by Yeung, Lap Kun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 96-98). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgements --- p.iv / Table of Contents --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Emergence of LTCC Technology --- p.1 / Chapter 1.2 --- Overview of the Work --- p.2 / Chapter 1.3 --- Original Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- Fundamentals of Partial Element Equivalent Circuit Modeling --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- PEEC Formulation --- p.6 / Chapter 2.2.1 --- Mixed potential integral equation --- p.6 / Chapter 2.2.2 --- Current discretization --- p.7 / Chapter 2.2.3 --- Charge discretization --- p.8 / Chapter 2.2.4 --- Galerkin matching --- p.9 / Chapter 2.3 --- Partial Inductance --- p.11 / Chapter 2.4 --- Partial Capacitance --- p.12 / Chapter 2.5 --- Meshing Scheme and Circuit Interpretation --- p.13 / Chapter 2.6 --- Summary --- p.15 / Chapter 3 --- PEEC Modeling of LTCC RF Circuits using Thin-film Approximation --- p.16 / Chapter 3.1 --- Introduction --- p.16 / Chapter 3.2 --- A Simple LTCC Band-pass Filter --- p.17 / Chapter 3.3 --- Discretization Scheme --- p.18 / Chapter 3.4 --- Quasi-static Green's Functions --- p.21 / Chapter 3.4.1 --- Free-space Green's function --- p.21 / Chapter 3.4.2 --- System with a single ground plane --- p.22 / Chapter 3.4.3 --- System with two ground planes --- p.25 / Chapter 3.5 --- Complex-Image Analysis --- p.25 / Chapter 3.6 --- Partial Inductance --- p.31 / Chapter 3.6.1 --- Strip-to-strip inductance --- p.31 / Chapter 3.6.2 --- System with one or more ground planes --- p.33 / Chapter 3.7 --- Partial Capacitance --- p.34 / Chapter 3.8 --- Numerical and Experimental Results --- p.37 / Chapter 3.9 --- Summary --- p.40 / Chapter 4 --- PEEC Modeling of LTCC RF Circuits using Thin-film Approximation (Via-hole Modeling) --- p.41 / Chapter 4.1 --- Introduction --- p.41 / Chapter 4.2 --- Via-hole Modeling --- p.42 / Chapter 4.2.1 --- Discretization scheme --- p.42 / Chapter 4.2.2 --- Inductance formulae --- p.43 / Chapter 4.2.3 --- Empirical formula --- p.46 / Chapter 4.2.4 --- Edge-effect compensation --- p.48 / Chapter 4.3 --- Numerical and Experimental Results --- p.49 / Chapter 4.4 --- Summary --- p.51 / Chapter 5 --- An Efficient PEEC Algorithm for Modeling of LTCC RF Circuits with Finite Metal Strip Thickness --- p.53 / Chapter 5.1 --- Introduction --- p.53 / Chapter 5.2 --- PEEC Modeling using Thin-film Approximation --- p.54 / Chapter 5.3 --- PEEC Modeling with Finite Metal Thickness --- p.55 / Chapter 5.4 --- Edge-effect Compensation in Inductance Calculation --- p.57 / Chapter 5.5 --- Numerical and Experimental Results --- p.61 / Chapter 5.6 --- Summary --- p.65 / Chapter 6 --- A Compact Second-order LTCC Band-pass Filter with Two Finite Transmission Zeros --- p.66 / Chapter 6.1 --- Introduction --- p.66 / Chapter 6.2 --- Features of the Filter --- p.67 / Chapter 6.3 --- Design Theory --- p.68 / Chapter 6.4 --- LTCC Filter Implementation --- p.70 / Chapter 6.4.1 --- Circuit model --- p.70 / Chapter 6.4.2 --- Physical layout --- p.73 / Chapter 6.5 --- Experimental Results --- p.75 / Chapter 6.6 --- Summary --- p.77 / Chapter 7 --- Concluding Remarks --- p.79 / Chapter 7.1 --- PEEC Modeling --- p.79 / Chapter 7.2 --- Limitations of the Algorithm --- p.80 / Chapter 7.3 --- Further Improvements --- p.81 / Appendix --- p.82 / References --- p.96 / Author's Publications --- p.98

