From mid-2000s, the realm of portable and embedded computing has expanded to include a wide variety of applications. Data mining is one of the many applications that are becoming common on these devices. Many of today’s data mining applications are compute and/or data intensive, requiring more processing power than ever before, thus speed performance is a major issue. In addition, embedded devices have stringent area and power requirements. At the same time manufacturing cost and time-to-market are decreasing rapidly. To satisfy the constraints associated with these devices, and also to improve the speed performance, it is imperative to incorporate some special-purpose hardware into embedded system design. In some cases, reconfigurable hardware support is desirable to provide the flexibility required in the ever-changing application environment. Our main objective is to provide chip-level and reconfigurable hardware support for data mining applications in portable, handheld, and embedded devices. We focus on the most widely used data mining tasks, clustering and classification. Our investigation on the hardware design and implementation of similarity computation (an important step in clustering/classification) illustrates that the chip-level hardware support for data mining operations is indeed a feasible and a worthwhile endeavour. Further performance gain is achieved with hardware optimizations such as parallel processing. To address the issue of limited hardware foot-print on portable and embedded devices, we investigate reconfigurable computing systems. We introduce dynamic reconfigurable hardware solutions for similarity computation using a multiplexer-based approach, and for principal component analysis (another important step in clustering/classification) using partial reconfiguration method. Experimental results are encouraging and show great potential in implementing data mining applications using reconfigurable platform. Finally, we formulate a design methodology for FPGA-based dynamic reconfigurable hardware, in order to select the most efficient FPGA-based reconfiguration method(s) for specific applications on portable and embedded devices. This design methodology can be generalized to other embedded applications and gives guidelines to the designer based on the computation model and characteristics of the application. / Graduate

Integration of multisensor airborne data for object based image analysis, and spectral classification of individual trees is complicated by the multi-modal operation of complimentary sensors required for intersensor calibration. Simplified and generalized representations of sensor data impacts the ability to calibrate, rectify, segment, and extract scene objects represented as differing scales. This research project examines the effect and implications of using lidar to calibrate, and rectify airborne imaging spectrometer to an appropriate resolution digital surface model. Through the use of a normalized digital canopy surface model, tree objects are detected and integrated with field surveyed species data for trees of classification interest. Canopy structure is used to segment, and extract airborne imaging spectrometer data for assessment and suitability in species classification. / Graduate

Classification using mixture distributions to model each class has not received too much attention in the literature. The most important attempts use normal distributions as com- ponents in these mixtures. Recently developed methods have allowed the use of these kinds of models as a flexible approach for density estimation. Most of the methods de- veloped so far use plug-in estimates for the parameters and assume that the number of components in the mixture is known. We obtain a predictive classifier for the classes by using Markov Chain Monte Carlo techniques which allow us to obtain a sampling chain for the parameters. This fully Bayesian approach to classification has the advantage that the number of components for each class is taken as another variable parameter and integrated out of the classification. To achieve this we use a birth-and-death/Gibbs sampler algorithm developed by Stephens (1997). We use five different datasets, two simulated ones to test the methods on a single class and three real datasets to test the methods for classification. We look at different models to de- fine which gives better flexibility in the modelling and an overall better classification. We look at different types of priors for the means and dispersion matrices of the components. Joint conjugate priors and an independent conjugate priors for the means and dispersion matrices for the components are used. We use a model with a common dispersion matrix for all the components and another one with a reparametrisation of these dispersion ma- trices into size, shape and orientation (Banfield and Raftery (1993)). We allow the sizes to differ while keeping a common shape and orientation for the dispersion matrices of the components in a class. We found that this type of modelling with independent conjugate priors for the means and dispersions while allowing the sizes of the dispersions to vary gave the best results for classification purposes as it allowed great flexibility and separation between the compo- nents of the classes.

Having outlined the present situation as regards rival taxonomic philosophies, and some of its historical background, the thesis examines this attempt to recategorize taxa as individual-like entities, and finds it wanting. The properties of species which render them regardable as individuals do not readily extend to more inclusive levels, or, if they do, are not readily restricted solely to cladistic taxa. Cladistic systematization, in moving away from the notion of a taxon as a class of similar entities, may cease to convey the information expected of a classification system. The practice of biology requires a more flexible and more stable taxonomy than can be provided by strict adherence to cladistic rules, and taxa are-better regarded as 'historical classes', delineated neither by pure unanalysed similarity nor by logical transformation of hypotheses of phylogenetic relationship, but by a considered pragmatic synthesis of the two, employing the notion of convexity as a criterion of acceptability.

Until recently, methodologies to extract sub-pixel information from remotely sensed data have focused on linear un-mixing models and so called fuzzy classifiers. Recent research has suggested that neural networks have the potential for providing sub- pixel information. Neural networks offer an attractive alternative as they are non- parametric, they are not restricted to any number of classes, they do not assume that the spectral signatures of pixel components mix linearly and they do not necessarily have to be trained with pure pixels. The thesis tests the validity of neural networks for extracting sub-pixel information using a combination of qualitative and quantitative analysis tools. Previously published experiments use data sets that are often limited in terms of numbers of pixels and numbers of classes. The data sets used in the thesis reflect the complexity of the landscape. Preparation for the experiments is canied out by analysing the data sets and establishing that the network is not sensitive to particular choices of parameters. Classification results using a conventional type of target with which to train the network show that the response of the network to mixed pixels is different from the response of the network to pure pixels. Different target types are then tested. Although targets which provide detailed compositional information produce higher accuracies of classification for subsidiary classes, there is a trade off between the added information and added complexity which can decrease classification accuracy. Overall, the results show that the network seems to be able to identify the classes that are present within pixels but not their proportions. Experiments with a very accurate data set show that the network behaves like a pattern matching algorithm and requires examples of mixed pixels in the training data set in order to estimate pixel compositions for unseen pixels. The network does not function like an unmixing model and cannot interpolate between pure classes.

Copies of author's previously published articles inserted . / Errata slip pasted on back endpaper. / Bibliography : leaves 220-233. / xi, 233, [49] leaves : ill., maps ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Members of the wasp subfamily Cardiochilinae (Hymenoptera: Braconidae) are endoparasitic in lepidopterous larvae and have proven and potential importance as biocontrol agents of agricultural pests. This thesis examines the taxonomy of species in the Australasian region, and develops a phylogenetic framework for world genera based on cladistic methodology. / Thesis (Ph.D.)--University of Adelaide, Dept. of Crop Protection, 1996

i, 224 leaves : ill. ; 31 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.1975) from the Dept. of Zoology, University of Adelaide

Thesis (Ph.D.)--University of Western Australia, 2007.

Thesis (Ph. D.)--Florida State University, 1994. / Typescript. Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.