Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1976 / Vita. / Includes bibliographical references. / Ph. D. / Ph. D. Massachusetts Institute of Technology, Department of Chemistry

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Yeung Shun-hin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 52-54). / Abstracts in English and Chinese. / TITLE PAGE --- p.i / THESIS COMMITTEE --- p.ii / ABSTRACT (ENGLISH) --- p.iii / ABSTRACT (CHINESE) --- p.iv / ACKNOWLEDGEMENTS --- p.v / TABLES OF CONTENTS --- p.vi / LIST OF FIGURES --- p.viii / LIST OF TABLES --- p.x / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Computer-controlled Data Acquisition and Frequency Calibration System for a Ti: sapphire laser spectrometer --- p.3 / Chapter Section 2A --- Motivation and Overview --- p.3 / Chapter Section 2B --- The Hardware --- p.5 / Chapter Section 2C --- The Program --- p.12 / Chapter Section 2D --- Summary --- p.27 / Chapter Chapter 3 --- Cavity Enhanced Absorption Spectroscopy Using Phase-Sensitive Detection --- p.28 / Chapter Section 3A --- Motivation --- p.28 / Chapter Section 3B --- Cavity ring-down technique: the background --- p.29 / Chapter Section 3C --- Cavity enhanced absorption spectroscopy: a historical review --- p.34 / Chapter Section 3D --- Experimental Apparatus --- p.37 / Chapter Section 3E --- Results of Performance tests --- p.41 / Chapter Section 3F --- Applications --- p.45 / Chapter Section 3G --- Summary --- p.49 / Chapter Chapter 4 --- Concluding Remarks --- p.50 / REFERENCES --- p.52

Yan, Lei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 50-52). / Abstracts in English and Chinese. / Title Page --- p.I / Thesis Committee --- p.II / Abstract --- p.III / 摘要 --- p.IV / Acknowledgements --- p.V / Table of Contents --- p.1 / Chapter Chapter I. --- Introduction --- p.3 / Chapter Chapter II. --- Properties of Hydrogen --- p.6 / Chapter A. --- Spectroscopic Properties of Hydrogen Molecules --- p.6 / Chapter B. --- Properties of Solid Hydrogen --- p.9 / Chapter C. --- Solid Hydrogen as a Matrix Material --- p.12 / Chapter Chapter III. --- Experimental Apparatus --- p.16 / Chapter A. --- Fourier Transformer Spectrometer --- p.16 / Chapter B. --- Cryostat and Sample Cell --- p.23 / Chapter C. --- Gas Handling System and ortho-para Converter --- p.25 / Chapter D. --- Determination of o-H2 Concentration --- p.29 / Chapter E. --- Preparation of Matrix-isolated Species --- p.32 / Chapter Chapter IV. --- Spectroscopic studies of CO in solid hydrogen --- p.34 / Chapter A. --- "Matrix Isolation Spectroscopy of CO, brief overview" --- p.34 / Chapter B. --- Observation and Preliminary Analysis --- p.35 / Chapter i. --- CO in Normal H2 Matrix --- p.35 / Chapter ii. --- CO in Hydrogen Matrix of 50/50 Mixture --- p.37 / Chapter iii. --- CO in Para-enriched Solid Matrix --- p.39 / Chapter C. --- Discussion --- p.46 / Chapter Chapter V. --- Concluding Remarks --- p.48 / Reference --- p.50

Thesis--St. Catharine's College, Cambridge. / Typescript (carbon) with ms. corrections. Without thesis statement. Appendices 1-3 by N. Sheppard and G.B.B.M. Sutherland. Bibliography: leaves 209-216.

Thesis (Ph. D.)--University of Michigan, 1935. / Cover title. By Paul F. Bartunek and E.F. Barker. "Reprinted from the Physical review, vol. 48, no. 6, September 15, 1935."

Thesis (Ph. D.)--Institute of Paper Chemistry, 1974. / Includes bibliographical references (p. 129-130).

No description available.

The tobacco growing area is an important aspect for the consistency of cigarette aroma and the control of cigarette quality. The fragrance of tobacco leaves would be different for climates and planting environments, such as soil and rainfalls. Accurately discriminating tobacco growing areas is very important to maintain the specifications of cigarettes. In this thesis, the relationship between tobacco near-infrared (NIR) spectra and growing areas is studied. Soft computing models and statistical classifiers are established, and the performance of the developed classifiers are compared in the prediction accuracy and in evaluations derived from confusion matrix. An artificial neural network (ANN) classifier and a statistical model are firstly developed. The best prediction accuracy of ANN model reaches to 79.3% in 226 training samples and 78.7% in 66 testing samples, respectively, which are 2.2% and 4.5% higher than the best results of the conventional statistical model in training (77.1%) and in testing (74.2%), respectively. A support vector machine (SVM) model is proposed to investigate the characteristics of growing areas based on risk error minimum, and produces a higher classification accuracy than ANN model does, demonstrating the effectiveness and robustness of SVM model. In addition, a genetic algorithm (GA) optimized SVM (GA-SVM) model is proposed for taking the influence of the interaction of individual inputs on the performance of classifiers into account. With the application of GA, the sensitive input subset is identified and used to discrimination models. The simulation results demonstrate that the GA-SVM model has the best performance among the other developed models, and the model complexity is simplified, which is shown by requiring fewer inputs to achieve the equivalent prediction accuracy. The GA-SVM classifier is preferred for solving multi-category problems. / Prof. Simon X. Yang