http://nova.newcastle.edu.au/vital/access/services/Feed ${session.getAttribute("locale")} 5 http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11654 A spiral scanning method for high-speed Atomic Force Microscopy (AFM) is described in this paper. In this method, the sample is scanned in a spiral pattern instead of the conventional raster pattern. A spiral scan can be produced by applying single frequency cosine and sine signals with slowly varying amplitudes to the x axis and y axis of an AFM scanner respectively. The use of the single tone input signals allows the scanner to move at high speeds without exciting the mechanical resonance of the device and with relatively small control efforts. These scan methods can be incorporated into most modern AFMs with minimal effort since they can be implemented in software using the existing hardware. Experimental results obtained by implementing this scanning method on a commercial AFM indicate that the obtained images are of a good quality and the profile of the calibration grating is well captured up to scan frequency of 120 Hz with a scanner where the first resonance frequency is 580 Hz. 2013-03-15T04:26:04.400Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:12625 Water allocation models can be used to compare water sharing scenarios in regulated catchments to evaluate the effects on both the water users and the environment. These models include a representation of the physical system with modules such as flow routing, rainfall-runoff modelling or groundwater/surface water interactions, as well as management components to take into account infrastructure such as dams, canals or extraction points. Water allocation models can be complex modelling structures with a large number of parameters to be calibrated on limited datasets, especially regarding the management aspects. Additionally, these models are used as a tool in the making of long-term decisions with important social and environmental impacts. As a result, the assessment of uncertainty becomes a critical task to inform the decision-makers about the likely robustness of the model analysis and predictions. Calibration of these models is currently problematic. In particular, the errors affecting system observations are often not properly accounted for, which is a concern since these errors may be quite large. Furthermore, calibration is often performed separately on various components of the system, resulting in inconsistencies when the components are linked. These deficiencies make it difficult to quantify the uncertainty in the predictions of the entire system performance. The Bayesian approach provides a platform to directly address the sources of uncertainty (input, output, and model error) in the model calibration and prediction process. This study seeks to develop a Bayesian multiresponse method for use with river system models, allowing joint calibration to all sources of information available in a particular application. Unlike the traditional approach, joint calibration forces consistency in performance across the entire system. Moreover, the Bayesian approach provides a framework for a proper accounting of uncertainty both in the inferred parameters and in the model predictions. This study illustrates the application of the Bayesian multi-response calibration approach to the STICKMAN model, a simplified river system model which describes key aspects of complex river basin models such as IQQM but is computationally less demanding. The model was calibrated using a Weighted Least Squares method in a synthetic data study. Model calibration used both single and multiple response data (eg. streamflow at the outlet and at internal system nodes, reservoir time series, etc.) to investigate the improvements in parameter estimation associated with the inclusion of additional responses. The use of multiple response data during model calibration was generally found to reduce parameter uncertainty. However, the extent of reductions in uncertainty depended on which responses were included, highlighting that some sources of data are more informative than others. This supports the findings of Kuczera and Mroczkowski’s (1998), who conclude that the value of new sources of response data should be assessed a priori before embarking on (potentially expensive) field campaigns. This study reports the first findings in this project. Future work will explore the effects of multiple response data on model predictive performance, further develop the STICKMAN model to better represent processes and errors, and finally consider IQQM case studies. 2013-03-12T05:41:34.470Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8677 This paper demonstrates a simple second-order controller that eliminates scan-induced oscillation and provides integral tracking action. The controller can be retrofitted to any scanning probe microscope with position sensors by implementing a simple digital controller or op-amp circuit. The controller is demonstrated to improve the tracking bandwidth of an NT-MDT scanning probe microscope from 15 Hz (with an integral controller) to 490 Hz while simultaneously improving gain-margin from 2 dB to 7 dB. The penalty on sensor induced positioning noise is minimal. For the Scanning Probe Microscope considered in this paper, the noise is marginally increased from 0.30 nm RMS to 0.39 nm RMS. Open- and closed-loop experimental images of a calibration standard are reported at speeds of 1 and 10 lines per second (with a scanner resonance frequency of 290 Hz). Compared to traditional integral or PID controllers, the proposed controller provides a bandwidth improvement of approximately ten times. This allows faster imaging and less tracking lag at low speeds. 