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<p>Previous findings have suggested that auditive white noise benefits cognitive performance under certain circumstances. The primary purpose of the present pilot study was to explore the effects of visual white noise on verbal episodic memory performance in a normal participant population. Performance was assessed by an immediate free recall test. A secondary purpose was to explore whether participants` eye blink rates and/or temporal processing alters in different noise conditions. The findings of the present study suggest that visual white noise does not affect recall performance among normal participants. However, partially different memory systems and/or memorizing techniques might be used in different noise conditions. Furthermore, noise was not found to affect participants` blink rates or temporal processing.</p>

Salient contour detection is an important lowlevel visual process in the human visual system, and has significance towards understanding higher visual and cognitive processes. Salience detection can be investigated by examining the visual cortical response to visual input. Visual response activity in the early stages of visual processing can be approximated by a sequence of convolutions of the input scene with the difference-of-Gaussian (DoG) and the oriented Gabor filters. The filtered responses are unusually high for prominent edge locations in the image, and are uniformly similar across different natural image inputs. Furthermore, such a response follows a power law distribution. The aim of this thesis is to examine how these response properties could be utilized to the problem of salience detection. First, I identify a method to find the best threshold on the response activity (orientation energy) toward the detection of salient contours: compare the response distribution to a Gaussian distribution of equal variance. Second, I justify this comparison by providing an explanation under the framework of Suspicious Coincidence proposed by Barlow [1]. A connection is provided between perceived salience of contours and the neuronal goal of detecting suspiciousness, where salient contours are seen as affording suspicious coincidences by the visual system. Finally, the neural plausibility of such a salience detection mechanism is investigated, and the representational effciency is shown which could potentially explain why the human visual system can effortlessly detect salience.

Previous findings have suggested that auditive white noise benefits cognitive performance under certain circumstances. The primary purpose of the present pilot study was to explore the effects of visual white noise on verbal episodic memory performance in a normal participant population. Performance was assessed by an immediate free recall test. A secondary purpose was to explore whether participants` eye blink rates and/or temporal processing alters in different noise conditions. The findings of the present study suggest that visual white noise does not affect recall performance among normal participants. However, partially different memory systems and/or memorizing techniques might be used in different noise conditions. Furthermore, noise was not found to affect participants` blink rates or temporal processing.

Salient contour detection is an important lowlevel visual process in the human visual system, and has significance towards understanding higher visual and cognitive processes. Salience detection can be investigated by examining the visual cortical response to visual input. Visual response activity in the early stages of visual processing can be approximated by a sequence of convolutions of the input scene with the difference-of-Gaussian (DoG) and the oriented Gabor filters. The filtered responses are unusually high for prominent edge locations in the image, and are uniformly similar across different natural image inputs. Furthermore, such a response follows a power law distribution. The aim of this thesis is to examine how these response properties could be utilized to the problem of salience detection. First, I identify a method to find the best threshold on the response activity (orientation energy) toward the detection of salient contours: compare the response distribution to a Gaussian distribution of equal variance. Second, I justify this comparison by providing an explanation under the framework of Suspicious Coincidence proposed by Barlow [1]. A connection is provided between perceived salience of contours and the neuronal goal of detecting suspiciousness, where salient contours are seen as affording suspicious coincidences by the visual system. Finally, the neural plausibility of such a salience detection mechanism is investigated, and the representational effciency is shown which could potentially explain why the human visual system can effortlessly detect salience.

Based on the Lyapunov stability theorem, a methodology of designing an nth order adaptive integral variable structure derivative estimator (AIVSDE) is proposed in this thesis. The proposed derivative estimator not only is an improved version of the existing AIVSDE, but also can be used to estimate the nth derivative of a smooth signal which has continuous and bounded derivatives up to n+1. Analysis results show that adjusting some of the parameters can facilitate the derivative estimation of signals with higher frequency noise. The adaptive algorithm is incorporated in the estimation scheme for tracking the unknown upper bounded of the input signal as well as their's derivatives. The stability of the proposed derivative estimator is guaranteed, and the comparison between recently proposed derivative estimator of high-order sliding mode control and AIVSDE is also demonstrated.

This thesis aims to explore how the idea of American Dream is presented in White Noise, how the Dream is represented as ¡§American magic,¡¨ and how eventually it turns into ¡§American dread,¡¨ the ultimate American nightmare. In Chapter One, I provide a brief historical survey on the concept of the American Dream, the idea that mainly shaped the American nation in history. I turn to Jim Cullen¡¦s The American Dream: A Short History of an Idea That Shaped a Nation and Andrew Delbanco¡¦s The Real American Dream: A Meditation on Hope to explore how the idea of the American Dream changes through the course of American history as well as construct a historical background of the American Dream. Chapter Two explores how the American Dream in White Noise is exposed and transformed into what DeLillo terms in the novel as the ¡§American magic¡¨ via the novel¡¦s extreme emphasis on the issue of mass media, the operation of simulated magic. First, I briefly analyze the American Dream succeeded in White Noise based on my survey of the American Dream in the previous chapter. Reading DeLillo¡¦s ¡§American magic¡¨ as the simulated dream in White Noise in light of Baudrillard¡¦s theory of simulacra and simulation, I argue that White Noise is in fact a novel based on the critique of the American Dream due to the falsehood of the protagonists¡¦ American Dream televised through media and consumer culture. In Chapter Three, by recalling the novel¡¦s emphasis on the protagonists¡¦ fear of death, I aim to examine the true reason for such fatal fear. While many may read White Noise simply as a postmodern representation of man¡¦s uncontrollable natural fear of death, I examine the connection of this major theme of fear towards death to DeLillo¡¦s American magic and point out the possibility of American magic acting both as a cause and reinforcement of this fear as well as relating it to the larger issue of DeLillo¡¦s ¡§American dread¡¨ ¡Xa portrayal of the American Dream and magic brought to its extremity and stirred towards a possible apocalyptic end.

We consider two different problems in quantization theory. During the first part we discuss the so called Bennett's White Noise Hypothesis, introduced to study quantization errors of different schemes. Under this hypothesis, one assumes that the reconstruction errors of different channels can be considered as uniform, independent and identically distributed random variables. We prove that in the case of uniform quantization errors for frame expansions, this hypothesis is in fact false. Nevertheless, we also prove that in the case of fine quantization, the errors of different channels are asymptotically uncorrelated, validating, at least partially, results on the computation of the mean square error of reconstructions that were obtained through the assumption of Bennett's hypothesis. On the second part, we will introduced a new scalar quantization scheme, called a Beta Alpha Encoder. We analyze its robustness with respect to the quantizer imperfections. This scheme also induces a challenging dynamical system. We give partial results dealing with the ergodicity of this system.

In this thesis we use techniques from white noise analysis to study solutions of semilinear stochastic differential equations in a Hilbert space H: {dX[subscript]t = (AX[subscript]t + F(t,X[subscript]t)) dt + ơ(t,X[subscript]t) δB[subscript]t, t∈ (0,T], X[subscript]0 = ξ, where A is a generator of either a C[subscript]0-semigroup or an n-times integrated semigroup, and B is a cylindrical Wiener process. We then consider applications to forward interest rate models, such as in the Heath-Jarrow-Morton framework. We also reformulate a phenomenological model of the forward rate. / Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Science, 2009