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(2007)ThesisMost current DFVPLs support flow control to facilitate experiments and complex problems. However, current approaches in DFVPLs still remain inefficient. We show that inadequacies in existing visual programming languages may be magnified in applications involving image analysis. These include a lack of efficient communication mechanisms and strong dependency on human involvement to customise properties. For instance, properties in one computational component can not be shared for other components. Moreover, conditional expressions used in control components hold data values that are unrelated with those computational components. Furthermore, since image processing libraries usua.lly only explicitly support pipeline processing, as exemplified by the widely used Insight Toolkit for Medical Image Segmentation and Registration (ITK), a looping algorithm would be difficult to implement without a feedback mechanism supported by the visual language itself. We propose a data-flow visual programming language that encompasses several novel control constructs and parameterised computational units. These components are facilitated by a novel hybrid data-flow model. We also present several conceptual models and design alternatives for control constructs. Several mechanisms and techniques are provided to enhance data propagation for these components. We demonstrate, in an environment that utilises ITK as the underlying processing engine, that the inadequacies in existing DFVPLs can be satisfactorily addressed through the visual components proposed in this thesis.
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(2018)ThesisIndoor localization traditionally uses fingerprinting approaches based on Received Signal Strength (RSS), where RSS plays a crucial role in determining the nature and characteristics of location fingerprints stored in a radio-map. The RSS is a function of the distance between transmitter and receiver, which can vary due to in-path interference. This thesis identifies the factors affecting the RSS in indoor localization, discusses the effect of identified factors such as spatial, temporal, environmental, hardware and human presence on the RSS through extensive measurements in a typical IEEE 802.11 a/g/n network, and demonstrates the reliability of RSS-based location fingerprints using statistical analysis of the measured data for indoor localization. This thesis presents a novel Wi-Fi fingerprinting system CSI-MIMO, which uses fine-grained information known as Channel State Information (CSI). CSI-MIMO exploits frequency diversity and spatial diversity in an Orthogonal Frequency Division Multiplexing (OFDM) system using a Multiple Inputs Multiple Outputs (MIMO) system. CSI-MIMO uses either magnitude of CSI or a complex CSI location signature, depending on mobility in indoor environments. The performance of CSI-MIMO is compared to Fine-grained Indoor Fingerprinting System (FIFS), CSI with Single Input Single Output (SISO), and a simple CSI with MIMO. The experimental results show significant improvement with accuracy of 0.98 meter in a static environment and 0.31 meter in a dynamic environment, with optimal war-driving over existing CSI-based fingerprinting systems.
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(2008)ThesisThe effectiveness of location aware applications is dependent on the accuracy of the supporting positioning system. This work evaluates the accuracy of an indoors 802.11 positioning system based on signal strength fingerprinting. The system relies on an empirical survey of signal strength prior to positioning. During this survey, signal strength recordings are made at a set of positions across the environment. These recordings are used as training data for the system during positioning. In this thesis, two surveying methods, five positioning algorithms, and two spatial output averaging methods are trialled. Accuracy is determined by empirical testing in two separate environments: a 100m square domestic house and the 1,333m square third floor of the University of New South Wales Computer Science and Engineering building. In the two environments, the lowest mean distance errors are 1.25m and 2.86m respectively.