2013-02-27T02:34:25.454Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:12414 The long-term stability of three clinical electronic portal imaging devices (EPIDs) was studied to determine if longer times between calibrations can be justified. This would make alternatives to flood-field calibration of EPIDs clinically feasible, allowing for more effective use of EPIDs for dosimetry. Images were acquired monthly for each EPID as part of regular clinical quality assurance over a time period of approximately 3 years. The images were analysed to determine (1) the long-term stability of the EPID positioning system, (2) the dose response of the central pixels and (3) the long term stability of each pixel in the imager. The position of the EPID was found to be very repeatable with variations less than 0.3 pixels (0.27 mm) for all imagers (1 standard deviation). The central axis dose response was found to reliably track ion chamber measurements to better than 0.5%. The mean variation in pixel response (1 standard deviation), averaged over all pixels in the EPID, was found to be at most 0.6% for the three EPIDs studied over the entire period. More than 99% of pixels in each EPID showed less than 1% variation. Since the EPID response was found to be very stable over long periods of time, an annual calibration should be sufficient in most cases. More complex dosimetric calibrations should be clinically feasible. 2013-01-14T04:00:03.768Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:12302 The outcomes of a recent brachytherapy welltype ionization chamber calibration error are given in the hope that other brachytherapy treatment centres may better understand the importance of each entry stated in a well chamber calibration certificate. A Nucletron Source Dosimetry System (SDS) PTW well-type ionization chamber was sent for a biennial calibration in September 2010. Upon calibration of the chamber, it was discovered that the previous calibration (in July 2008) contained a +2.6% error in the chamber calibration coefficient. Investigation of the information on the 2008 well chamber calibration certificate indicated the source of the error, which could or should have been detected by both the calibration laboratory and/or the radiation therapy department upon return of the chamber. Consideration must be given to all values and conditions given on the calibration certificate when accepting a ionization chamber back from a calibration laboratory. The issue of whether the source strength from the source calibration certificate or the measured source strength from the calibrated ionization chamber should be entered into the treatment unit is also raised. 2012-12-18T21:22:41.591Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:11523 We examine the effects of local turbulence Reynolds number R⋋ and inflow conditions on the magnitude of vorticity in plane turbulent wakes. Measurements of the spanwise component (ω₃) of the fluctuating vorticity vector ω = ω₁i + ω₂j + ω₃k (here the subscripts 1, 2 and 3 denote the streamwise, lateral and spanwise directions, respectively) are made in turbulent wakes of a screen and a circular cylinder. Lateral distributions of ω*₃ (normalized) in general depend on both R⋋ and inflow conditions. In the developing region, as the downstream distance x₁ increases, ω*₃ increases significantly in the screen wake but decreases slightly in the cylinder wake. Far downstream in the self-preserving region, ω*₃ increases linearly with R⋋ while it no longer varies with x₁ and depends weakly on the influence of inflow conditions. Our analysis suggests that these findings from the measurement of ω*₃ should apply for the magnitude of ω. 2012-09-13T05:42:43.219Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10965 Hydrological calibration and prediction using conceptual models is affected by forcing/response data uncertainty and structural model error. The Bayesian Total Error Analysis methodology uses a hierarchical representation of individual sources of uncertainty. However, it is shown that standard multiblock “Metropolis-within-Gibbs” Markov chain Monte Carlo (MCMC) samplers commonly used in Bayesian hierarchical inference are exceedingly computationally expensive when applied to hydrologic models, which use recursive numerical solutions of coupled nonlinear differential equations to describe the evolution of catchment states such as soil and groundwater storages. This note develops a “limited-memory” algorithm for accelerating multiblock MCMC sampling from the posterior distributions of such models using low-dimensional jump distributions. The new algorithm exploits the decaying memory of hydrological systems to provide accurate tolerance-based approximations of traditional “full-memory” MCMC methods and is orders of magnitude more efficient than the latter. 2012-06-25T06:19:12.959Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10942 Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors describing rainfall uncertainty. Compared with traditional lumped additive error approaches, the quantification of the total predictive uncertainty in the runoff is improved when rainfall and/or structural errors are characterized explicitly. However, the decomposition of the total uncertainty into individual sources is more challenging. In particular, poor identifiability may arise when the inference scheme represents rainfall and structural errors using separate probabilistic models. The inference becomes ill-posed unless sufficiently precise prior knowledge of data uncertainty is supplied; this ill-posedness can often be detected from the behavior of the Monte Carlo sampling algorithm. Moreover, the priors on the data quality must also be sufficiently accurate if the inference is to be reliable and support meaningful uncertainty decomposition. Our findings highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall-runoff data with large unknown errors. Bayesian total error analysis can overcome these problems using independent prior information. The need for deriving independent descriptions of the uncertainties in the input and output data is clearly demonstrated. 