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An electrocardiogram (ECG) is a measurement of the electrical signal produced by the heart as it beats. This is a signal very commonly used by medical professionals, as it gives an indication of an individual’s heart rate and can further be used to detect specific abnormalities within the heart. There are a number of sources of noise that can corrupt the ECG signal, the most problematic being that of motion artifacts. As an individual wearing a surface ECG moves, their movements will add noise to the signal. This noise is particularly difficult to remove, as it will change depending on the movements of the user and will often fall in the same spectrum as the ECG signal itself. The effectiveness of the adaptive filtering method in reducing motion artifacts is investigated using multiple motion sensors on key locations of the body and by combining the motion data through the use of various blind source separation methods. An adaptive filter is a filter that can use a reference signal in order to readjust itself to a constantly changing noise signal and is commonly used to clean ECG signals. The adaptive filter uses noise estimations based on the reference signal as well as previous noise estimations in order to continually clean the noisy signal. Since motion artifacts are based directly off the movements of the user, collected motion data will be directly correlated with the noise being introduced to the ECG, and can therefore be used in the adaptive filter to produce a desirable ECG signal.

Voice over IP (VoIP) is the group of techniques for delivering voice communications over Internet Protocol (IP) networks. It has mainly served as the possible substitution for regular PSTN over the last decades, but has recently gained an increased interest in various areas such as alarm applications and customer service. Acoustic echo is the situation were a distorted version of the sent signal is transmitted back to the sender, due to acoustic feedback between loudspeaker and microphone. There already exists several algorithms to solve this problem, and this thesis provides a study of the performance in relation to the computational complexity of the algorithms. This is in order to indicate which approaches are better suited for implementation in an embedded system, where resources are limited. During the thesis a number of algorithms were tested, including variations of the LMS algorithm, some other approaches utilizing the correlation between echo and signal, and the RLS algorithm. They were first tested in MATLAB, on speech signals recorded at Syntronic and distorted by adding echo, then tested by implementation in C, and run on speech signals recorded in a simulated VoIP system at Syntronic. The results were then evaluated in terms of efficiency and computational complexity.

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The main theme of this thesis is the control of acoustic echoes for modem voice communication systems by means of echo cancellation. Two important issues in acoustic echo cancellation, namely the efficient adaptation of the echo cancellation filter and the reliable adaptation of the echo cancellation filter in double talk environment, are investigated. The delayless subband adaptive filter architecture is studied. Efficient implementation of the analysis filter bank and the time domain filtering are derived. The transforming of the subband filter weights to a fullhand counterpart is examined. It is shown that the weight transform is a synthesis filtering procedure. Two new weight transform schemes that deliver substantial performance improvements are proposed. The open-loop optimal subband filter impulse responses are shown to be non-causal and several anti-causal laps in the subband filters are required to model this non-causality. Because of the inevitable double talk detection errors, adaptive filtering algorithms with built-in double talk robustness measures are needed for the reliable operation of the echo canceller. The basic idea of robust adaptive filtering is examined. A comparison of different existing time domain robust adaptive filtering algorithms demonstrates that excellent trade-off between the convergence and the tracking properties and the double talk robustness of the adaptive filtering algorithm can he achieved by using Huher’s method for both the update of the echo cancellation filter and the estimation of scale. A delayless closed-loop robust sub- hand adaptive filter is proposed. / By independently adapting the scale estimates and normalizing the adaptation in each subband, significant improvement in terms of the convergence and tracking speed over the time domain robust NLMS algorithm can be obtained without sacrificing the double talk robustness. Moreover, it is demonstrated that by using different thresholds in the update of the echo cancellation filter and the scales, the robust algorithms converge and track echo path variation as fast as their non-robust counter part while still maintaining a sufficiently low sensitivity to double talk detection errors. The application of two path adaptive filters to acoustic echo cancellation is examined. An analysis of the original two path adaptive filtering algorithm shows that it suffers from two kinds of performance degradation due to the divergence of the background filter during double talk, namely the slow tracking of echo path variation and the false filter coefficient copying after double talk. A robust two path adaptive filter is proposed to mitigate these problems.

Speech coding or speech compression is one of the important aspects of speech communications nowadays. By coding the speech, the speed needed to transmit the digitized speech, called the bit rate, can be reduced. This means that for a certain speech communications channel, the lower the bit rate of the speech coding, the more communicating parties can be carried on that channel. This research has as its main application the extraction of the parameters of human speech for speech coding purposes. We propose an RLS-based cascade adaptive filter structure that can significantly reduce the computational effort required by the RLS algorithm for inverse filtering types of applications. We named it the Cascade RLS with Subsection Adaptation (CRLS-SA) algorithm. The reduction in computational effort comes from the fact that, for inverse filtering applications, the gradients of each section in the cascade are almost uncorrelated with the gradients in other sections. Hence, the gradient autocorrelation matrix is assumed to be block diagonal. Since we use a second order filter for each section, the computation of the adaptation involves only the 2x2- gradient autocorrelation matrix for that section, while still being based on a global minimization criterion. The gradient signal of a section itself is defined as the derivative of the overall output error with respect to the coefficients of the particular section, which can be computed efficiently by passing the overall output of the cascade to a filter with coefficients that are derived from the coefficients of that section. The computational effort of the CRLS-SA algorithm is approximately 20*L*N/2, where L is the data record length and N is the order of the filter. We analyze the convergence rate of the CRLS-SA algorithm based on the convergence time constant concept, which is the ratio of the condition number and the sensitivity. The CRLS- SA structure is shown to satisfy the DeBrunner-Beex conjecture which says that a structure with a smaller convergence time constant converges faster than a structure with a larger convergence time constant. We show that CRLS-SA converges faster than the Direct Form RLS (DFRLS) algorithm and that its convergence time constant is lower than that of the direct form. The convergence behavior is verified by looking at how fast the estimated system approaches the true system. Here we use the Itakura distance as the measure of closeness between the estimated and the true system. We show that the Itakura distance associated with the CRLS-SA algorithm approaches zero faster than that associated with the direct form RLS algorithm. The CRLS-SA algorithm is applied in this dissertation to general linear prediction, to the direct adaptive computation of the LSF and their representation in quantized form using a split vector quantization (VQ) approach, and to the detection and tracking of the frequencies in signals consisting of multiple sinusoids in noise. / Ph. D.

