Round-off error propagation in four generally applicable, recursive, least-squares-estimation schemes

The numerical robustness of four generally applicable, recursive, least-squares-estimation schemes is analyzed by means of a theoretical round-off propagation study. This study highlights a number of practical, interesting insights of widely used recursive least-squares schemes. These insights have been confirmed in an experimental study as well

Robust speaker identification using artificial neural networks

This research mainly focuses on recognizing the speakers through their speech samples. Numerous Text-Dependent or Text-Independent algorithms have been developed by people so far, to recognize the speaker from his/her speech. In this thesis, we concentrate on the recognition of the speaker from the fixed text i.e. Text-Dependent . Possibility of extending this method to variable text i.e. Text-Independent is also analyzed. Different feature extraction algorithms are employed and their performance with Artificial Neural Networks as a Data Classifier on a fixed training set is analyzed. We find a way to combine all these individual feature extraction algorithms by incorporating their interdependence. The efficiency of these algorithms is determined after the input speech is classified using Back Propagation Algorithm of Artificial Neural Networks. A special case of Back Propagation Algorithm which improves the efficiency of the classification is also discussed

Stability of Levinson algorithm for Toeplitz-like systems

Numerical stability of the Levinson algorithm generalized for Toeplitzlike systems, is studied. Arguments based on the analytic results of an error analysis for floating point arithmetic produce an exponential upper bound on the norm of the residual vector. The base of such exponential function can be small for a class of matrices containing point row diagonally dominant matrices. Numerical experiments show that, for this class, Gaussian elimination by row and Levinson algorithm have residuals of the same order of magnitude. As expected, the empirical results point out that the theoretical bound is too pessimistic

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications, Springer. The article contains 23 pages, 4 figures, 8 tables and 51 reference

Iterative analysis of the steady-state weight fluctuations in LMS-type adaptive filters

An iterative method is proposed for the analysis of the steady-state weight fluctuations in an LMS-type adaptive FIR filter. Without the widely used independence assumption, a power series of the weight error correlation matrix is derived in terms of the stepsize. Some new effects are observed, e.g., a decrease of the weight fluctuations along the tapped-delay lin

Linear predictive modelling of speech : constraints and line spectrum pair decomposition

In an exploration of the spectral modelling of speech, this thesis presents theory and applications of constrained linear predictive (LP) models. Spectral models are essential in many applications of speech technology, such as speech coding, synthesis and recognition. At present, the prevailing approach in speech spectral modelling is linear prediction. In speech coding, spectral models obtained by LP are typically quantised using a polynomial transform called the Line Spectrum Pair (LSP) decomposition. An inherent drawback of conventional LP is its inability to include speech specific a priori information in the modelling process. This thesis, in contrast, presents different constraints applied to LP models, which are then shown to have relevant properties with respect to root loci of the model in its all-pole form. Namely, we show that LSP polynomials correspond to time domain constraints that force the roots of the model to the unit circle. Furthermore, this result is used in the development of advanced spectral models of speech that are represented by stable all-pole filters. Moreover, the theoretical results also include a generic framework for constrained linear predictive models in matrix notation. For these models, we derive sufficient criteria for stability of their all-pole form. Such models can be used to include a priori information in the generation of any application specific, linear predictive model. As a side result, we present a matrix decomposition rule for Toeplitz and Hankel matrices.reviewe

TrackFit: Uncertainty Quantification, Optimal Filtering and Onterpolation of Tracks for Time-Resolved Lagrangian Particle Tracking

Advanced Lagrangian Particle Tracking methods (such as the STB algorithm (Schanz et al. 2016)) are a very useful tool for uncovering properties of flow. As a measurement technique, the results of such methods are perturbed by different sources of errors and noise. This work addresses the problem of optimal filtering of particle tracks as well as estimating uncertainties of derived quantities such as location, velocity and acceleration of observed particles. The behavior and performance of this new filtering method (“TrackFit”), first introduced at Gesemann et al. 2016 is analyzed and compared to the Savitzky–Golay filter (Savitzky & Golay 1964) which is commonly used for these purposes. The optimal choice of parameters of this filtering method as well as the uncertainty quantification of the reconstructed tracks can be extracted from a spectral analysis of the recorded raw particle tracking data. This is in contrast to a Savitzky–Golay filter where the choice of parameters might often be driven by experience and gut feeling. In addition, we show that regardless of the choice of Savitzky–Golay filter parameters, the resulting filter will not approximate the ideal noise reduction filter well unlike the “TrackFit” described in this work