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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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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.

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Passive radar detects and ranges targets by receiving signals which are reflected off targets. Communication transmissions are generally used, however, theoretically any signal with a suitable ambiguity function may be used. The exploitation of an existing transmitter and the removal of emissions allow passive radars to act as a complementary sensor which is useful in environments where conventional active radar is not well suited. Such environments are in covert operations and in situations where a low cost or spectrally efficient solution is required. Most developed passive radars employ intensive signal processing and use application specific equipment to achieve detection. The high-end processors and receiver equipment, however, detract from some of the inherent advantages in the passive radar architecture. These include the lower cost and power requirements achieved by removing transmitter hardware. This study investigates the challenges faced when removing application-specific and high end components from the system and replacing them with low-cost alternatives. Solutions to these challenges are presented and validated by designing and evaluating a radar using these principles. It was found that the major limitation in passive radar is the dynamic range of the receiver. While processing the signals was, and is, a significant challenge, be implemented on a low-cost, low-power embedded processor. This was achieved by asserting a few limitations to the configuration, exploiting the subsequently generated redundancy, and taking advantage of the parallelism by using general purpose graphics processing.. Even on this processor, the system was able to run in real time and able to detect targets up to 91 km (bistatic range of 195 km) from the radar. / Dissertation (MEng)--University of Pretoria, 2016. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

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