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 113-116. / by James R. Rinderle. / Ph.D.

by Ma Wing-Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 121-124). / Chapter 1 --- Introduction to Otoacoustic Emissions --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Clinical Significance of the OAEs --- p.2 / Chapter 1.3 --- Classes of OAEs --- p.3 / Chapter 1.4 --- The Distortion Product OAEs --- p.4 / Chapter 1.4.1 --- Measurement of DPOAEs --- p.5 / Chapter 1.4.2 --- Some Properties of DPOAEs --- p.8 / Chapter 1.4.3 --- Noise Reduction of DPOAEs --- p.8 / Chapter 1.5 --- Goal of this work and Organization of the Thesis --- p.9 / Chapter 2 --- Review to some Topics in Narrowband Signal Estimation --- p.11 / Chapter 2.1 --- Fourier Transforms --- p.12 / Chapter 2.2 --- Periodogram ´ؤ Classical Spectrum Estimation Method --- p.15 / Chapter 2.2.1 --- Signal-to-Noise Ratios and Equivalent Noise Bandwidth --- p.17 / Chapter 2.2.2 --- Scalloping --- p.18 / Chapter 2.3 --- Maximum Likelihood Estimation --- p.19 / Chapter 2.3.1 --- Finding of the ML Estimator --- p.19 / Chapter 2.3.2 --- Properties of the ML Estimator --- p.21 / Chapter 3 --- Review to Adaptive Notch/Bandpass Filter --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Filter Structure --- p.24 / Chapter 3.3 --- Adaptation Algorithms --- p.25 / Chapter 3.3.1 --- Least Squares Method --- p.25 / Chapter 3.3.2 --- Least-Mean-Squares Algorithm --- p.27 / Chapter 3.3.3 --- Recursive-Least-Squares Algorithm --- p.28 / Chapter 3.4 --- LMS ANBF Versus RLS ANBF --- p.31 / Chapter 3.5 --- the IIR filter Versus ANBF --- p.31 / Chapter 4 --- Fast RLS Adaptive Notch/Bandpass Filter --- p.33 / Chapter 4.1 --- Motivation --- p.33 / Chapter 4.2 --- Theoretical Analysis of Sample Autocorrelation Matrix --- p.34 / Chapter 4.2.1 --- Solution of Φ (n) --- p.34 / Chapter 4.2.2 --- Approximation of Φ (n) --- p.35 / Chapter 4.3 --- Fast RLS ANBF Algorithm --- p.37 / Chapter 4.4 --- Performance Study --- p.39 / Chapter 4.4.1 --- Relationship to LMS ANBF and Bandwidth Evaluation . --- p.39 / Chapter 4.4.2 --- Estimation Error of Tap Weights --- p.40 / Chapter 4.4.3 --- Residual Noise Power of Bandpass Output --- p.42 / Chapter 4.5 --- Simulation Examples --- p.43 / Chapter 4.5.1 --- Estimation of Single Sinusoid in Gaussian White Noise . --- p.43 / Chapter 4.5.2 --- Comparing the Performance of IIR Filter and ANBFs . . --- p.44 / Chapter 4.5.3 --- Harmonic Signal Enhancement --- p.45 / Chapter 4.5.4 --- Cancelling 50/60Hz Interference in ECG signal --- p.46 / Chapter 4.6 --- Simulation Results of Performance Study --- p.52 / Chapter 4.6.1 --- Bandwidth --- p.52 / Chapter 4.6.2 --- Estimation Errors --- p.53 / Chapter 4.7 --- Concluding Summary --- p.55 / Chapter 4.8 --- Appendix A: Derivation of Ts --- p.56 / Chapter 4.9 --- Appendix B: Derivation of XT(n)Λ(n)ΛT(n)X(n) --- p.56 / Chapter 5 --- Investigation of the Performance of two