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Thesis (Ph. D.)--Ohio University, March, 1994. / Title from PDF t.p.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 174-193). Also available via the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (January 11, 2007) Includes bibliographical references.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 124-133). Also available in electronic version.

Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal detection. The algorithm leverages kernel methods to inflate SNR of the data and enhance the noise rejection capabilities of the traditional cross-correlation. The goal of the algorithm is to achieve detection for signals past the limits of those of the matched filter and the cross-correlation in the presence of white and colored noise. To evaluate the effectiveness of the correlation algorithm, Monte Carlo simulations are performed to determine the performance in the context of different types of noise. The performance of the algorithm will be compared against the cross-correlation and the matched filter. / Master of Science / As society advances, the tools we rely on become increasingly more intricate. Pivotal to the development of these systems is the algorithms used to process the data they collect. Particularly crucial to the field of signal processing, weak signal detection is focused on the processing of barely comprehensible data in the context of powerful noise. In recent years, advancements in weak signal detection have focused on pushing the theoretical limits of signal discernibility, especially when heavily obscured by noise. Leveraging the power of machine learning, certain AI algorithms have showcased promise in the detection of weak signals. It has yet to be seen if a foundational principle of AI called a kernel transform can be applied to classic signal detection theory to increase detection performance. This thesis will propose a kernel based detection algorithm for weak signal detection and the performance of the algorithm will be compared against previously established theory. New breakthroughs in detection algorithms facilitate improvements in active and passive sonar, medical devices and even the finance sector.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

by Fei Yang. / Title also in Chinese characters. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 92-96). / ACKNOWLEDGEMENTS --- p.i / ABSTRACT --- p.ii / TABLE OF CONTENT --- p.iii / Chapter CHAPTER I. --- INTRODUCTION --- p.1 / Chapter §1.1 --- The Background of Pulsed Laser Ablation --- p.1 / Chapter §1.2 --- Properties of PZT --- p.3 / Chapter §1.3 --- Objectives of This Thesis --- p.7 / Chapter CHAPTER II. --- INTERACTION OF PULSED LASER RADIATION WITH A SOLID TARGET --- p.8 / Chapter §2.1 --- Introduction --- p.8 / Chapter §2.2 --- Absorption of Laser Radiation by the Target --- p.9 / Chapter §2.3 --- Absorption of Laser Radiation by the Plume --- p.11 / Chapter §2.4 --- Ablation Depth and Ablation Rate --- p.12 / Chapter §2.5 --- Formation and Evolution of the Plume --- p.14 / Chapter (a) --- Small-time Solution (c´Øt ζ) --- p.15 / Chapter (b) --- Large-time Solution (c´Øt ζ) --- p.15 / Chapter §2.6 --- Piezoelectric Signal --- p.17 / Chapter §2.7 --- Angular Distributions --- p.21 / Chapter §2.8 --- Target Surface Temperature --- p.26 / Chapter CHAPTER III. --- EXPERIMENTAL TECHNIQUES --- p.27 / Chapter §3.1 --- Pulsed Laser System --- p.27 / Chapter §3.2 --- Setup for Piezoelectric Detection of Laser Pulse --- p.28 / Chapter §3.3 --- Setup for Optical Measurement of Laser Plasma --- p.30 / Chapter §3.4 --- Setup for Ion Detection --- p.32 / Chapter §3.5 --- Sample Preparation --- p.35 / Chapter CHAPTER IV. --- PIEZOELECTRIC DETECTION OF LASER ABLATION --- p.38 / Chapter §4.1 --- Laser Ablation of a Piezoelectric Target --- p.38 / Chapter §4.1.1 --- Pyroelectric Effect --- p.38 / Chapter §4.1.2 --- Laser-induced Piezoelectric Signal (LIPS) --- p.39 / Chapter §4.2 --- Propagation of LIPS --- p.41 / Chapter §4.3 --- Laser Ablation Threshold --- p.45 / Chapter §4.4 --- Ablation Rate Measurement by LIPS --- p.47 / Chapter §4.5 --- Temperature Dependence of LIPS --- p.53 / Chapter §4.5.1 --- The Polarization Measurement --- p.53 / Chapter §4.5.2 --- The Piezoelectric Coefficient --- p.56 / Chapter CHAPTER V. --- STUDIES OF LASER PLASMA BY FARADAY PROBE METHOD --- p.59 / Chapter §5.1 --- Angular Distribution of The Plume Density --- p.59 / Chapter §5.2 --- The Plume Ionization Fraction --- p.64 / Chapter §5.3 --- The Plume Velocity Distribution and The Plume Temperature --- p.72 / Chapter §5.4 --- The Target Surface Temperature --- p.82 / Chapter §5.5 --- Plume Orientation Determination --- p.85 / Chapter CHAPTER VI. --- OVERALL CONCLUSION --- p.90 / REFERENCES --- p.92

This research examines an ordered perception rater model, an extension of the equal perception signal detection theory (SDT) latent class rater model. The expectation-maximization algorithm and the Newton-Raphson algorithm are used to estimate parameters. Four simulation studies are conducted to answer three research questions. Simulation studies 1 and 2 fit correct models to the data. Simulation study 1 generates one hundred data sets from the equal perception rater model, both with fully-crossed design and BIB design, and both without and with rater effects, and fits the equal perception model. Parameter recovery is excellent for fully-crossed design and reasonable for BIB design, and all rater effects are detected. Simulation study 2 generates one hundred simulated data sets from the ordered perception model, both with fully-crossed design and BIB design, and both without and with rater effects, and fits the ordered perception rater model. Although parameter recovery is biased for some parameters in the BIB design, all rater effects are recovered. Simulation studies 3 and 4 fit wrong models to the data. Simulation study 3 fits equal perception models to the fully-crossed and BIB ordered perception data sets generated in simulation study 2. All rater effects are revealed, although rater effects are distorted to some extent in the BIB design. Simulation study 4 fits ordered perception models to the fully-crossed and BIB equal perception data sets generated in study 1. All rater effects are recovered. Using essay scores from a large-scale language test, an empirical study is conducted. Both the equal and the ordered perception models are fitted. Information criteria favor the equal perception model.