australian Women’s Health Network (AWHN) National Conference in Hobart on the 19 May 2010. Within this radio interview the background of the Strategy is discussed, funding, who did the consultations and the writing. In the interview Bronwyn Fredericks outlines the process of the Strategy’s development and its uses for the future.----- It is important to note that this Strategy does not replace other national or State and Territory documents which identify priorities and needs. The aim is to supplement existing work.

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Airborne measurements of particle number concentrations from biomass burning were conducted in the Northern Territory, Australia, during June and September campaigns in 2003, which is the early and the late dry season in that region. The airborne measurements were performed along horizontal flight tracks, at several heights in order to gain insight into the particle concentration levels and their variation with height within the lower boundary layer (LBL), upper boundary layer (UBL), and also in the free troposphere (FT). The measurements found that the concentration of particles during the early dry season was lower than that for the late dry season. For the June campaign, the concentration of particles in LBL, UBL, and FT were (685 ± 245) particles/cm3, (365 ± 183) particles/cm3, and (495 ± 45) particle/cm3 respectively. For the September campaign, the concentration of particles were found to be (1233 ± 274) particles/cm3 in the LBL, (651 ± 68) particles/cm3 in the UBL, and (568 ± 70) particles/cm3 in the FT. The particle size distribution measurements indicate that during the late dry season there was no change in the particle size distribution below (LBL) and above the boundary layer (UBL). This indicates that there was possibly some penetration of biomass burning particles into the upper boundary layer. In the free troposphere the particle concentration and size measured during both campaigns were approximately the same.

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Much has been written about airborne particulate matter, and countless meetings, workshops and conferences have been held, both nationally and internationally, to address the many scientific challenges which they present, especially when one considers their effects on human health. Particles are a complex airborne pollutant, because of their many different characteristics and the many different ways in which they can be measured and detected. This article summarises the current state of knowledge on the effects of particulate matter and health, based primarily on epidemiological studies which focused on exposure to particle mass, and more recently, on particle number concentration.

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This article focuses on airborne engineered nanoparticles generated in a growing number of commercial and research facilities. Despite their presence in the air of many such facilities, there are currently no established and validated measurement methods to detect them, characterise their properties or quantify their concentrations. In relation to their possible health impacts, the key questions include: (i) Are the particles in the nano-size range are more toxic than larger particles of the same material? (ii) Does the surface chemistry of the lung alters the toxicity of inhaled nanoparticles? (iii) Do nano-fibers pose the same risk as asbestos? and (iv) Are the methods for assessing the health risk are appropriate? This article summarises the state of knowledge in relation to these issues.

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The multi-criteria decision making methods, Preference METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.

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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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This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application specific positioning criteria.

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This paper considers an aircraft collision avoidance design problem that also incorporates design of the aircraft’s return-to-course flight. This control design problem is formulated as a non-linear optimal-stopping control problem; a formulation that does not require a prior knowledge of time taken to perform the avoidance and return-to-course manoeuvre. A dynamic programming solution to the avoidance and return-to-course problem is presented, before a Markov chain numerical approximation technique is described. Simulation results are presented that illustrate the proposed collision avoidance and return-to-course flight approach.

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Particle number concentrations and size distributions, visibility and particulate mass concentrations and weather parameters were monitored in Brisbane, Australia, on 23 September 2009, during the passage of a dust storm that originated 1400 km away in the dry continental interior. The dust concentration peaked at about mid-day when the hourly average PM2.5 and PM10 values reached 814 and 6460 µg m-3, respectively, with a sharp drop in atmospheric visibility. A linear regression analysis showed a good correlation between the coefficient of light scattering by particles (Bsp) and both PM10 and PM2.5. The particle number in the size range 0.5-20 µm exhibited a lognormal size distribution with modal and geometrical mean diameters of 1.6 and 1.9 µm, respectively. The modal mass was around 10 µm with less than 10% of the mass carried by particles smaller than 2.5 µm. The PM10 fraction accounted for about 68% of the total mass. By mid-day, as the dust began to increase sharply, the ultrafine particle number concentration fell from about 6x103 cm-3 to 3x103 cm-3 and then continued to decrease to less than 1x103 cm-3 by 14h, showing a power-law decrease with Bsp with an R2 value of 0.77 (p<0.01). Ultrafine particle size distributions also showed a significant decrease in number during the dust storm. This is the first scientific study of particle size distributions in an Australian dust storm.

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Fixed-wing aircraft equipped with downward pointing cameras and/or LiDAR can be used for inspecting approximately piecewise linear assets such as oil-gas pipelines, roads and power-lines. Automatic control of such aircraft is important from a productivity and safety point of view (long periods of precision manual flight at low-altitude is not considered reasonable from a safety perspective). This paper investigates the effect of any unwanted coupling between guidance and autopilot loops (typically caused by unmodeled delays in the aircraft’s response), and the specific impact of any unwanted dynamics on the performance of aircraft undertaking inspection of piecewise linear corridor assets (such as powerlines). Simulation studies and experimental flight tests are used to demonstrate the benefits of a simple compensator in mitigating the unwanted lateral oscillatory behaviour (or coupling) that is caused by unmodeled time constants in the aircraft dynamics.

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This paper presents a preliminary flight test based detection range versus false alarm performance characterisation of a morphological-hidden Markov model filtering approach to vision-based airborne dim-target collision detection. On the basis of compelling in-flight collision scenario data, we calculate system operating characteristic (SOC) curves that concisely illustrate the detection range versus false alarm rate performance design trade-offs. These preliminary SOC curves provide a more complete dim-target detection performance description than previous studies (due to the experimental difficulties involved, previous studies have been limited to very short flight data sample sets and hence have not been able to quantify false alarm behaviour). The preliminary investigation here is based on data collected from 4 controlled collision encounters and supporting non-target flight data. This study suggests head-on detection ranges of approximately 2.22 km under blue sky background conditions (1.26 km in cluttered background conditions), whilst experiencing false alarms at a rate less than 1.7 false alarms/hour (ie. less than once every 36 minutes). Further data collection is currently in progress.

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Background: Room ventilation is a key determinant of airborne disease transmission. Despite this, ventilation guidelines in hospitals are not founded on robust scientific evidence related to prevention of airborne transmission. Methods: We sought to assess the effect of ventilation rates on influenza, tuberculosis (TB) and rhinovirus infection risk within three distinct rooms in a major urban hospital; a Lung Function Laboratory, Emergency Department (ED) Negative-pressure Isolation Room and an Outpatient Consultation Room were investigated. Air exchange rate measurements were performed in each room using CO2 as a tracer. Gammaitoni and Nucci’s model was employed to estimate infection risk. Results: Current outdoor air exchange rates in the Lung Function Laboratory and ED Isolation Room limited infection risks to between 0.1 and 3.6%. Influenza risk for individuals entering an Outpatient Consultation Room after an infectious individual departed ranged from 3.6 to 20.7%, depending on the duration for which each person occupied the room. Conclusions: Given the absence of definitive ventilation guidelines for hospitals, air exchange measurements combined with modelling afford a useful means of assessing, on a case-by-case basis, the suitability of room ventilation at preventing airborne disease transmission.