Conventional DPOAE Estimation Methods --- p.58 / Chapter 5.1 --- Motivation --- p.58 / Chapter 5.2 --- The DPOAE Signal Model --- p.59 / Chapter 5.3 --- Preliminaries to the Conventional Methods --- p.60 / Chapter 5.3.1 --- Conventional Method 1: Constrained Stimulus Generation --- p.60 / Chapter 5.3.2 --- Conventional Method 2: Windowing --- p.61 / Chapter 5.4 --- Performance Comparison --- p.63 / Chapter 5.4.1 --- Sidelobe Level Reduction --- p.63 / Chapter 5.4.2 --- Estimation Accuracy --- p.65 / Chapter 5.4.3 --- Noise Floor Level --- p.67 / Chapter 5.4.4 --- Additional Loss by Scalloping --- p.68 / Chapter 5.5 --- Simulation Study --- p.69 / Chapter 5.5.1 --- Sidelobe Suppressions of the Windows --- p.69 / Chapter 5.5.2 --- Mean Level Estimation --- p.70 / Chapter 5.5.3 --- Mean Squared Error Analysis --- p.71 / Chapter 5.6 --- Concluding Summary --- p.75 / Chapter 5.7 --- Discussion --- p.75 / Chapter 5.8 --- Appendix A: Cramer-Rao Bound of the DPOAE Level Estimation --- p.76 / Chapter 6 --- Theoretical Considerations of Maximum Likelihood Estimation for the DPOAEs --- p.77 / Chapter 6.1 --- Motivation --- p.77 / Chapter 6.2 --- Finding of the MLEs --- p.78 / Chapter 6.2.1 --- First Form: Joint Estimation of DPOAE and Artifact Pa- rameter --- p.79 / Chapter 6.2.2 --- Second Form: Artifact Cancellation --- p.80 / Chapter 6.3 --- Relationship of CM1 to MLE --- p.81 / Chapter 6.4 --- Approximating the MLE --- p.82 / Chapter 6.5 --- Concluding Summary --- p.84 / Chapter 6.6 --- Appendix A: Equivalent Forms for the Minimum Least Squares Error --- p.85 / Chapter 7 --- Optimum Estimator Structure and Artifact Cancellation Ap- proaches for the DPOAEs --- p.87 / Chapter 7.1 --- Motivation --- p.87 / Chapter 7.2 --- The Optimum Estimator Structure --- p.88 / Chapter 7.3 --- References and Frequency Offset Effect --- p.89 / Chapter 7.4 --- Artifact Canceling Algorithms --- p.92 / Chapter 7.4.1 --- Least-Squares Canceler --- p.93 / Chapter 7.4.2 --- Windowed-Fourier-Transform Canceler --- p.93 / Chapter 7.4.3 --- FRLS Adaptive Canceler --- p.95 / Chapter 7.5 --- Time-domain Noise Rejection --- p.97 / Chapter 7.6 --- Regional Periodogram --- p.98 / Chapter 7.7 --- Experimental Results --- p.99 / Chapter 7.7.1 --- Artifact Cancellation via External Reference --- p.99 / Chapter 7.7.2 --- Artifact Cancellation via Internal Reference --- p.99 / Chapter 7.7.3 --- Artifact Cancellation in presence of Transient Noise --- p.101 / Chapter 7.7.4 --- Illustrative Example: DPgrams --- p.102 / Chapter 7.8 --- Conclusion and Discussion --- p.111 / Chapter 7.9 --- Appendix A: Derivation of the Parabolic Interpolation Method . --- p.113 / Chapter 7.10 --- Appendix B: Derivation of Weighted-Least-Squares Canceler . . --- p.114 / Chapter 8 --- Conclusions and Future Research Directions --- p.118 / Chapter 8.1 --- Conclusions --- p.118 / Chapter 8.2 --- Future Research Directions --- p.119 / Bibliography --- p.121

