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Manzoor, ShahidaChaos Theory and Robert Wilson: A Critical Analysis of Wilson’s Visual Arts and Theatrical Performances
Doctor of Philosophy (PhD), Ohio University, 2003, Comparative Arts (Fine Arts)

This dissertation explores the formal elements of Robert Wilson’s art, with a focus on two in particular: time and space, through the methodology of Chaos Theory. Although this theory is widely practiced by physicists and mathematicians, it can be utilized with other disciplines, in this case visual arts and theater. By unfolding the complex layering of space and time in Wilson’s art, it is possible to see the hidden reality behind these artifacts. The study reveals that by applying this scientific method to the visual arts and theater, one can best understand the nonlinear and fragmented forms of Wilson's art. Moreover, the study demonstrates that time and space are Wilson's primary structuring tools and are bound together in a self-renewing process. Each image is not the death of time and space but its own simulation, individual and singular. The study identifies some of the parallels between Wilson’s art, Chaos Theory and Postmodernism, specifically, their orientation towards chance and indeterminacy and their shared idea that nature and reality are unpredictable, because life itself is open to the unexpected and therefore always fresh and new. The dissertation ultimately seeks to promote communication across disciplines.

Committee:

Charles Buchanan (Advisor)

Subjects:

Fine Arts

Keywords:

Robert Wilson; Chaos Theory, Robert Wilson and Postmodernism; Robert Wilson's Visual Arts and Theatrical Performances; Chaos Theory and Robert Wilson; A letter for Queen Victoria: An Analysis of Space and Time

Krcelic, Khristine MChaos and Dynamical Systems
Master of Science in Mathematics, Youngstown State University, 2012, Department of Mathematics and Statistics
Throughout the ages, mathematics has been evolving and creating new branches. In the middle to late twentieth century, a new branch formed: chaos. Chaos is the study of dynamical systems that vary greatly with respect to initial conditions. The slightest change in an initial condition, a seemingly unnoticeable change, can yield a drastically different result if the system is chaotic. Hence the common term relating to chaos theory, "the butterfly effect". Something as minute as the flap of a butterfly's wings could spawn a natural disaster half-way across the world. This thesis provides an insight to chaos from both a pure and an applied mathematician's point of view.

Committee:

Eric Wingler, PhD (Advisor); Zbigniew Piotrowski, PhD (Committee Member); Jamal Tartir, PhD (Committee Member)

Subjects:

Applied Mathematics; Mathematics

Keywords:

chaos theory; dynamical systems; butterfly effect

DeBonis, Joseph AlexStrange Houses
PhD, University of Cincinnati, 2006, Arts and Sciences : English and Comparative Literature
This dissertation, Strange Houses, is a collection of original short stories by the author, Joseph Alex DeBonis. The stories engage notions about home, family, estrangement, and alienation. Since many of the stories deal with family and marital relations, homes feature prominently in the action and as settings. Often characters are estranged or exiled, and their obsessions with having normal families and/or stable lives drive them to construct elaborate fantasies in which they are included, loved, and part of something enduring that is larger than themselves. The dissertation also includes a critical paper, “A Butterfly, a Cannonball, and a Sneeze: Notions of Chaos Theory in Cormac McCarthy’s All The Pretty Horses and Thomas Pynchon’s The Crying of Lot 49.” In this essay, I argue that the novel All The Pretty Horses grapples with a sense of freedom that is rife with ambiguity and demonstrates McCarthy’s engagement with chaos theory. The essay shows how Thomas Pynchon, in The Crying of Lot 49, exhibits similar concerns with the dynamic interaction of order and disorder. Though Horses has realistic details and does not appear to engage chaos theory in as obvious a way as The Crying of Lot 49 does, McCarthy’s novel can be profitably read through the lens of chaos theory.

Committee:

Michael Griffith (Advisor)

Subjects:

Literature, American

Keywords:

short fiction; chaos theory; narrative; American literature

Ghosh Dastidar, SamanwoyModels of EEG data mining and classification in temporal lobe epilepsy: wavelet-chaos-neural network methodology and spiking neural networks
Doctor of Philosophy, The Ohio State University, 2007, Biomedical Engineering
A multi-paradigm approach integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks is developed for EEG-based epilepsy diagnosis and seizure detection. This research challenges the assumption that the EEG represents the dynamics of the entire brain as a unified system. It is postulated that the sub-bands yield more accurate information about constituent neuronal activities underlying the EEG. Consequently, certain changes in EEGs not evident in the original full-spectrum EEG may be amplified when each sub-band is analyzed separately. A novel wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma sub-bands of EEGs for detection of seizure and epilepsy. The methodology is applied to three different groups of EEGs: healthy subjects, epileptic subjects during a seizure-free interval (interictal), and epileptic subjects during a seizure (ictal). Two potential markers of abnormality quantifying the non-linear chaotic EEG dynamics are discovered: the correlation dimension and largest Lyapunov exponent. A novel wavelet-chaos-neural network methodology is developed for EEG classification. Along with the aforementioned two parameters, the standard deviation (quantifying the signal variance) is employed for EEG representation. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy (96.7%). To increase the robustness of classification, a novel principal component analysis-enhanced cosine radial basis function neural network classifier is developed. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network employed in the second stage significantly. The new classifier is as accurate as LMBPNN and is twice as robust. Next, biologically realistic artificial neural networks are developed to reach the next milestone in artificial intelligence. First, an efficient spiking neural network (SNN) model is presented using three training algorithms: SpikeProp, QuickProp, and RProp. Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy. Next, a new Multi-Spiking Neural Network (MuSpiNN) and supervised learning algorithm (Multi-SpikeProp) are developed. Finally, the models are applied to the epilepsy and seizure detection problems to achieve high classification accuracies.

Committee:

Hojjat Adeli (Advisor)

Keywords:

Temporal Lobe Epilepsy; Electroencephalogram (EEG); EEG Classification; Epilepsy Diagnosis; Seizure Detection; Wavelet Transform; Chaos Theory; Artificial Neural Networks; Spiking Neural Networks; Principal Component Analysis; Cosine Radial Basis Function