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Ocular Vergence Response Over Anaglyphic Stereoscopic Videos, Brian Daugherty
Ocular Vergence Response Over Anaglyphic Stereoscopic Videos, Brian Daugherty
All Theses
The effect of anaglyphic stereographic stimuli on ocular vergence response is examined. An experiment is performed comparing ocular vergence response induced by anaglyphic stereographic display versus standard monoscopic display. Two visualization tools, synchronized three-dimensional scanpath playback and real-time dynamic heatmap generation,
are developed and used to subjectively support the quantitative analysis of ocular disparity. The results of a one-way ANOVA indicate that there is a highly significant effect of anaglyphic stereoscopic display on ocular vergence for a majority of subjects although consistency of vergence response is difficult to predict.
Convergence And The Lebesgue Integral, Ryan Vail Thomas
Convergence And The Lebesgue Integral, Ryan Vail Thomas
MSU Graduate Theses
In this paper, we examine the theory of integration of functions of real variables. Background information in measure theory and convergence is provided and several examples are considered. We compare Riemann and Lebesgue integration and develop several important theorems. In particular, the Monotone Convergence Theorem and Dominated Convergence Theorem are considered under both pointwise convergence and convergence in measure.
Almost Everywhere Convergence Of Weighted Ergodic Averages, Christopher Michael Wedrychowicz
Almost Everywhere Convergence Of Weighted Ergodic Averages, Christopher Michael Wedrychowicz
Legacy Theses & Dissertations (2009 - 2024)
Let $(X,\mathcal{B},\lambda,T)$ be a dynamical system and $\mbox{Log}_{(n)}x$
Almost Everywhere Convergence Of Convolution Measures, Anna K. Savvopoulou
Almost Everywhere Convergence Of Convolution Measures, Anna K. Savvopoulou
Legacy Theses & Dissertations (2009 - 2024)
Let $(X,\mathcal{B},m,\tau)$ be a dynamical system with $(X,\mathcal{B},m)$ a probability space and $\tau$ a measurable, invertible, measure preserving transformation. The present thesis deals with the almost everywhere convergence in $\mbox{L}^1(X)$ of a sequence of operators of weighted averages. Almost everywhere convergence follows once we obtain an appropriate maximal estimate and once we provide a dense class where convergence holds almost everywhere. The weights are convolution products of members of a sequence of probability measures $\{\nu_i\}$ on $\mathbb{Z}$. In the last section, we also prove a variation inequality for this type of sequence operators.