Thesis (Ph. D.)--University of Alabama at Birmingham, 2008. / Title from PDF title page (viewed Mar. 30, 2010). Additional advisors: Franklin R. Amthor, James E. Cox, Timothy J. Gawne, Rosalyn E. Weller. Includes bibliographical references (p. 104-114).

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1977 / Bibliography : p. 248-254. / Ph. D. / Ph. D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

published_or_final_version / Psychology / Master / Master of Philosophy

It is well established that exposure to virtual motion environments (VME) can elicit postural instability (PI) in addition to motion sickness (MS). While research has found sex differences in motion sickness, the results of experimental studies are equivocal regarding these differences, and previous studies utilizing VME have failed to address the factor of sex differences in terms of hormonal fluctuations, which may also be instrumental in behavioral responses to VME, such as PI. The intent of this investigation was to determine whether exposure to VME, during various phases of the menstrual cycle (premenstrual, permenstrual, ovulation) would reveal sex differences in MS and PI during some phases, but not others. The first experiment involved men and women completing Daily Living Logs for a period of 40 days to provide a baseline for any sex differences (and for women, menstrual phase differences) in motion related activity and symptomatology. The second experiment involved 24 participants (6 men) viewing a rotating Archimedes spiral for a period of twenty minutes. Exposures were timed to place each woman in three phases of her menstrual cycle; men were exposed by yoking their exposure time to a female counterpart. Multiple measures of PI and MS were recorded before, after and during exposure. Results of the first experiment found no significant effects of sex or phase upon symptomatology, revealing no support for the theory of a reporting bias as influencing sex differences in MS or PI elicited in the laboratory. The second experiment found no significant effect of sex of phase upon any of the PI measures, but found significant interaction effects of sequence and phase, as well as sequence and sex, upon reported magnitude ratings of illusory self-motion perception. There were also significant effects of sex found upon measures of MS, with women reporting more discomfort to exposure to motion stimulation, as compared to men. There were no significant effects of phase upon any of the MS measures. While these findings show no support for a reporting bias influencing the sex differences found experimentally induced MS, it yields no evidence to support a hormonal influence on these differences.

Thesis. 1977. Ph.D.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERO / Vita. / Bibliography : leaves 323-333. / by Greg L. Zacharias. / Ph.D.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography : leaves 53-54. / by Martin Peter Moser, Jr. / M.S.