2012-06-21T04:28:59.119Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10806 It is well documented that capacitance-based soil moisture sensor measurements are particularly influenced by particle size distribution, density, salinity, and temperature of a soil, in addition to its moisture content. Moreover, the equations provided by manufacturers of soil moisture sensors are often only applicable to a limited number of soil types, thus yielding significant errors when compared with gravimetric measurements for observations in real soils. This limitation makes site-specific calibrations of such sensors necessary. Consequently, development of a general equation provides the possibility to derive the needed parameters from information such as soil type or particle size distribution. This paper describes the development of a general equation for the Campbell Scientific CS616 Water Content Reflectometers using data from sensors installed throughout the Goulburn River experimental catchment. It is subsequently tested using monitoring sites in the Murrumbidgee Soil Moisture Monitoring Network, which were not part of the original development; both monitoring networks are located in south-eastern Australia. Previously developed equations for temperature correction and soil moisture estimation using the Campbell Scientific CS615 Water Content Reflectometer are adapted to the new CS616 sensor. Moreover, relationships between readily available soil properties and the parameters of the general equations are derived. It is shown that the general equations developed here can be applied to data collected in the field using only information on the soil particle size distribution with an RMSE of around 6% m³/m³ (<1% m³/m³ under laboratory conditions; which is a significant improvement in comparison to 14% m³/m³ when using the manufacturer’s equations). 2012-05-16T04:40:03.889Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8881 The development of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall runoff (CRR) models remains a key challenge in hydrology. For practical purposes, reliable and robust characterization of predictive uncertainty is important for comparing the impact of management options on key variables of interest (e.g. reservoir yield, meeting low flow criteria for ecological purposes). For research purposes, robust identification of the sources of uncertainty is essential for understanding how to reduce predictive uncertainty, and thereby enhance model predictions. Both these tasks are recognized as a major challenge for hydrological modelling science. It is generally recognized that CRR modelling is affected by three main sources of uncertainty: (i) input uncertainty, e.g., measurement and sampling errors in the estimates of areal rainfall; (ii) output uncertainty, e.g., rating curve errors affecting runoff estimates; and (iii) structural uncertainty (sometimes referred to as “model uncertainty”), arising from lumped and simplified representation of hydrological processes in CRR models. Various approaches in the literature have aimed to quantify the individual contributions of input, output and structural uncertainties to the total predictive uncertainty. The beneficial impact of quantifying input errors on CRR parameter estimates and the reliability of model predictions has been established and techniques for evaluating model structural errors have begun to appear. However, almost all these studies make assumptions regarding the output (runoff measurement) errors. This study evaluated whether there is any beneficial impact in utilizing rating curve data to fit a runoff measurement error model. This was undertaken by incorporating this fitted output error (OE) model into the Bayesian total error analysis (BATEA) methodology. BATEA provides a comprehensive framework to hypothesize, infer and evaluate probability models describing input, output and model structural error. BATEA was used to calibrate the GR4J model to the ephemeral Horton catchment. To evaluate the impact of the fitted OE model the calibration results were compared to two other OE models; one representing a commonly assumed OE model and the other representing a conservative “overestimate” of the OE model. The estimated predictive uncertainty was more consistent with the observed runoff data for the fitted OE model than the assumed OE (which systemically under predicted the observed runoff) and the conservative OE (which overestimated the predictive uncertainty). This result was consistent in model calibration and validation. This illustrates for this case study there was beneficial impact in incorporating a fitted OE model. Comparison of the posterior distributions of parameters showed that the different OE model produced significantly different parameter estimates. This has implications for regionalizing parameters estimations to produce predictions in ungauged basins. Comparison of the estimated input/structural errors also showed substantial differences for different OE. This suggests an interdependency between the error sources, where reliable estimates of input/structural errors will be dependent on reliable estimates of the output error. 