In this thesis, a class of combinatorial optimization methods rooted in statistical mechanics and their use in signal processing applications will be discussed. The thesis consists of two separate parts. The first part deals with the rationale for my work and also covers the background information necessary to put the second part, which consists of a number of papers, in context. There are (at least) two sides to an optimization problem---the problem statement arising from an application or a design and the selection of an algorithm to solve the problem. In this work the problem statements are practical problems, of combinatorial nature, frequently encountered in signal processing and the algorithms of choice are annealing based algorithms, founded in statistical mechanics. From my work, it is my experience that solving a particular problem often leads to new developments on the part of the algorithm which, in turn, open up possibilities to apply the modified algorithm to a new set of problems, leading to a continuously improving algorithm and a growing field of applications. The included papers deal with the application of annealing optimization methods to the problems of configuring active noise and vibration control systems, digital filter design and adaptive filtering. They also describe the successive development of a highly efficient entropy-directed deterministic annealing (EDDA) optimization algorithm detailed in the final paper.

Ever since the birth of the telephony system, the problem with echoes, arising from impedance mismatch in 2/4-wire hybrids, or acoustic echoes where a loudspeaker signal is picked up by a closely located microphone, has been ever present. The removal of these echoes is crucial in order to achieve an acceptable audio quality for conversation. Today, the perhaps most common way for echo removal is through cancellation, where an adaptive filter is used to produce an estimated replica of the echo which is then subtracted from the echo-infested signal. Echo cancellation in practice requires extensive control of the filter adaptation process in order to obtain as rapid convergence as possible while also achieving robustness towards disturbances. Moreover, despite the rapid advancement in the computational capabilities of modern digital signal processors there is a constant demand for low-complexity solutions that can be implemented using low power and low cost hardware. This thesis presents low-complexity solutions for echo cancellation related to both the actual filter adaptation process itself as well as for controlling the adaptation process in order to obtain a robust system. Extensive simulations and evaluations using real world recorded signals are used to demonstrate the performance of the proposed solutions.

[pt] Este trabalho tem como finalidade explorar uma técnica de redução do erro de estimativas da resposta de freqüência discreta do canal geradas por símbolos piloto em sistemas de transmissão OFDM (Orthogonal Frequency Division Multiplexing). Nesta técnica, uma transformação linear projeta o vetor que contem as estimativas obtidas inicialmente no subespaço em que a verdadeira resposta de freqüência do canal tem que estar, resultando em uma redução da variância do erro das estimativas. A aplicação conjunta desta técnica com filtragem adaptativa para a estimação da resposta de freqüência do canal também está no contexto desta dissertação. Os resultados dos experimentos são analisados em termos da taxa de erro de bit média obtida e da convergência dos algoritmos adaptaivos empregados nas etapas de estimação de canal no receptor. / [en] This work a technique for error reduction in estimates of the discrete channel frequency response obtained with aid of pilot symbols in OFDM (Orthogonal Frequency Division Multiplexing) transmission systems. In this technique projects the vector that contains the initial discrete channel frequency response estimate is projected into the subspace where the true channel frequency response has to lye, yielding a new channel estimate with a reduced error variance. The joint application of this technique with adaptive filtering for channel estimation is also developed herein. The performance of the proposed methods is analyzed in terms of the mean bit error rate achieved and of the convergence of the adaptive channel estimation algorithms used in the receiver.

<p>The novel method of this thesis work is based on using quadrature filters to estimate an orientation tensor and to use the advantage of tensor information to control 3D adaptive filters. The adaptive filters are applied to enhance the Magnetic Resonance Angiography (MRA) images. The tubular structures are extracted from the volume dataset by using the quadrature filters. The idea of developing adaptive filtering in this thesis work is to enhance the volume dataset and suppress the image noise. Then the output of the adaptive filtering can be a clean dataset for segmentation of blood vessel structures to get appropriate volume visualization.</p><p>The local tensors are used to create the control tensor which is used to control adaptive filters. By evaluation of the tensor eigenvalues combination, the local structures like tubular structures and stenosis structures are extracted from the dataset. The method has been evaluated with synthetic objects, which are vessel models (for segmentation), and onion like synthetic object (for enhancement). The experimental results are shown on clinical images to validate the proposed method as well.</p>