Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1368818 / Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 2009

Generation Y (born between 1978 and 1995) has tremendous buying power and represents the future markets for most e-commerce Websites and companies. All online businesses should try to attract them and keep them as returning customers. Although former research findings distinguished Internet shoppers from the non-shoppers within the Internet user population, they did not focus on the characteristics of Internet shoppers and non-shoppers in specific market segments, especially in generations Y. In addition, studies on Taiwanese generation Y's profile and buying behaviours were not many either. / Thesis (DBA(DoctorateofBusinessAdministration))--University of South Australia, 2006.

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Electronically displayed images are becoming increasingly important as an interface between man and information systems. Lengthy periods of intense observation are no longer unusual. There is a growing awareness that specific demands should be made on displayed images in order to achieve an optimum match with the perceptual properties of the human visual system. These demands may vary greatly, depending on the task for which the displayed image is to be used and the ambient conditions. Optimal image specifications are clearly not the same for a home TV, a radar signal monitor or an infrared targeting image display. There is, therefore, a growing need for means of objective measurement of image quality, where "image quality" is used in a very broad sense and is defined in the thesis, but includes any impact of image properties on human performance in relation to specified visual tasks. The aim of this thesis is to consolidate and comment on the image measure literatures, and to find through experiment the salient properties of electronically displayed real world complex imagery that impacts on human performance. These experiments were carried out for well specified visual tasks (of real relevance), and the appropriate application of image measures to this imagery, to predict human performance, was considered. An introduction to certain aspects of image quality measures is given, and clutter metrics are integrated into this concept. A very brief and basic introduction to the human visual system (HVS) is given, with some basic models. The literature on image measures is analysed, with a resulting classification of image measures, according to which features they were attempting to quantify. A series of experiments were performed to evaluate the effects of image properties on human performance, using appropriate measures of performance. The concept of image similarity was explored, by objectively measuring the subjective perception of imagery of the same scene, as obtained through different sensors, and which underwent different luminance transformations. Controlled degradations were introduced, by using image compression. Both still and video compression were used to investigate both spatial and temporal aspects of HVS processing. The effects of various compression schemes on human target acquisition performance were quantified. A study was carried out to determine the "local" extent, to which the clutter around a target, affects its detectability. It was found in this case, that the excepted wisdom, of setting the local domain (support of the metric) to twice the expected target size, was incorrect. The local extent of clutter was found to be much greater, with this having implications for the application of clutter metrics. An image quality metric called the gradient energy measure (GEM), for quantifying the affect of filtering on Nuclear Medicine derived images, was developed and evaluated. This proved to be a reliable measure of image smoothing and noise level, which in preliminary studies agreed with human perception. The final study discussed in this thesis determined the performance of human image analysts, in terms of their receiver-operating characteristic, when using Synthetic Aperture Radar (SAR) derived images in the surveillance context. In particular, the effects of target contrast and background clutter on human analyst target detection performance were quantified. In the final chapter, suggestions to extend the work of this thesis are made, and in this context a system to predict human visual performance, based on input imagery, is proposed. This system intelligently uses image metrics based on the particular visual task and human expectations and human visual system performance parameters. / Thesis (Ph.D.)--Medical School; School of Computer Science, 1999.

In this dissertation, we apply stochastic approximation (SA) to two different problems addressed respectively in Part I and Part II. / The contribution of Part I is mostly theoretical. We consider the problem of online tracking of moving targets such as a signals, through noisy measurements. In particular, we study a non-stationary environment that is subject to sudden discontinuous changes in the underlying parameters of the system. We assume no a priori knowledge about the parameters nor the change-times. Our approach is based on constant stepsize SA. However, because of the unpredictable discontinuous changes, the choice of stepsize is difficult. Small stepsizes improve precision while large stepsizes allow the SA iterates to react faster to sudden changes. / We first investigate target estimation. Our work appears in [Levy 09]. We propose to combine a small constant stepsize with change-point monitoring, and to reset the process at a value closer to the new target when a change is detected. Because the environment is not stationary, we cannot directly apply the usual limit theorems. We thus give a theoretical characterization and discuss the tradeoff between precision and fast adaptation. We also introduce a new monitoring scheme, the regression-based hypothesis test. / Secondly, we consider an online version of the well-known Q-learning algorithm, which operates directly in its target environment, to optimize a Markov decision process. Online algorithms are challenging because the errors, necessarily made when learning, affect performance. Again, under a switching environment the usual limit theorems are not applicable. We introduce an adaptive stepsize selection algorithm based on weak convergence results for SA. Our algorithm automatically achieves a desirable balance between speed and accuracy. These findings are published in [Levy 06, Costa 09]. / In Part II, we study an applied problem related to the mining industry. Strategic management requires managing large portfolios of investments. Because financial resources are limited, only the projects with the highest net present value (NPV), their measure of economic value, will be funded. To value a mine project we need to consider future uncertainties. The approach commonly taken to value a project is to assume that if funded, the mine will be operated optimally throughout its life. Our final aim is not to provide an exact strategy, but to propose an optimization tool to improve decision-making in complex scenarios. Of all the variables involved, the typically large investments in infrastructure, as well as the uncertainty in commodity price, have the most significant impact on the mine value. We thus adopt a simplified model of the infrastructure and extraction optimization problem, subject to price uncertainty. / Common optimization methods are impractical for realistic size models. Our main contribution is the threshold optimization methodology based on measured valued differentiation (MVD) and SA. We also present another simulation-based method, the particles method [Dallagi 07], for comparison purposes. Both methods are well-adapted for high dimensional problems. We provide numerical results and discuss their characteristics and applicability.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 111-114).

Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Eric Feron; Committee Member: Eric Johnson; Committee Member: Jerry Seitzman.