Towards an Efficient, Scalable Stream Query Operator Framework for Representing and Analyzing Continuous Fields

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited. This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream query operator frameworks. First, the ST Interpolation Stream Query Operator Framework (STI-SQO framework) continuously transforms sensor streams into rasters using a novel set of stream query operators that perform ST-IDW interpolation. A key component of the STI-SQO framework is the 3D, main memory-based, ST Grid Index that enables high performance ST insertion and deletion of massive numbers of sensor observations through Isotropic Time Cell and Time Block-based partitioning. The ST Grid Index facilitates fast ST search for samples using ST shell-based neighborhood search templates, namely the Cylindrical Shell Template and Nested Shell Template. Furthermore, the framework contains the stream-based ST-IDW algorithms ST Shell and ST ak-Shell for high performance, parallel grid cell interpolation. Secondly, the proposed ST Predicate Stream Query Operator Framework (STP-SQO framework) efficiently evaluates value predicates over ST streams of ST continuous phenomena. The framework contains several stream-based predicate evaluation algorithms, including Region-Growing, Tile-based, and Phenomenon-Aware algorithms, that target predicate evaluation to regions with seed points and minimize the number of raster cells that are interpolated when evaluating value predicates. The performance of the proposed frameworks was assessed with regard to prediction accuracy of output results and runtime. The STI-SQO framework achieved a processing throughput of 250,000 observations in 2.5 s with a Normalized Root Mean Square Error under 0.19 using a 500×500 grid. The STP-SQO framework processed over 250,000 observations in under 0.25 s for predicate results covering less than 40% of the observation area, and the Scan Line Region Growing algorithm was consistently the fastest algorithm tested

The use of modelling approaches to explore interactions in two aquatic host-pest systems

Modelling is a useful tool that has been applied in both human and animal epidemiological research. A model is a simplified system that represents a much more complex phenomenon. Various types of models are available. They are generally used for the purposes of explaining phenomena, making predictions, or exploring different scenarios. Several challenges have been encountered during the construction of models in aquatic animal health and are discussed in the dissertation. The research documented in this dissertation aimed to demonstrate the application of modelling to address specific health and production issues associated with two aquatic animal species (blue mussels and wild Pacific salmon). The first problem dealt with sea lice infestations in wild Pacific salmon populations on the west coast of British Columbia, Canada. The levels of sea lice infestations on wild chum and pink salmon were described and factors associated with inter-annual variation of the infestations were identified using a multivariable logistic regression model. This model included site information as a random effect, to account for spatial aggregation, which provided further details on the degree of clustering at the site level and suggested that the infestation levels depended on the location of fish. This raised the question as to where the risks were and, as a result, a spatial cluster analysis technique (i.e. spatial scan statistics) was used to identify when and where the clusters (of elevated sea lice infestation levels) occurred. The results from clustering analysis can facilitate the hypothesis-generating process for future studies. The second issue was the problem of mussel loss due to biofouling by tunicates (Ciona intestinalis) on Prince Edward Island mussel farms, which was assessed through the use of a mathematical model to describe the dynamics of C. intestinalis populations over the growing season. The model incorporated temperature dependencies, which allowed for the assessment of population dynamics under different temperatures, and was then used to evaluate the effectiveness of different mitigation strategies, using fewer resources than would be required if field trials were undertaken. The research documented in this dissertation demonstrates the use of modelling to address production and health issues in the context of aquatic animals. In addition to the use of field-based trials the research also suggests that modelling can be used as an alternative method to investigate various scenarios and facilitate management planning with advantages in time and cost savings

Evolution, Monitoring and Predicting Models of Rockburst: Precursor Information for Rock Failure

Load/unload response ratio predicting of rockburst; Three-dimensional reconstruction of fissured rock; Nonlinear dynamics evolution pattern of rock cracks; Bayesian model for predicting rockburs

Trajectory prediction of moving objects by means of neural networks

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997Includes bibliographical references (leaves: 103-105)Text in English; Abstract: Turkish and Englishviii, 105 leavesEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively

The quantitative epidemiology of canine neoplastic disease: risk factor identification using diagnostic histopathology data

