On issues of equalization with the decorrelation algorithm : fast converging structures and finite-precision

To increase the rate of convergence of the blind, adaptive, decision feedback equalizer based on the decorrelation criterion, structures have been proposed which dramatically increase the complexity of the equalizer. The complexity of an algorithm has a direct bearing on the cost of implementing the algorithm in either hardware or software. In this thesis, more computationally efficient structures, based on the fast transversal filter and lattice algorithms, are proposed for the decorrelation algorithm which maintain the high rate of convergence of the more complex algorithms. Furthermore, the performance of the decorrelation algorithm in a finite-precision environment will be studied and compared to the widely used LMS algorithm

Adaptive Control of a MEMS Steering Mirror for Suppression of Laser Beam Jitter

Abstract-This paper presents an adaptive control scheme for laser-beam steering by a two-axis MEMS tilt mirror. Disturbances in the laser beam are rejected by a µ-synthesis feedback controller augmented by the adaptive control loop, which determines control gains that are optimal for the current disturbance acting on the laser beam. The adaptive loop is based on an adaptive lattice filter that implicitly identifies the disturbance statistics from real-time sensor data. Experimental results are presented to demonstrate that the adaptive controller significantly extends the disturbancerejection bandwidth achieved by the feedback controller alone

Adaptive filtering approaches for non-Gaussian stable processes

A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this paper, α-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such kind of noise is a requirement of many practical problems. Since, direct application of commonly used adaptation techniques fail in these applications, new approaches for adaptive filtering for α-stable random processes are introduced

Adaptive Filtering for Non-Gaussian Stable Processes

A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this letter, a-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such a noise is a requirement of many practical problems. Since direct application of commonly used adaptation techniques fail in these applications, new algorithms for adaptive filtering for α-stable random processes are introduced. © 1994 IEE

Investigations on efficient adaptation algorithms

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1995.Thesis (Master's) -- Bilkent University, 1995.Includes bibliographical references leaves 71-75.Efficient adaptation algorithms, which are intended to improve the performances of the LMS and the RLS algorithms are introduced. It is shown that nonlinear transformations of the input and the desired signals by a softlimiter improve the convergence speed of the LMS algorithm at no cost, with a small bias in the optimal filter coefficients. Also, the new algorithm can be used to filter a-stable non-Gaussian processes for which the conventional adaptive algorithms are useless. In a second approach, a prewhitening filter is used to increase the convergence speed of the LMS algorithm. It is shown that prewhitening does not change the relation between the input and the desired signals provided that the relation is a linear one. A low order adaptive prewhitening filter can provide significant speed up in the convergence. Finally, adaptive filtering algorithms running on roughly quantized signals are proposed to decrease the number of multiplications in the LMS and the RLS algorithms. Although, they require significantly less computations their preformances are comparable to those of the conventional LMS and RLS algorithms.Belge, MuratM.S

Multi-dimensional lattice equaliser for Q2 PSK

The aim of this dissertation was the design, implementation and performance evaluation of a Recursive Least Squares (RLS), lattice based, adaptive, multidimensional, decision feedback equaliser (DFE) for the spectrally efficient four-dimensional digital modulation technique, re¬ferred to as Quadrature-Quadrature Phase-Shift Keying, Q2pSK. Q2PSK constitutes a relatively new modulation technique, and the application of adaptive equalisation to this technique has not yet been considered in the open literature. This dissertation represents an in depth study into the Q2PSK modulation technique, as well as the optimal implementation, in simulation, of such a modem to aid the inclusion of the adap¬tive lattice DFE, for application to high speed mobile digital communication over the V /UHF channel in the presence of multi path propagation. Specific aspects of synchronization applicable to this modem platform are also addressed. An in depth study was also conducted into the realisation of a V /UHF channel simulation, capable of producing a Ricean and/or Rayleigh fad¬ing multipath propagation environment for the evaluation of the modem platform and adaptive equaliser structure. The theoretical analysis of the effect of multi path on a Q2PSK signal led to the correct design of the adaptive lattice structure, as well as the correct interfacing of the equaliser to the receiver platform. The performance of the proposed synchronisation strategies, in tandem with the equalisation technique were evaluated for several static, as well as fading multipath channels. The simulation results obtained show the equaliser operates correctly, and can give large performance gains over the static matched filter (matched to the transmitted waveform) implementation of the modem platform. Several simulations were specifically designed to highlight the performance limitations of the adaptive equalisation technique.Dissertation (MEng (Digital Communication))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte

New methods for robust speech recognition

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 1995.Thesis (Ph.D.) -- Bilkent University, 1995.Includes bibliographical references leaves 86-92.New methods of feature extraction, end-point detection and speech enhcincement are developed for a robust speech recognition system. The methods of feature extraction and end-point detection are based on wavelet analysis or subband analysis of the speech signal. Two new sets of speech feature parameters, SUBLSF’s and SUBCEP’s, are introduced. Both parameter sets are based on subband analysis. The SUBLSF feature parameters are obtained via linear predictive analysis on subbands. These speech feature parameters can produce better results than the full-band parameters when the noise is colored. The SUBCEP parameters are based on wavelet analysis or equivalently the multirate subband analysis of the speech signal. The SUBCEP parameters also provide robust recognition performance by appropriately deemphasizing the frequency bands corrupted by noise. It is experimentally observed that the subband analysis based feature parameters are more robust than the commonly used full-band analysis based parameters in the presence of car noise. The a-stable random processes can be used to model the impulsive nature of the public network telecommunication noise. Adaptive filtering are developed for Q-stable random processes. Adaptive noise cancelation techniques are used to reduce the mismacth between training and testing conditions of the recognition system over telephone lines. Another important problem in isolated speech recognition is to determine the boundaries of the speech utterances or words. Precise boundary detection of utterances improves the performance of speech recognition systems. A new distance measure based on the subband energy levels is introduced for endpoint detection.Erzin, EnginPh.D