Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

A New Hammerstein Model for Non-Linear System Identification

In the present work a newer type of black box nonlinear model in Hammerstein structure is proposed. The model has Wavelet Network coupled with Orthonormal Basis Functions which is capable of modeling a class of non-linear systems with acceptable accuracy. Wavelet basis functions have the property of localization in both the time and frequency domains which enables wavelet networks to approximate severe non-linearities using few number of parameters. Orthonormal Basis functions possess the ability to approximate any linear time invariant system using appropriate basis functions. The efficacy of the model in modeling is demonstrated using numerical examples

Switched capacitor networks : a novel prewarping procedure

Bibliography: leaves 152-157.Novel methods for prewarping filter specifications prior to realization. in Switched Capacitor (SC) form are presented. These allow the design of arbitrary response requirements, exhibiting a low amount of error that normally results from the frequency warping associated with sampled-data networks. Adjustment is applied to the pole and zero locations of a reference filter, using three distinct approaches (Center frequency "CF", Selectivity "S" and Complex Mapping "CM" pole/zero prewarping), developed for both the Lossless Discrete Integrator (LOI) and Bilinear (Bil) analog to digital transformations. The derivation of the prewarping expressions is explained with reference to these mappings, and the effect they have on the apparent pole and zero locations of an SC filter realization

Optimal digital filter design for dispersed signal equalization

Any signal a satellite receives from Earth has traveled through the ionosphere. Transmission through the ionosphere results in a frequency dependent time-delay of the signal frequency components. This effect of the medium on the signal is termed dispersion, and it increases the difficulty of pulse detection. A system capable of compensating for the dispersion would be desirable, as pulsed signals would be more readily detected after compression. In this thesis, we investigate the derivation of a digital filter to compensate for the dispersion caused by the ionosphere. A transfer function model for the analysis of the ionosphere as a system is introduced. Based on the signal model, a matched filter response is derived. The problem is formulated as a group delay compensation effort. The Abel-Smith algorithm is employed for the synthesis of a cascaded allpass filter bank with desired group delay characteristics. Extending this work, an optimized allpass filter is then derived using a pole location approach. A mean-square error metric shows that the optimized filter can reproduce, and even improve upon, the results of the Abel-Smith design with a significantly lower order filter. When compared against digital filters produced with the least p-th minimax algorithm, we find that the new method exhibits significantly lower error in the band of interest, as well as lower mean squared error overall. The result is a simple optimized equalization filter that is stable, robust against cascading difficulties, and applicable to arbitrary waveforms. This filter is the cornerstone to a new all-digital electromagnetic pulse detection system

Development of Urban Electric Bus Drivetrain

The development of the drivetrain for a new series of urban electric buses is presented in the paper. The traction and design properties of several drive variants are compared. The efficiency of the drive was tested using simulation calculations of the vehicle rides based on data from real bus lines in Prague. The results of the design work and simulation calculations are presented in the paper

Hybrid DDS-PLL based reconfigurable oscillators with high spectral purity for cognitive radio

Analytical, design and simulation studies on the performance optimization of reconfigurable architecture of a Hybrid DDS – PLL are presented in this thesis. The original contributions of this thesis are aimed towards the DDS, the dithering (spur suppression) scheme and the PLL. A new design of Taylor series-based DDS that reduces the dynamic power and number of multipliers is a significant contribution of this thesis. This thesis compares dynamic power and SFDR achieved in the design of varieties of DDS such as Quartic, Cubic, Linear and LHSC. This thesis proposes two novel schemes namely “Hartley Image Suppression” and “Adaptive Sinusoidal Interference Cancellation” overcoming the low noise floor of traditional dithering schemes. The simulation studies on a Taylor series-based DDS reveal an improvement in SFDR from 74 dB to 114 dB by using Least Mean Squares -Sinusoidal Interference Canceller (LM-SIC) with the noise floor maintained at -200 dB. Analytical formulations have been developed for a second order PLL to relate the phase noise to settling time and Phase Margin (PM) as well as to relate jitter variance and PM. New expressions relating phase noise to PM and lock time to PM are derived. This thesis derives the analytical relationship between the roots of the characteristic equation of a third order PLL and its performance metrics like PM, Gardner’s stability factor, jitter variance, spur gain and ratio of noise power to carrier power. This thesis presents an analysis to relate spur gain and capacitance ratio of a third order PLL. This thesis presents an analytical relationship between the lock time and the roots of its characteristic equation of a third order PLL. Through Vieta’s circle and Vieta’s angle, the performance metrics of a third order PLL are related to the real roots of its characteristic equation

A new structure for nonlinear black-box system identification using the extended Kalman filter

A new tool for black-box non-linear system identification of multi-input multi-output systems is presented in this paper. The new structure extends the conventional linear state-space model into a non-linear framework, where each parameter is a non-linear function of the inputs or the states. The method works iteratively in the time domain using an extended Kalman filter. The model retains a state-space structure in modal canonical form, which ensures that a minimal number of parameters need to be identified and also produces additional information in terms of the system eigenvalues and the dominant modes. This structure is a completely black-box system, which requires no physical understanding of the process for successful identification, and it is possible to expand easily the order and the complexity of non-linearities, while ensuring good parameter conditioning. A simple non-linear example illustrates the method, and identification of a highly non-linear brake model is also presented. These examples show that the method can be applied as a mechanism for model order reduction; it is equally very suitable as a tool for non-linear plant system identification. In both capacities this new method is valuable, particularly as the generation of simplified models for the whole vehicle and its subsystems is an increasingly important aspect of modern vehicle design

Target Recognition Using Late-Time Returns from Ultra-Wideband, Short-Pulse Radar

The goal of this research is to develop algorithms that recognize targets by exploiting properties in the late-time resonance induced by ultra-wide band radar signals. A new variant of the Matrix Pencil Method algorithm is developed that identifies complex resonant frequencies present in the scattered signal. Kalman filters are developed to represent the dynamics of the signals scattered from several target types. The Multiple Model Adaptive Estimation algorithm uses the Kalman filters to recognize targets. The target recognition algorithm is shown to be successful in the presence of noise. The performance of the new algorithms is compared to that of previously published algorithms