2012-03-26T22:00:04.859Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6843 The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall-runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer, and evaluate probability models describing input, output, and model structural error. This paper assesses the ability of BATEA and standard calibration approaches (standard least squares (SLS) and weighted least squares (WLS)) to address two key requirements of uncertainty assessment: (1) reliable quantification of predictive uncertainty and (2) reliable estimation of parameter uncertainty. The case study presents a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients. Postcalibration diagnostics, including checks of predictive distributions using quantile‐quantile analysis, suggest that while still far from perfect, BATEA satisfied its assumed probability models better than SLS and WLS. In addition, WLS/SLS parameter estimates were highly dependent on the selected rain gauge and calibration period. This will obscure potential relationships between CRR parameters and catchment attributes and prevent the development of meaningful regional relationships. Conversely, BATEA provided consistent, albeit more uncertain, parameter estimates and thus overcomes one of the obstacles to parameter regionalization. However, significant departures from the calibration assumptions remained even in BATEA, e.g., systematic overestimation of predictive uncertainty, especially in validation. This is likely due to the inferred rainfall errors compensating for simplified treatment of model structural error. 2012-03-12T06:42:30.525Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6147 This paper presents a system for automating the time consuming task of manual colour calibration for a mobile robot. By converting a series of YUV images to HSI format and analysing histogram data it can be seen that there are distinct regions of colour space for each object colour class and that one dimension, hue, can be used to uniquely identify each colour class. Using an expectation maximisation (EM) algorithm to estimate the parameters of a Gaussian mixture model, it is proposed that the HSI colour space can be segmented and automatically labeled for the purpose of automatic colour calibration. This method is applied to a Aldebaran Nao robot vision system that uses a ‘soft’ colour classification method to classify non-unique colour space. By reducing the colour labeling dimension to one and implementing soft classification principles, a reliable automatic calibration system was achieved. 2012-03-07T23:14:02.543Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6077 Research Doctorate - Doctor of Philosophy (PhD) 2011-12-13T01:00:02.057Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:8928 Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the sampling and measurement uncertainty in the observed input/output data and by the structural error of the model conceptualisation. The Bayesian Total Error Analysis methodology (BATEA) provides the opportunity to directly and comprehensively address these sources of uncertainty. BATEA is based on Bayesian hierarchical methods and uses explicit error models for input/output data and structural errors. Previous studies demonstrated that simultaneous inference on forcing (e.g. rainfall) and structural errors requires strong prior knowledge of the error mechanisms (e.g., statistical properties of rainfall errors).This paper investigates a geostatistical approach to estimate the sampling error made in approximating the areal rainfall by averaging point-values from a raingauge network. The geostatistical model generates an ensemble of rainfall fields conditioned on gauged values. This ensemble is treated as a distribution of the “true” areal rainfall over the catchment and used as a prior in the BATEA framework. A case study shows that the inclusion of such prior knowledge allows simultaneous estimation of input and structural errors, whereas in the absence of such information the inference is ill-posed. 2011-09-13T01:01:18.355Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:1791 The neutron probe has proven to be an effective means for monitoring long term in situ soil moisture variations. However, it is difficult to experimentally correlate neutron probe data (i.e., neutron counts) with accurate estimates of absolute soil moisture content, particularly for unsaturated clay soils. In this paper, a numerical model based on multigroup neutron diffusion theory is employed to predict the distribution of neutron flux in a neutron probe system. The model discretizes the neutron energy spectrum into seven intervals, with energy-dependent diffusion coefficients and parameters for each energy interval. The finite element method is employed to solve the coupled seven-group neutron diffusion equations. It is demonstrated that the numerical results compare very well with both laboratory experimental results and field measurements. The theoretical approach to neutron probe calibration described herein offers significant time and cost savings over traditional calibration methods, and potentially opens up new applications for neutron probe monitoring. 