Vehicle vibration is inherently random and non-stationary with a non-Gaussian distribution. In addition, variations in vehicle parameters, product payloads and distribution journeys mean that the characteristics of vibration are not identical for all distribution journeys. Because vehicle vibration and shock are key causes of damage during distribution, their simulation in pre-distribution testing is vital in order to ensure that adequate protection is provided for transported products. The established method set out in the current testing standards utilises a global set of averaged accelerated power spectral density spectra to construct random vibration signals. These signals are stationary with Gaussian distributions and, therefore, do not fully represent actual vehicle vibration, only an average. The aim of the investigation, reported on in this Thesis, was to create an improved test regime for simulating vehicle vibration for pre-distribution testing of packaging. This aim has been achieved through the construction of representative tests and the creation of realistic simulations with statistical significance. A journey database has been created, in which historic road profile data along with a quarter vehicle model have been used to approximate a known vehicle’s vibration on a specific distribution journey. Additionally, a wavelet decomposition method, in which wavelet analysis is used to decompose the approximate vehicle vibration in to a series of Gaussian approximations of varying amplitude and spectral content, has been developed. Along with theoretical work, case studies have been undertaken in order to validate the test regime.

Dentre as inúmeras técnicas de conversão de voz utilizadas atualmente, aquelas baseadas em bancos de filtros wavelet, associadas com redes neurais artificiais,têm se destacado. Este trabalho se concentra em tais técnicas, realizando um estudo que relaciona qual a melhor wavelet para conversão de determinados padrões de voz, apresentando uma análise detalhada de quais são as características que levam a estes resultados. Os testes são realizados com vozes da base de dados TIMIT do Linguistic Data Consortium (LDC). / Dentre as inúmeras técnicas de conversão de voz utilizadas atualmente, aquelas baseadas em bancos de filtros wavelet, associadas com redes neurais artificiais, têm se destacado. Este trabalho se concentra em tais técnicas, realizando um estudo que relaciona qual a melhor wavelet para conversão de determinados padrões de voz, apresentando uma análise detalhada de quais são as características que levam a estes resultados. Os testes são realizados com vozes da base de dados TIMIT do Linguistic Data Consortium (LDC).

Shea Yuen Cheuk. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 91-94). / Abstracts in English and Chinese. / Introduction --- p.5 / Chapter 1 --- Prelimaries --- p.9 / Chapter 1.1 --- Basic Notations --- p.9 / Chapter 1.2 --- Multiresolution Analysis --- p.12 / Chapter 1.3 --- Orthonormal Wavelets --- p.17 / Chapter 1.4 --- Theory of Frames --- p.24 / Chapter 2 --- Construction of Orthonormal Wavelets --- p.33 / Chapter 2.1 --- Compactly Supported Smooth Orthonormal Wavelet in R --- p.33 / Chapter 2.2 --- Compactly Supported Smooth Orthonormal Wavelet in R2 --- p.40 / Chapter 3 --- Wavelet Frames --- p.51 / Chapter 3.1 --- Basic Properties --- p.51 / Chapter 3.2 --- Dual Wavelet Frame --- p.56 / Chapter 3.3 --- Canonical Dual Frame --- p.66 / Chapter 3.4 --- Oversampling --- p.69 / Chapter 4 --- MRA-Based Wavelet Frames --- p.74 / Chapter 4.1 --- Definitions --- p.74 / Chapter 4.2 --- Tight Frames Constructed by MRA --- p.77 / Chapter 4.3 --- Approximation Order and Vanishing Moments for Wavelet Frames --- p.82 / Chapter 4.4 --- Construction of MRA-Based Wavelet Frames --- p.85 / Bibliography --- p.91

