Thesis (M. S.)--Architecture, Georgia Institute of Technology, 2008. / Committee Chair: Spuybroek, Lars; Committee Member: Al-Haddad, Tristan; Committee Member: Romm, Stuart.

Thesis (Ph.D.)--University of Adelaide, School of Chemistry and Physics, Discipline of Physics, 2006. / Includes author's previously published papers. "February 2006" Bibliography: p. [129]-140. Also available in print form.

Thesis (Ph. D.)--Oregon State University, 1997. / Typescript (photocopy). Includes bibliographical references (leaves 105-107). Also available on the World Wide Web.

Thesis (Ph.D)--Materials Science and Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Tannenbaum, Rina; Committee Member: Garmestani, Hamid; Committee Member: Jacob, Karl; Committee Member: Patterson, Tim; Committee Member: Singh, Preet. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Duke University, 2007. / Includes bibliographical references.

by John Pui-fai Sum. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 99-104). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Idea of SSOM --- p.3 / Chapter 1.3 --- Other Approaches --- p.3 / Chapter 1.4 --- Contribution of the Thesis --- p.4 / Chapter 1.5 --- Outline of Thesis --- p.5 / Chapter 2 --- Self-Organizing Map --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Algorithm of SOM --- p.8 / Chapter 2.3 --- Illustrative Example --- p.10 / Chapter 2.4 --- Property of SOM --- p.14 / Chapter 2.4.1 --- Convergence property --- p.14 / Chapter 2.4.2 --- Topological Order --- p.15 / Chapter 2.4.3 --- Objective Function of SOM --- p.15 / Chapter 2.5 --- Conclusion --- p.17 / Chapter 3 --- Algorithms for Soft Self-Organizing Map --- p.18 / Chapter 3.1 --- Competitive Learning and Soft Competitive Learning --- p.19 / Chapter 3.2 --- How does SOM generate ordered map? --- p.21 / Chapter 3.3 --- Algorithms of Soft SOM --- p.23 / Chapter 3.4 --- Simulation Results --- p.25 / Chapter 3.4.1 --- One dimensional map under uniform distribution --- p.25 / Chapter 3.4.2 --- One dimensional map under Gaussian distribution --- p.27 / Chapter 3.4.3 --- Two dimensional map in a unit square --- p.28 / Chapter 3.5 --- Conclusion --- p.30 / Chapter 4 --- Application to Uncover Vowel Relationship --- p.31 / Chapter 4.1 --- Experiment Set Up --- p.32 / Chapter 4.1.1 --- Network structure --- p.32 / Chapter 4.1.2 --- Training procedure --- p.32 / Chapter 4.1.3 --- Relationship Construction Scheme --- p.34 / Chapter 4.2 --- Results --- p.34 / Chapter 4.2.1 --- Hidden-unit labeling for SSOM2 --- p.34 / Chapter 4.2.2 --- Hidden-unit labeling for SOM --- p.35 / Chapter 4.3 --- Conclusion --- p.37 / Chapter 5 --- Application to vowel data transmission --- p.42 / Chapter 5.1 --- Introduction --- p.42 / Chapter 5.2 --- Simulation --- p.45 / Chapter 5.2.1 --- Setup --- p.45 / Chapter 5.2.2 --- Noise model and demodulation scheme --- p.46 / Chapter 5.2.3 --- Performance index --- p.46 / Chapter 5.2.4 --- Control experiment: random coding scheme --- p.46 / Chapter 5.3 --- Results --- p.47 / Chapter 5.3.1 --- Null channel noise (σ = 0) --- p.47 / Chapter 5.3.2 --- Small channel noise (0 ≤ σ ≤1) --- p.49 / Chapter 5.3.3 --- Large channel noise (1 ≤σ ≤7) --- p.49 / Chapter 5.3.4 --- Very large channel noise (σ > 7) --- p.49 / Chapter 5.4 --- Conclusion --- p.50 / Chapter 6 --- Convergence Analysis --- p.53 / Chapter 6.1 --- Kushner and Clark Lemma --- p.53 / Chapter 6.2 --- Condition for the Convergence of Jou's Algorithm --- p.54 / Chapter 6.3 --- Alternative Proof on the Convergence of Competitive Learning --- p.56 / Chapter 6.4 --- Convergence of Soft SOM --- p.58 / Chapter 6.5 --- Convergence of SOM --- p.60 / Chapter 7 --- Conclusion --- p.61 / Chapter 7.1 --- Limitations of SSOM --- p.62 / Chapter 7.2 --- Further Research --- p.63 / Chapter A --- Proof of Corollary1 --- p.65 / Chapter A.l --- Mean Average Update --- p.66 / Chapter A.2 --- Case 1: Uniform Distribution --- p.68 / Chapter A.3 --- Case 2: Logconcave Distribution --- p.70 / Chapter A.4 --- Case 3: Loglinear Distribution --- p.72 / Chapter B --- Different Senses of neighborhood --- p.79 / Chapter B.l --- Static neighborhood: Kohonen's sense --- p.79 / Chapter B.2 --- Dynamic neighborhood --- p.80 / Chapter B.2.1 --- Mou-Yeung Definition --- p.80 / Chapter B.2.2 --- Martinetz et al. Definition --- p.81 / Chapter B.2.3 --- Tsao-Bezdek-Pal Definition --- p.81 / Chapter B.3 --- Example --- p.82 / Chapter B.4 --- Discussion --- p.84 / Chapter C --- Supplementary to Chapter4 --- p.86 / Chapter D --- Quadrature Amplitude Modulation --- p.92 / Chapter D.l --- Amplitude Modulation --- p.92 / Chapter D.2 --- QAM --- p.93 / Bibliography --- p.99