2011-05-03T00:20:05.275Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:1261 Near-edge X-ray absorption spectroscopy (NEXAFS) is widely used to probe the chemistry and structure of surface layers. Moreover, using ultra-high brilliance polarised synchrotron light sources, it is possible to determine the molecular alignment of ultra-thin surface films. However, the quantitative analysis of NEXAFS data is complicated by many experimental factors and, historically, the essential methods of calibration, normalisation and artefact removal are presented in the literature in a somewhat fragmented manner, thus hindering their integrated implementation as well as their further development. This paper outlines a unified, systematic approach to the collection and quantitative analysis of NEXAFS data with a particular focus upon carbon K-edge spectra. As a consequence, we show that current methods neglect several important aspects of the data analysis process, which we address with a combination of novel and adapted techniques. We discuss multiple approaches in solving the issues commonly encountered in the analysis of NEXAFS data, revealing the inherent assumptions of each approach and providing guidelines for assessing their appropriateness in a broad range of experimental situations. 2010-04-27T06:56:08.406Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:2271 In this paper, we report the use of an NIR fiber-optic spectrometer with a high-speed diode array for calibration-free monitoring and modeling of the reaction of acetic anhydride with butanol using the catalyst 4-(dimethylamino)pyridine in a microscale batch reactor. Acquisition of spectra at 5 ms/scan gave information relevant for modeling these fast batch processes with a single multibatch kinetic model. Nonlinear fitting of a first-principles model directly to the reaction spectra gave calibration-free estimates of time-dependent concentration profiles and pure component spectra. The amount of catalyst was varied between different batches to permit accurate estimation of its effect in the multiway model. A wide range of different models with increasing complexity could be fit to each batch individually with low residuals and apparent low lack of fit. However, only one model properly estimated the concentration profiles when all five batches were fitted simultaneously in a multiway kinetic model. Inclusion of on-line temperature measurements and use of an Arrhenius model for the estimated rate constant gave significantly improved model fits compared to an isothermal kinetic model. Augmentation of prerun batches with data from an additional batch permitted model-based forecasts of reaction trajectories, reaction yield, reaction end points, and process upsets. One batch with added water to simulate a process upset was easily detected by the calibration free process model. 2010-04-27T06:19:33.946Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:1042 Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the uncertainty in the observed forcing/response data and the structural error in the model. This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterisation of model error in CRR modelling has been thwarted by the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. This paper argues that the fluxes in CRR models should be treated as stochastic quantities because their estimation involves spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterisation of model error. The hypothesis advanced in this paper is that CRR model error can be characterised by storm-dependent random variation of one or more CRR model parameters. A simple sensitivity analysis is used to identify the parameters most likely to behave stochastically, with variation in these parameters yielding the largest changes in model predictions as measured by the Nash–Sutcliffe criterion. A Bayesian hierarchical model is then formulated to explicitly differentiate between forcing, response and model error. It provides a very general framework for calibration and prediction, as well as for testing hypotheses regarding model structure and data uncertainty. A case study calibrating a six-parameter CRR model to daily data from the Abercrombie catchment (Australia) demonstrates the considerable potential of this approach. Allowing storm-dependent variation in just two model parameters (with one of the parameters characterising model error and the other reflecting input uncertainty) yields a substantially improved model fit raising the Nash–Sutcliffe statistic from 0.74 to 0.94. Of particular significance is the use of posterior diagnostics to test the key assumptions about the data and model errors. The assumption that the storm-dependent parameters are log-normally distributed is only partially supported by the data, which suggests that the parameter hyper-distributions have thicker tails. The results also indicate that in this case study the uncertainty in the rainfall data dominates model uncertainty. 2010-04-27T06:08:06.667Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:2545 The majority of environmental models require calibration of some or all of their parameters before meaningful predictions of catchment behaviour can be made. Despite the importance of reliable parameter estimates, there are growing concerns about the ability of objective-based inference methods to adequately calibrate environmental models. The problem lies with the formulation of the objective or likelihood function, which is currently implemented using essentially ad-hoc methods. We outline limitations of current calibration methodologies, including least squares, multi-objective, GLUE and Kalman filter schemes and introduce a more systematic Bayesian Total Error Analysis (BATEA) framework for environmental model calibration and validation. BATEA imposes a hitherto missing rigour in environmental modelling by requiring the specification of physically realistic uncertainty models with explicit assumptions that can and must be tested against available evidence. Distinguishing between the various sources of errors will reduce the current ambiguity about parameter and predictive uncertainty and enable rational testing of environmental model hypotheses. A synthetic study demonstrates that explicitly accounting for forcing errors leads to immediate advantages over traditional least squares methods that ignore rainfall history corruption and do not directly address the sources of uncertainty in the calibration. We expect that confronting all sources of uncertainty, including data and model errors, will force fundamental shifts in the model calibration/verification philosophy. 