by Pun Kong-Pang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 94-[96]). / Acknowledgement --- p.i / Abstract --- p.iii / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Role of Device Simulation --- p.2 / Chapter 1.2 --- Classification of Device Models --- p.3 / Chapter 1.3 --- Sections of a Typical Simulator --- p.6 / Chapter 1.4 --- Arrangement of This Thesis --- p.7 / Chapter 2 --- Classical Physical Model --- p.9 / Chapter 2.1 --- Carrier Densities --- p.12 / Chapter 2.2 --- Space Charge --- p.14 / Chapter 2.3 --- Carrier Mobilities --- p.15 / Chapter 2.4 --- Generation and Recombination --- p.17 / Chapter 2.5 --- Modeling of Device Boundaries --- p.20 / Chapter 2.6 --- Limits of Classical Device Modeling --- p.22 / Chapter 3 --- Computational Aspects --- p.23 / Chapter 3.1 --- Normalization --- p.24 / Chapter 3.2 --- Discretization --- p.26 / Chapter 3.2.1 --- Finite Difference Method --- p.26 / Chapter 3.2.2 --- Finite Element Method --- p.27 / Chapter 3.3 --- Nonlinear Systems --- p.28 / Chapter 3.3.1 --- Newton's Method --- p.28 / Chapter 3.3.2 --- Gummel's Method and its modification --- p.29 / Chapter 3.3.3 --- Comparison and discussion --- p.30 / Chapter 3.4 --- Linear System and Sparse Matrix --- p.32 / Chapter 4 --- Cubic Spline Wavelet Collocation Method for PDEs --- p.34 / Chapter 4.1 --- Cubic spline scaling functions and wavelets --- p.35 / Chapter 4.1.1 --- Approximation for a function in H2(I) --- p.43 / Chapter 4.2 --- Wavelet interpolation --- p.45 / Chapter 4.2.1 --- Interpolant operator Ivo in Vo --- p.45 / Chapter 4.2.2 --- Interpolation operator IWjf in Wj --- p.47 / Chapter 4.3 --- Derivative Matrices --- p.51 / Chapter 4.3.1 --- First derivative matrix --- p.51 / Chapter 4.3.2 --- Second derivative matrix --- p.53 / Chapter 4.4 --- Wavelet Collocation Method for Solving Device Equations --- p.55 / Chapter 4.4.1 --- Steady state solution --- p.57 / Chapter 4.4.2 --- Transient solution --- p.58 / Chapter 4.5 --- Reducing Collocation Points --- p.59 / Chapter 4.5.1 --- Error evaluation --- p.59 / Chapter 4.5.2 --- Deleting collocation points --- p.61 / Chapter 5 --- Numerical Results --- p.64 / Chapter 5.1 --- P-N Junction Diode --- p.64 / Chapter 5.1.1 --- Steady state solution --- p.69 / Chapter 5.1.2 --- Transient solution --- p.76 / Chapter 5.1.3 --- Convergence --- p.79 / Chapter 5.2 --- Bipolar Transistor --- p.81 / Chapter 5.2.1 --- Boundary Model --- p.82 / Chapter 5.2.2 --- DC Solution --- p.83 / Chapter 5.2.3 --- Transient Solution --- p.89 / Chapter 6 --- Conclusions --- p.92 / Bibliography --- p.94

No description available.

On account of ¡§the X-ray Phase Problem¡¨, it¡¦s impossible to get the electronic density by inverse Fourier transformation(IFT). And the crystal structure of molecule can¡¦t be solved by IFT. There are many methods to overcome the problem, such as Direct Method, Heavy Atom, and Simulated Annealing. But for macro-molecules, we still face a great challenge due to tremendous computing load. We propose a method by using wavelets to resolve the electronic density. We hope the multiresolution nature of wavelets can reduce the computing load for simulated annealing method.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 85-87). Also available on the Internet.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 80-81).

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2002. / Thesis advisor(s): Murali Tummala, Robert Ives. Includes bibliographical references (p. 89-90). Also available online.

Thesis (Ph.D.)--Brown University, 2008. / Vita. Advisor : Donald McClure. Includes bibliographical references (leaves 87-90).