Wong, Wing Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 76-79). / Abstracts in English and Chinese. / Contents --- p.i / Acknowledgments --- p.iii / Abstract --- p.iv / Abbreviations and Acronyms --- p.vii / Publications Originated from the Work of this Thesis --- p.ix / Chapter Part I: --- "Self-Assemblyy Stability Quantification, Controlled Molecular Switching, and Sensing Properties of an Anthracene-Containing Dynamic [2]Rotaxane" / Chapter Chapter 1 - --- Introduction / Chapter 1.1 --- Definition of Rotaxane --- p.2 / Chapter 1.2 --- Dynamic Covalent Chemistry in Rotaxane Synthesis --- p.5 / Chapter 1.3 --- Thermodynamic Template --- p.6 / Chapter 1.4 --- Molecular Sensing Properties in Rotaxane --- p.10 / Chapter 1.5 --- Examples --- p.13 / Chapter Chapter 2 - --- Anthracene-Containing Dynamic [2]Rotaxane / Chapter 2.1 --- Background --- p.17 / Chapter 2.2 --- Modification and Design of Dynamic [2]Rotaxane --- p.18 / Chapter 2.3 --- Self-Assembly of Rotaxane and Synthesis of Components --- p.19 / Chapter 2.4 --- Characterization / Chapter 2.4.1 --- 1H NMR Spectroscopy --- p.21 / Chapter 2.4.2 --- 13C NMR Spectroscopy --- p.23 / Chapter 2.4.3 --- Mass Spectrometry --- p.24 / Chapter 2.4.4 --- X-Ray Crystallography --- p.25 / Chapter 2.4.5 --- UV/Visible Absorption and Fluorescence Spectroscopies --- p.26 / Chapter 2.5 --- Effect of External Stimuli / Chapter 2.5.1 --- Addition of Water --- p.29 / Chapter 2.5.2 --- Addition of Acid --- p.33 / Chapter 2.5.3 --- Addition of Salts --- p.38 / Chapter 2.5.4 --- Addition of Amines --- p.40 / Chapter 2.6 --- Conclusions --- p.43 / Chapter Part II: --- Substituent Effect in Imine-Containing Molecular Tweezers / Chapter Chapter 3 - --- Molecular Tweezers / Chapter 3.1 --- Introduction --- p.46 / Chapter 3.2 --- Synthesis --- p.48 / Chapter 3.3 --- Characterization of Molecular Tweezers / Chapter 3.3.1 --- 1H NMR Spectroscopy --- p.49 / Chapter 3.3.2 --- Mass Spectrometry --- p.51 / Chapter 3.4 --- Characterization of Molecular Tweezers / Chapter 3.4.1 --- 1H NMR Spectroscopy --- p.51 / Chapter 3.4.2 --- X-Ray Crystallography --- p.59 / Chapter 3.4.3 --- Mass Spectrometry --- p.60 / Chapter 3.4.4 --- UV/Visible Absorption Spectroscopy --- p.61 / Chapter 3.5 --- Conclusions --- p.63 / Chapter Chapter 4 - --- Experimental Procedures / Chapter 4.1 --- General Information --- p.64 / Chapter 4.2 --- General Synthetic Procedures for Molecular Tweezers (34-40) --- p.65 / Chapter 4.3 --- Experimental Procedures --- p.65 / Chapter 4.4 --- Determination of Binding Constant K --- p.73 / References --- p.76 / Appendix / List of Spectra --- p.A-l / List of Crystal Data --- p.A-2