2010-04-27T06:03:50.116Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:2519 The capacity reduction factors (ϕ) in AS3700-2001 have been derived by 'soft' conversion from previous working stress codes, which were themselves derived from overseas. Compressive wall strengths in AS3700-2001 are discounted by a capacity reduction factor ϕ=0.45. This factor seems low, even for masonry. The paper compares design strengths with actual wall test data to estimate the model error in probabilistic terms. This information, in conjunction with probabilistic models for material properties and loads, is used to calculate the structural reliability (complement of probability of failure) of masonry walls in compression. The existing safety levels were found to be much higher than those accepted for other materials. Based on this rational analysis, it is recommended that ϕ for walls loaded concentrically in compression be increased from 0.45 to 0.75, resulting in a 66% increase in the compressive design capacity of structural masonry. 2010-04-27T06:03:38.327Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:178 This paper examines the potential of low-cost digital cameras for close range surface measurement using feature-based image matching methods. This is achieved through extracting digital elevation models (DEMs) and comparing accuracies between three low-cost consumer-grade digital cameras (Sony DSC-P10, Olympus C3030, Nikon Coolpix 3100) and the proven Kodak DCS460. Surprisingly, the tests revealed that the highest accuracies were achieved using the Sony DSC-P10, not the Kodak DCS460, whilst the other two cameras certainly proved suitable for most close range surface measurement tasks. Lens modelling was found to provide a limiting constraint on final accuracies, with very small systematic error surfaces caused by residual imperfections in lens modelling. The IMAGINE OrthoBASE Pro software and an independent self-calibrating bundle adjustment were used to process these data. These tests identified an inaccuracy in the self-calibrating capability of IMAGINE OrthoBASE Pro version 8(.)6 and Leica Geosystems LPS 8(.)7, which will be rectified in subsequent software releases. The study has demonstrated that cheaper consumer-grade digital cameras have potential for routine surface measurement provided lens modelling is considered. The lead author is maintaining a web-based repository,for camera calibration data, which may assist other users. 2010-04-27T06:00:06.253Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:308 In order to assess performance and to improve predictions, land surface models are routinely calibrated against measurements of either latent heat or sensible heat fluxes. Generally, little regard is given to the multi-output nature of these models, resulting in a model evaluation that is inherently biased towards the calibration variable. In this paper, an assessment strategy that accounts for multiple outputs is explored and an examination of incorporating alternative sources of information to assess performance is undertaken. The benefits of such a multi-objective calibration framework are illustrated through comparison with traditional single objective calibration. Results indicate that combining different observation data streams for calibration purposes assists in producing a more robust process model and provides improved surface flux predictions. Further, the utility of using correlated, if not commensurate, sources of data, is demonstrated through analysis of a time series of surface temperature measurements. (C) 2004 Elsevier B.V. All rights reserved. 2010-04-27T05:49:05.176Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:516 We have undertaken a comparative study of down-core variation in multiproxy palaeoclimate data (magnetic susceptibility, calcium carbonate content and total organic carbon) from two coastal water bodies (Myall and Tuggerah Lakes) in temperate eastern Australia to identify local, regional and global-forcing factors within Holocene estuarine sediments. The two lakes lie within the same temperate climate zone adjacent to the Tasman Sea, but are not part of the same catchment and drain different geological provinces. One is essentially a freshwater coastal lake whereas the other is a brackish back-barrier lagoon. Despite these differences, data from two sites in each of the two lakes have allowed us to investigate and compare cyclicity in otherwise uniform, single facies sediments within the frequency range of 200-2000 years, limited by the sedimentation rate within the lakes and our sample requirements. We have auto- and cross-correlated strong periodicities at similar to 360 years, similar to 500-530 years, similar to 270-290 years, 420-450 years and similar to 210 years, and subordinate periods of similar to 650 years, 1200-1400 years and similar to 1800 years. Our thesis is that climate is the only regionally available mechanism available to control common millennial and centennial scale cyclicity in these sediments, given the geographical and other differences. However, regional climate may not be the dominant effect at any single time and either location. Within the range of frequency spectral peaks we have identified, several fall within known long-term periodical fluctuations of sun spot activity; however, feedback loops associated with short-term orbital variation, such as Dansgaard-Oeschger cycles, and the relationship between these and palaeo-ENSO variation, are also possible contributors. 