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1978. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaf 29. / B.S.

O objetivo da desconvolução autodidata de imagens é reconstruir a imagem original a partir de uma imagem degradada sem usar informação da imagem real ou da função de degradação. O processo de reconstrução é crítico em aplicações em que a imagem original ou suas características estatísticas não são conhecidas. Fazendo um mapeamento da imagem digital antes de sua transmissão, ela pode ser interpretada como um sinal de comunicação com modulação do tipo PAM (Pulse Amplitude Modulation). Utilizando essa interpretação, técnicas clássicas de equalização de canais de comunicação podem ser usadas para restauração de imagens. Além disso, é usual considerar os pixels de uma imagem como um sinal não-estacionário, o que justifica o uso de algoritmos adaptativos. Neste trabalho, técnicas adaptativas usadas em equalização de canais de comunicação são aplicadas para restauração de imagens. Inicialmente, é proposta uma nova técnica de varredura a fim de minimizar alterações bruscas no sinal de entrada do filtro adaptativo. Utilizando o algoritmo Least Mean Squares, obtém-se uma equivalência entre funções de degradação de imagens e canais de comunicação variantes no tempo. Isso possibilitou comparar algumas funções de degradação com relação à distorção causada em imagens. Em seguida, usando um rearranjo dos elementos da matriz de entrada em um vetor, o algoritmo multimódulo regional (RMMA - Region-based Multimodulus Algorithm) foi estendido para restauração de imagens. Esse algoritmo é então usado para adaptação dos coeficientes do equalizador linear transversal e também do equalizador de decisão realimentada. Cabe observar que o RMMA trata um sinal de módulo não-constante como se fosse de módulo constante, o que proporciona um desempenho melhor quando comparado ao algoritmo do módulo constante (CMA - Constant Modulus Algorithm) convencional, usado em equalização autodidata de canais de comunicação. Esse comportamento também foi observado na reconstrução de imagens, através das simulações apresentadas nesta dissertação. Este estudo abre novas perspectivas de extensão de técnicas usadas em equalização de canais de comunicação para restauração imagens. Uma delas é a possibilidade de restauração de imagens coloridas usando diversidade espacial. / The aim of blind image deconvolution is to reconstruct the original scene from a degraded observation without using information about the true image and the point spread function. The restoration process is critical in applications, where the true image or its statistical characteristics are unknown. Mapping the pixels of the original image before its transmission, the mapped image can be interpreted as a pulse amplitude modulation (PAM) signal, used in communications systems. With this interpretation, classic equalization techniques of communication channels can be used to image restoration. Furthermore, the pixels of a true image constitute a nonstationary signal, which justifies the use of adaptive filters. In this dissertation, adaptive techniques used for equalization of communication channels are applied to image restoration. Firstly, we propose a new update path through the blurred image that consists in a combination of horizontal and vertical alternate paths. This update path minimizes the problem of abrupt changes in the adaptation of the filter and provides better conditions to the image recovery. Using the least mean squares (LMS) algorithm, we obtain an equivalence between a point spread function and a time-variant communication channel. This equivalence was used to compare some point spread functions in relation to the distortion caused in images. Secondly, reshaping the input matrix into a column vector, we extend the regional-based multimodulus algorithm (RMMA) to blind image deconvolution. This algorithm is used to update the coefficients of the linear transversal equalizer and also of the decision feedback equalizer. RMMA treats nonconstant modulus signals as constant modulus ones, which provides a better performance when compared to the conventional constant modulus algorithm (CMA), used in blind equalization of communication channels. This behavior was also observed in image restoration, through the simulations presented in this dissertation. This study pushes back the frontiers of image processing, since different techniques used in equalization can be extended to image restoration. One of the new possibilities is the color image restoration using the spatial diversity.