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Coherence of self-concept refers to the ability to stabilize on a clear set of views about oneself. This aspect of self-structure is closely linked self-esteem, and similar evidence in emotion research suggests an intricate connection between the self-system and emotion. Evidence suggests that emotions of seemingly opposing valence such as happy and sad can co-occur (i.e., mixed emotion). This study validated a new set of emotional stimuli particularly to elicit mixed emotion and used these stimuli with a mouse task that allowed participants to report positive and negative emotions simultaneously. The study examined possible individual differences in discrete emotional response associated with self-esteem as well as a possible connection between selfconcept coherence and a differential ability to harbor mixed emotions; specifically that individuals with high coherence in self-concept would tend to disambiguate their emotional response, but those with low coherence would be more susceptible to cooccurring positive and negative emotion. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Iterative Learning Control (ILC) iterates with a real world control system repeatedly performing the same task. It adjusts the control action based on error history from the previous iteration, aiming to converge to zero tracking error. ILC has been widely used in various applications due to its high precision in trajectory tracking, e.g. semiconductor manufacturing sensors that repeatedly perform scanning maneuvers. Designing effective feedback controllers for non-minimum phase (NMP) systems can be challenging. Applying Iterative Learning Control (ILC) to NMP systems is particularly problematic. Asking for zero error at sample times usually involves inverting the control system. However, the inverse process is unstable when the system has NMP zeros. The control action will grow exponentially every time step, and the error between time steps also grows exponentially. If there are NMP zeros on the negative real axis, the control action will alternate its sign every time step. ILC must be digital to use previous run data to improve the tracking error in the current run. There are two kinds of NMP digital systems, ones having intrinsic NMP zeros as images of continuous time NMP zeros, and NMP sampling zeros introduced by discretization. Two ILC design methods have been investigated in this thesis to handle NMP sampling zeros, producing zero tracking error at addressed sample times: (1) One can simply start asking for zero error after a few initial time steps, like using multiple zero order holds for the first addressed time step only (2) Or increase the sample rate, ask for zero error at the original rate, making two or more zero order holds per addressed time step. The internal instability can be manifested by the singular value decomposition of the input-output matrix. Non-minimum phase systems have particularly small singular values which are related to the NMP zeros. The aim is to eliminate these anomalous singular values. However, when applying the second approach, there are cases that the original anomalous singular values are gone, but some new anomalous singular values appear in the system matrix that cause difficulties to the inverse problem. Not asking for zero error for a small number of initial addressed time steps is shown to eliminate all anomalous singular values. This suggests that a more accurate statement of the second approach is: using multiple zero order holds per addressed time step, and eliminating a few initial addressed time steps if there are new anomalous singular values. We also extend the use of these methods to systems having intrinsic NMP zeros. By modifying ILC laws to perform pole-zero cancellation inside the unit circle, we observe that all of the rules for sampling zeros are effective for intrinsic zeros. Hence, one can now achieve convergence to zero tracking error at addressed time steps in ILC of NMP systems with a well behaved control action. In addition, this thesis studies the robustness of the two approaches along with several other candidate approaches with respect to model parameter uncertainty. Three classes of ILC laws are used. Both approaches show great robustness. Quadratic cost ILC is seen to have substantially better robustness to parameter uncertainty than the other laws.