2010-04-27T05:38:12.041Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3842 The PUB initiative aims to integrate knowledge of hydrological processes to provide the best hydrological characterization of ungauged basins. This requires the integration of models and methods to achieve those objectives. In this paper, recent modelling activities are reviewed, with the aim of demonstrating potential application to ungauged basins. First, the development and testing of process-oriented hydrological models is presented. Examples are shown of the utility of remote sensing in conditioning hydrological model parameters at the catchment scale. Subsequently, a Bayesian error-sensitive model calibration scheme (BATEA) is presented. This scheme acknowledges that rainfall errors propagate and persist in hydrological models, corrupting the parameter estimates. It is shown that BATEA offers parameter estimates unbiased by error in rainfall data. BATEA will be applied to multiple models across a range of basins using MOPEX and Australian data. As regionalization relationships will be derived through unbiased model parameter estimates, it is hoped that stronger relationships between catchment characteristics and model parameter values may be identified, permitting improved model performance in ungauged basins. Finally, multi-decadal climate variability across New South Wales is demonstrated and an ENSO-based mechanism is elucidated. Such understanding of climate/hydrology interfaces offers a greater insight into hydrological risk assessment at different temporal scales and may easily be coupled to regionalized models for ungauged basins at continental scales. 2010-04-27T05:32:55.472Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3151 Amorphous silicon (a-Si) electronic portal imaging devices (EPIDs) have typically been calibrated to dose at central axis (CAX). Division of acquired images by the flood-field (FF) image that corrects for pixel sensitivity variation as well as open field energy-dependent off-axis response variation should result in a flat EPID response over the entire matrix for the same field size. While the beam profile can be reintroduced to the image by an additional correction matrix, the CAX EPID response to dose calibration factor is assumed to apply to all pixels in the detector. The aim of this work was to investigate the dose response of the Varian aS500 amorphous silicon detector across the entire detector area. First it was established that the EPID response across the panel became stable (within ~0.2%) for MU settings greater than ~200 MU. The EPID was then FF calibrated with a high MU setting of ~400 for all subsequent experiments. Whole detector images with varying MU settings from 2–500 were then acquired for two dose rates (300 and 600 MU/min) for 6 MV photons for two EPIDs. The FF corrected EPID response was approximately flat or uniform across the detector for greater than 100 MU delivered (within 0.5%). However, the off-axis EPID response was greater than the CAX response for small MU irradiations, giving a raised EPID profile. Up to 5% increase in response at 20 cm off-axis compared to CAX was found for very small MU settings for one EPID, while it was within 2% for the second (newer) EPID. Off-axis response nonuniformities attributed to detector damage were also found for the older EPID. Similar results were obtained with the EPID at 18 MV energy and operating in asynchronous mode (acquisition not synchronized with beam pulses), however the profiles were flatter and more irregular for the small MU irradiations. By moving the detector laterally and repeating the experiments, the increase in response off-axis was found to depend on the pixel position relative to the beam CAX. When the beam was heavily filtered by a phantom the off-axis response variation was reduced markedly to within 0.5% for all MU settings. Independent measurements of off-axis point doses with ion chamber did not show any change in off-axis factor with MUs. Measurements of beam quality (TMR₂₀−₁₀) for MU settings of 2, 5, and 100 at central axis and at 15 cm off-axis could not explain the effect. The response change is unlikely to be significant for clinical IMRT verification with this imaging/acclerator system where MUs are of the order of 100–300, provided the detector does not exhibit radiation damage artifacts. 