Research was undertaken to investigate the risk factors for neoplasia in two canine biopsy populations, the first originating fi-om the Canine Infectious Diseases Research Unit (CIDRU) diagnostic histopathology service, based at the University of Glasgow Veterinary School (GUVS), and the second from the diagnostic histopathology service operated by a commercial organisation. Both provide histopathology reports to veterinary practitioners located throughout the United Kingdom and occasionally overseas. The studies were undertaken to determine the feasibility of using these data sources for meaningful epidemiological analyses of host-related risk factors for canine neoplasia. The analytical scope was expanded to explore the effect of submitting practice as a risk factor for canine neoplasia. Finally, spatial and spatio-temporal epidemiological techniques were applied to the CIDRU data to explore the significance of its geographical origin upon an outcome of neoplasia in a canine biopsy. Records pertaining to canine biopsy submissions were extracted from both histopathology databases. The records were subjected to a hierarchical and iterative data cleaning process, which focused upon the main host-related variables of age, gender and breed of dog, when available, and biopsy site of origin. This procedure highlighted a number of important quality assurance issues in both datasets. The coding system used in the CIDRU database was found to be adequate for assisting data preparation, although there were issues relating to the lack of integral data checks and the use of free text input. The extensive use of free text input for the commercial dataset limited the amount of data content that could be prepared fi-om this database for subsequent analysis. Following establishment of data integrity, case-control studies of the cleaned datasets were performed. Multivariable logistic regression was used to assess the effect of the host- related risk factors of age, gender and breed of dog, when available, and site of biopsy, on the outcome of neoplasia in a biopsy submitted to the histopathology service. Similar results were produced in analyses of both datasets. The grouping of data by submitting veterinary practice was considered to cause violation of the assumption of independence for individual biopsies because of unknown practice-related factors associated with biopsy submission. Following the application of inclusion criteria to the data, a practice variable was entered into the host related multivariable models first as a fixed-effect term, then as a random- effect term. The introduction of a variable for practice verified that group effects due to practice were significant in the data from both histopathology services. Spatial and space-time analyses were conducted on the CIDRU dataset using spatial and space-time scan statistics. Graphical display of the results with a Geographical Information System (GIS) illustrated a trend for clusters with low risk of neoplasia diagnosis in biopsies submitted from the north of the UK compared to high risk clusters located in the south of the country. A number of individual practices caused significant subclusters within the main clusters, leading to the proposal of conducting practice-based research to investigate practice-related factors that influence tissue biopsy submission. It was concluded that the histopathology databases provided data suitable for the epidemiological analysis of host-related risk factors for canine neoplasia. The findings of the studies suggest that future research should focus upon identification of factors within individual practices which directly influence the procurement of tissue for histopathological analysis, to accurately ascertain their effect upon the diagnosis of neoplasia in canine biopsies. Practice-based research may also provide insight into differences in geographical occurrence of canine neoplasia, which may lead to the generation of hypotheses regarding possible environmental factors contributing to the aetiology of canine neoplastic disease

Numerical study of real gas effects during bubble collapse using a disequilibrium multiphase model

An explicit density-based solver of the Euler equations for inviscid and immiscible gas-liquid flow media is coupled with real-fluid thermodynamic equations of state supporting mild dissociation and calibrated with shock tube data up to 5000 K and 28 GPa. The present work expands the original 6-equation disequilibrium method by generalising the numerical approach required for estimating the equilibrium pressure in computational cells where both gas and liquid phases co-exist while enforcing energy conservation for all media. An iterative numerical procedure is suggested for taking into account the properties of the gas content as derived from highly non-linear real gas equations of state and implemented in a tabulated form during the numerical solution. The developed method is subsequently used to investigate gaseous bubble collapse cases considering both spherical and 2D asymmetric arrangements as induced by the presence of a rigid wall. It is demonstrated that the predicted maximum temperatures are strongly influenced by the equations of state used; the real gas model predicts a temperature reduction in the bubble interior up to 41% space-averaged and 50% locally during the collapse phase compared to the predictions obtained with the aid of the widely used ideal gas approximation

Optimal measurement locations for parameter estimation of distributed parameter systems

Identifying the parameters with the largest influence on the predicted outputs of a model revealswhich parameters need to be known more precisely to reduce the overall uncertainty on themodel output. A large improvement of such models would result when uncertainties in the keymodel parameters are reduced. To achieve this, new experiments could be very helpful,especially if the measurements are taken at the spatio-temporal locations that allow estimate the parameters in an optimal way. After evaluating the methodologies available for optimal sensor location, a few observations were drawn. The method based on the Gram determinant evolution can report results not according to what should be expected. This method is strongly dependent of the sensitivity coefficients behaviour. The approach based on the maximum angle between subspaces, in some cases, produced more that one optimal solution. It was observed that this method depends on the magnitude of outputs values and report the measurement positions where the outputs reached their extrema values. The D-optimal design method produces number and locations of the optimal measurements and it depends strongly of the sensitivity coefficients, but mostly of their behaviours. In general it was observed that the measurements should be taken at the locations where the extrema values (sensitivity coefficients, POD modes and/or outputs values) are reached. Further improvements can be obtained when a reduced model of the system is employed. This is computationally less expensive and the best estimation of the parameter is obtained, even with experimental data contaminated with noise. A new approach to calculate the time coefficients belonging to an empirical approximator based on the POD-modes derived from experimental data is introduced. Additionally, an artificial neural network can be used to calculate the derivatives but only for systems without complex nonlinear behaviour. The latter two approximations are very valuable and useful especially if the model of the system is unknown.EThOS - Electronic Theses Online ServiceUniversidad del Zulia, Maracaibo, VenezuelaGBUnited Kingdo