Nas últimas décadas, vários sistemas de comunicação baseados em sincronismo caótico têm sido propostos na literatura como alternativa a sistemas de espalhamento espectral que melhoram o nível de privacidade na transmissão da mensagem. No entanto, devido à falta de robustez do sincronismo caótico, um pequeno nível de ruído ou uma simples imperfeição no canal é suficiente para impedir a comunicação. Neste trabalho, equalizadores adaptativos são utilizados para permitir a comunicação em um sistema de comunicação baseado em caos quando a resposta em frequência do canal não é ideal. São propostos algoritmos de equalização baseados em versões modificadas do algoritmo normalized least-mean-squares para a versão de tempo discreto do sistema de comunicação baseado no modelo de sincronismo de Wu e Chua. Para esses algoritmos, é calculado o intervalo para a escolha do passo de adaptação para evitar a divergência. Como geradores de sinais caóticos (GSC), são utilizados os mapas de Hénon e de Ikeda e, para a codificação da mensagem, são consideradas duas funções, sendo uma baseada na multiplicação da mensagem por um dos estados do GSC e a outra baseada na soma da mensagem com um dos estados do GSC. Os resultados de simulação indicam que os algoritmos propostos são capazes de equalizar o canal de comunicação e permitir o sincronismo caótico em diferente cenários. / In the last decades, many communication systems applying synchronism of chaotic systems have been proposed as an alternative spread spectrum modulation that improves the level of privacy in data transmission. However, due to the lack of robustness of chaos synchronization, even a low level of noise or minor channel imperfections are enough to hinder communication. In this work, adaptive equalizers are used to enable chaotic synchronization when the communication channel is not ideal. Adaptive equalization algorithms are proposed based on a modified version of the normalized least-mean-squares algorithm, considering the discrete-time version of the communication system based on Wu and Chuas synchronization model. For these algorithms, the interval for the choice of the step-size is computed, in order to avoid divergence. The Hénon and the Ikeda maps are used as chaotic signal generators (CSG) and two functions are considered to encode the message, one based on the multiplication of the message by one of the states of the CSG and the other based on the addition of the message to one of the states of the CSG. Simulation results show that the proposed algorithms can successfully equalize the channel in different scenarios.

It is important to have the ability to predict the effects of device model variation when designing integrated transconductance-C type active filters. Applying these filters to integrated circuit design has become increasingly popular due to its ease of implementation in monolithic form. With the introduction of fully automated design tools, predictable behavior of high-level variables becomes still more important. The purpose of this study is to evaluate the process parameter spread of analog device models to determine the effect on the design parameters of an active filter. This information's significant contribution directly effects the feasibility and realization of automating analog filter design. In order to explore the dependence of filter performance on the device v model parameter spread, a fifth-order inverse Chebyshev filter is designed and simulated using a two year history of process models. It has not been observed that higher order filters have been successfully designed using fully automated design tools. This filter was realized using automated filter design currently being developed in parallel with this study. A single-ended input to single-ended output transconductance amplifier is chosen for this design for its simplicity and small size. Differential performance is easily adapted with exact duplication which is demonstrated in the measurements of the fabricated filter. Simulation of the design is performed using MOSIS SCNA device parameters. Filter performance data such as cutoff frequency, stopband attenuation, and phase response is collected. Experimental results from the fabricated device are compared to simulation and the original prototype. 2 It is shown that the most predicable effect on the design parameters of a filter is caused by the parasitic output conductance parameter g0. This process dependent variable causes both a deviation in the cutoff frequency, and a decrease in the filter quality factor. In addition, it is also shown that the practice employed to predistort for absorption of parasitic capacitors in a MOS technology is a very effective tool in the reduction of capacitive process dependence.n software

The 24-bit Motorola DSP56001 processor will be used in combination with the DSP56ADC16 and the PCM-56 to design a good FIR compensation filter. Our objective is to digitize the input analog signal, and to compensate for the attenuation in the magnitude response of the digital sine wave. Two different experiments will be conducted, a hands on approach, and a simulation program. The first one will be realized directly, using the DSP system. We will determine the magnitude response of the system, and then deduce the coefficients of the FIR sin(x)/x filter. A look up table will store those values which will be fetched by the DSP program. With a minimum set of instructions we will generate a new digital output sequence after a N-point circular convolution is performed. The output signal is a good reconstruction of the input signal at frequencies below 22 Khz. However, a second experiment will be needed to improve this FIR sin(x)/x compensation filter, because we are not able to go beyond a 300-point impulse sequence. After that value (300-point), the time that each value is read and is ready to be processed by the DSP56001 becomes smaller than the time each instruction in the DSP program is executed and written to the PCM-56 via the SSI register. To be able to expand our experiment, we need to write a simulation program. A simulation program of the previous experiment, which take as input the measured magnitude response of the system. The challenge will be to find ways to map the frequency domain, by using the maximum value of each linear convolution sequence, with a finite input sequence. A step by step approach will be drawn until our final objective is reached. Our final step will be, to increase the number of sampling point in the frequency domain and will be to demonstrate that the result of the simulated program value will coincide with our objective, which is to compensate for the attenuation of the magnitude response of the system. By increasing the sampling frequency we will eventually obtain a good compensation filter.

Graduation date: 1995

No description available.