2010-04-27T05:06:15.744Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3149 The aim of this work was to experimentally determine the difference in response of an amorphous silicon (a-Si) electronic portal imaging device (EPID) to the open and multileaf collimator (MLC) transmitted beam components of intensity modulated radiation therapy (IMRT) beams. EPID dose response curves were measured for open and MLC transmitted (MLCtr) 10×10 cm² beams at central axis and with off axis distance using a shifting field technique. The EPID signal was obtained by replacing the flood-field correction with a pixel sensitivity variation matrix correction. This signal, which includes energy-dependent response, was then compared to ion-chamber measurements. An EPID calibration method to remove the effect of beam energy variations on EPID response was developed for IMRT beams. This method uses the component of open and MLCtr fluence to an EPID pixel calculated from the MLC delivery file and applies separate radially dependent calibration factors for each component. The calibration procedure does not correct for scatter differences between ion chamber in water measurements and EPID response; these must be accounted for separately with a kernel-based approach or similar method. The EPID response at central axis for the open beam was found to be 1.28±0.03 of the response for the MLCtr beam, with the ratio increasing to 1.39 at 12.5 cm off axis. The EPID response to MLCtr radiation did not change with off-axis distance. Filtering the beam with copper plates to reduce the beam energy difference between open and MLCtr beams was investigated; however, these were not effective at reducing EPID response differences. The change in EPID response for uniform sliding window IMRT beams with MLCtr dose components from 0.3% to 69% was predicted to within 2.3% using the separate EPID response calibration factors for each dose component. A clinical IMRT image calibrated with this method differed by nearly 30% in high MLCtr regions from an image calibrated with an open beam calibration factor only. Accounting for the difference in EPID response to open and MLCtr radiation should improve IMRT dosimetry with a-Si EPIDs. 2010-04-27T05:06:10.335Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3417 A calibration procedure for content depth profile analysis by glow discharge optical emission spectroscopy is presented. This new method is based on the well established relative or internal standard method. The method allows reduction of the number of CRM with known sputtering rates to a strict minimum, while still using a large number of CRMs for establishing the analytical curves. The new calibration method allows the uncertainty in the calculation of the chemical composition to be separated from the uncertainties of sputtering rate measurements. The application of this new calibration procedure is applied to industrial samples and further possible improvements are discussed. 2010-04-27T05:00:59.771Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:3331 Conceptual hydrological modelling has under-utilised classical parameter analysis techniques (for both optimisation and uncertainty assessment) due to the prohibitively complicated nonsmooth geometry of typical parameter distributions. In the companion paper, a numerically robust model implementation framework was developed, based on stable time stepping schemes and careful threshold smoothing to eliminate the roughness of parameter surfaces. Here, this framework is exploited to enable parameter estimation using powerful and well-established techniques including: (i) Newton-type optimisation and (ii) principal-component-type (Hessian-based) uncertainty analysis. A case study using a representative rainfall-runoff-snow model illustrates the advantages of these previously unavailable methods, contrasting them with slower and less informative current approaches designed for nonsmooth functions. In addition to boosting the computational efficiency, the methods advocated in the paper yield more insight into improved model formulation and parameterisation (e.g., reducing model nonlinearity, detecting ill-conditioning and handling parameter multi-optimality). The impact of extreme model nonlinearity on model and parameter stability is also discussed, focusing on model identification aspects. 2010-04-27T05:00:29.821Z ]]> http://nova.newcastle.edu.au/vital/access/manager/Repository/uon:5131 The National Airborne Field Experiment 2006 (NAFE'06) was conducted during a three week period of November 2006 in the Murrumbidgee River catchment, located in southeastern Australia. One objective of NAFE'06 was to explore the suitability of the area for SMOS (Soil Moisture and Ocean Salinity) calibration/validation and develop downscaling and assimilation techniques for when SMOS does come on line. Airborne L-band brightness temperature was mapped at 1 km resolution 11 times (every 1-3 days) over a 40 by 55 km area in the Yanco region and 3 times over a 40 by 50 km area that includes Kyeamba Creek catchment. Moreover, multi-resolution, multi-angle and multi-spectral airborne data including surface temperature, surface reflectance (green, read and near infrared), lidar data and aerial photos were acquired over selected areas to develop downscaling algorithms and test multi-angle and multi-spectral retrieval approaches. The near-surface soil moisture was measured extensively on the ground in eight sampling areas concurrently with aircraft flights, and the soil moisture profile was continuously monitored at 41 sites. Preliminary analyses indicate that (i) the uncertainty of a single ground measurement was typically less than 5% vol. (ii) the spatial variability of ground measurements at 1 km resolution was up to 10% vol. and (iii) the validation of 1 km resolution L-band data is facilitated by selecting pixels with a spatial soil moisture variability lower than the point-scale uncertainty. The sensitivity of passive microwave and thermal data is also compared at 1 km resolution to illustrate the multi-spectral synergy for soil moisture monitoring at improved accuracy and resolution. The data described in this paper are available at www.nafe.unimelb.edu.au. 2010-04-27T04:41:45.826Z ]]>