Lee Kwok Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 144-148). / Text in English; abstracts in English and Chinese. / Lee Kwok Wai. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgements --- p.iii / Table of Content --- p.iv / List of Figures & Tables --- p.viii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Motion Symmetry and its Application in Classification of Articulated Motions --- p.5 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.1.1 --- Motivation & Related Works --- p.7 / Chapter 2.1.2 --- Transformation Matrix for a Rigid Motion --- p.8 / Chapter 2.2 --- Review of Motion Estimation --- p.9 / Chapter 2.2.1 --- Motion Estimation & Motion Fields --- p.9 / Chapter 2.2.2 --- Motion Field Construction from Optical Flow --- p.10 / Chapter 2.2.3 --- Motion Field Construction from Image Matching --- p.12 / Chapter 2.3 --- Motion Symmetry --- p.13 / Chapter 2.3.1 --- Problem Definition --- p.13 / Chapter 2.3.2 --- Definitions of Transformation Symmetry & Anti- symmetry --- p.14 / Chapter 2.3.2.1 --- Translation Symmetry --- p.15 / Chapter 2.3.2.2 --- Translation Anti-symmetry --- p.16 / Chapter 2.3.2.3 --- Rotation Symmetry --- p.17 / Chapter 2.3.2.4 --- Rotation Anti-symmetry --- p.18 / Chapter 2.3.2.5 --- Scaling Symmetry … --- p.18 / Chapter 2.3.2.6 --- Scaling Anti-symmetry --- p.19 / Chapter 2.3.3 --- Transformation Quasi-symmetry & Quasi-anti- symmetry --- p.19 / Chapter 2.3.4 --- Symmetric Transform of a Transformation --- p.19 / Chapter 2.3.5 --- Symmetric Motions & Periodic Symmetric Motions --- p.20 / Chapter 2.3.6 --- Transformation Vector Fields of Symmetric Motions --- p.20 / Chapter 2.4 --- Detection of Motion Symmetry --- p.23 / Chapter 2.4.1 --- Model-based Motion Parameter Analysis --- p.24 / Chapter 2.4.2 --- Transformation Matrices Analysis --- p.25 / Chapter 2.4.3 --- Simultaneous Resultant Transformation Matrix Analysis --- p.31 / Chapter 2.4.4 --- Motion Symmetry as a Continuous Feature --- p.38 / Chapter 2.5 --- Illustrations & Results … --- p.39 / Chapter 2.5.1 --- Experiment 1: Randomly Generated Data --- p.39 / Chapter 2.5.2 --- Experiment 2: Symmetry Axis for a 3D object --- p.41 / Chapter 2.6 --- Summary & Discussion --- p.44 / Chapter 2.7 --- Appendices --- p.47 / Chapter 2.7.1 --- Appendix 1: Reflection of a Point about a Line --- p.47 / Chapter 2.7.2 --- Appendix 2: Symmetric Transform of a Transformation --- p.49 / Chapter Chapter 3 --- Motion Representation by Feedforward Neural Networks --- p.53 / Chapter 3.1 --- Introduction --- p.54 / Chapter 3.2 --- Motion Modeling in Animation --- p.57 / Chapter 3.2.1 --- Parameterized Motion Representation --- p.58 / Chapter 3.2.2 --- Problems of Motion Analysis --- p.62 / Chapter 3.3 --- Multi-value Regression by Feedforward Neural Networks --- p.66 / Chapter 3.3.1 --- Review of Multi-value Regression Methods --- p.66 / Chapter 3.3.2 --- Problem Definition --- p.68 / Chapter 3.3.3 --- Proposed Methods --- p.69 / Chapter 3.3.3.1 --- Modular Networks with Verification Module --- p.69 / Chapter (a) --- Validation by Decoding --- p.70 / Chapter (b) --- Validation by inverse mapping --- p.71 / Chapter 3.3.3.2 --- Partition Algorithm --- p.73 / Chapter 3.4 --- Illustration & Results --- p.76 / Chapter 3.4.1 --- Cylindrical Spiral Function --- p.76 / Chapter 3.4.2 --- Elongated Cylindrical Spiral Function --- p.79 / Chapter 3.4.3 --- Cylindrical Spiral Surface --- p.83 / Chapter 3.4.4 --- S-curve Data --- p.87 / Chapter 3.4.5 --- Inverse Sine function --- p.89 / Chapter 3.5 --- Motion Analysis … --- p.91 / Chapter 3.6 --- Summary & Discussion --- p.94 / Chapter Chapter 4 --- Motion Representation by Recurrent Neural Networks --- p.98 / Chapter 4.1 --- Introduction --- p.99 / Chapter 4.1.1 --- Recurrent Neural Networks (RNNs) --- p.99 / Chapter 4.1.2 --- Fully & Partially Recurrent Neural Networks --- p.101 / Chapter 4.1.3 --- Back-propagation Training Algorithm --- p.105 / Chapter 4.2 --- Sequence Encoding by Recurrent Neural Networks --- p.106 / Chapter 4.2.1 --- Random Binary Sequence --- p.107 / Chapter 4.2.2 --- Angular Positions of Clock Needles --- p.108 / Chapter 4.2.3 --- Absolute Positions of Clock Needles´ةTips --- p.109 / Chapter 4.2.4 --- Henon Time Series --- p.111 / Chapter 4.2.5 --- Ikeda Time Series … --- p.112 / Chapter 4.2.6 --- Single-Input-Single-Output (SISO) Non-linear System --- p.114 / Chapter 4.2.7 --- Circular Trajectory --- p.115 / Chapter 4.2.8 --- Number Trajectories --- p.118 / Chapter 4.3 --- Animation Generation by Recurrent Neural Networks --- p.123 / Chapter 4.3.1 --- Storage & Generation of Animations --- p.127 / Chapter 4.3.2 --- Interpolation between two Motion Segments --- p.127 / Chapter 4.4 --- Motion Analysis by Recurrent Neural Networks --- p.129 / Chapter 4.5 --- Experimental Results --- p.130 / Chapter 4.5.1 --- Network Training --- p.130 / Chapter 4.5.2 --- Motion Interpolation --- p.134 / Chapter 4.5.3 --- Motion Recognition --- p.135 / Chapter 4.6 --- Summary & Discussion --- p.138 / Chapter Chapter 5 --- Conclusion --- p.141 / References --- p.144

No description available.

This thesis presents an algorithm that estimates motion in image sequence using wavelet transform. The motion detection is performed under unfavorable conditions of background movement, change of brightness, and noise. The algorithm is tolerant to brightness changes, noise, and small movement in the background. The false alarm rate of motion detection is reduced as compared to standard techniques. Using wavelet transform is numerically efficient and the storage requirements are significantly reduced. Also, a more accurate motion detection is achieved. Tests performed on real images show the effectiveness of this algorithm. Practical results of motion detection are presented. / Graduation date: 2003

This paper presents some mathematical results concerning the measurement of motion of contours. A fundamental problem of motion measurement in general is that the velocity field is not determined uniquely from the changing intensity patterns. Recently Hildreth & Ullman have studied a solution to this problem based on an Extremum Principle [Hildreth (1983), Ullman & Hildreth (1983)]. That is, they formulate the measurement of motion as the computation of the smoothest velocity field consistent with the changing contour. We analyse this Extremum principle and prove that it is closely related to a matching scheme for motion measurement which matches points on the moving contour that have similar tangent vectors. We then derive necessary and sufficient conditions for the principle to yield the correct velocity field. These results have possible implications for the design of computer vision systems, and for the study of human vision.