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Space-Time Adaptive Processing is a signal processing technique that uses an adaptive array to help remove nonhomogeneous data points from a dataset. Since the early 1970s, STAP has been used in radar systems for their ability to "filter clutter, interference and jamming signals. One major flaw with early STAP radar systems is the reliance on non-robust estimators to estimate the noise condition. When even a single outlier is present, the earliest STAP radar systems would break down, causing the target to be missed. Many algorithms have been developed to successfully estimate the noise condition of the dataset when outliers are present. As recently as 2007, a STAP radar processing system based on Adaptive Complex Projection Statistics has been proposed and successfully"filters out the noise condition even when outliers are present. However, this algorithm requires the data to be entirely real. Radar data, which consists of amplitude and phase, is complex valued. Therefore, it must be converted into its rectangular components before processing can commence. This introduces many additional processing steps which significantly increase the computing time. The STAP radar algorithm of this thesis overcomes the problems with early radar systems. It is based on the Complex GM Wiener Filter (CGMWF) with the Minimum Covariance Determinant (MCD) for outlier detection. The robustness of the conventional Wiener "lter is enhanced by robust Huber Estimator, and using the MCD enables processing entirely in the complex domain. This results in a STAP radar algorithm with a breakdown point of nearly 35% and that enables processing entirely in the complex domain for fewer computing steps. / Master of Science

Performing non-destructive testing (NDT) on materials is a helpful tool for maintenance and quality control because the materials are not destroyed or disturbed; ultrasound imaging is one type of NDT. Ultrasound imaging of heterogeneous materials contains many echoes from the material itself. These echoes come from changes in the acoustic impedance, i.e. changes in the relation between the density and the sound speed of the material. However, these echoes will show speckle characteristics in images, making it hard to detect any defects in the imaged material. In this work, a method of suppressing this speckle noise is proposed. The proposed method is a 2D Wiener filter, which with the help of an image of the healthy material models changes in the material when a new image is taken later. The filter models the changes of the speckle noise between images of a defected- and healty material and then supresses the speckle from the image with defects. The filter works well on the artificial images used in this work but have yet to be tested on actual data. A version of a weighted moving average filter was also looked into, but this filter did not produce usable results.

The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of this decomposition produces a reduced rank filter with many benefits, for example, improved performance. This dissertation extends knowledge of the MWF in four areas. The first area is rank and sample support compression. This dissertation examines, under a wide variety of conditions, the size of the adaptive subspace required by the MWF (i.e., the rank) as well as the required number of training samples. Comparisons are made with other algorithms such as the eigenvector-based principal components algorithm. The second area investigated in this dissertation concerns "soft stops", i.e., the insertion of diagonal loading into the MWF. Several methods for inserting loading into the MWF are described, as well as methods for choosing the amount of loading. The next area investigated is MWF rank selection. The MWF will outperform the classical Wiener filter when the rank is properly chosen. This dissertation presents six approaches for selecting MWF rank. The algorithms are compared to one another and an overall design space taxonomy is presented. Finally, as digital modelling capabilities become more sophisticated there is emerging interest in augmenting adaptive processing algorithms to incorporate prior knowledge. This dissertation presents two methods for augmenting the MWF, one based on linear constraints and a second based on non-zero weight vector initialization. Both approaches are evaluated under ideal and perturbed conditions. Together the research described in this dissertation increases the utility and robustness of the multistage Wiener filter. The analysis is presented in the context of adaptive array processing, both spatial array processing and space-time adaptive processing for airborne radar. The results, however, are applicable across the entire spectrum of adaptive signal processing applications. / Ph. D.

The blind source separation problem has received a great deal of attention in previous years. The aim of this problem is to estimate a set of original source signals from a set of linearly mixed signals through any number of signal processing techniques. While many methods exist that attempt to solve the blind source separation problem, a new technique is being used that uniquely separates audio sources as they are received from a microphone array. In this thesis a new algorithm is proposed that that utilizes the ICA algorithm in conjunction with a filtering technique that separates source signals and then removes sources of interference so that a signal of interest can be accurately tracked. Experimental results will compare a common blind source separation technique to the new algorithm and show that the new algorithm can detect a signal of interest and accurately track it as it moves through an anechoic environment. / Master of Science

A eletromiografia consiste no estudo do movimentos dos músculos através dos sinais elétricos emitidos pelos mesmos. Esses sinais são captados por meio de eletrodos (de surpefície ou de agulha), sendo muito suscetíveis a variações e interferências não relacionadas diretamente com o movimento muscular (ruídos). Visando obter dados qualitativamente confiáveis, o processamento digital de sinais fornece como ferramentas os filtros ótimos e adaptativos, que são utilizados quando o sinal desajado está contaminado por ruído. Com a finalidade de diminuir o ruído presente no sinal eletromiográfico, foram implementados os filtros de wiener e wiener adaptativo ao algoritmo LMS (least mean square), tendo a análise da relação sinal/ruído dos sinais obtidos demonstrado que não há diferença significativa entre os filtros. Como conclusão, no tratamento de sinais eletromiográficos, pode-se aplicar tanto o filtro de wiener como o de wiener adaptativo, observando-se que este último apresenta a vantagem de consumir menos tempo de processamento. / Electromyography is the study of muscle moviments through the electrical signal that they emanate. These signals are detected with eletrodes (surface or needle), where variations and interferences not directly related with movement are present (noises). Digital signal processing provides optimal and adaptative filters with the aim to get qualitative reliable data. The filters are used when desired signal is corrupted by noise. With the purpose of noise reduction in electromyography signal, wiener and adaptative wiener filters (the last one with least mean square algorithm) were implemented. However, signal-to-noise ratio analysis gave evidence that there is no significative difference between both the filters. As conclusion, in electromygraphy signal treatment, wiener and adaptative wiener filters could be used, with the only difference that the last one takes less processing time.

Este trabalho tem por objetivo a investigação do comportamento de equipamentos de radiodiagnóstico em termos da qualidade da imagem produzida e a subseqüente aplicação desses resultados na otimização do desempenho de esquemas computacionais de auxílio ao diagnóstico, também conhecidos como esquemas CAD (do inglês, Computer-Aided Diagnosis). A principal meta consiste no desenvolvimento de técnicas de pré-processamento para imagens mamográficas digitalizadas que as realçasse de acordo com as características e limitações dos equipamentos utilizados na sua aquisição. A proposta está dividida em duas etapas. Na primeira, foram determinadas as características relativas tanto à resolução espacial como à resolução de contraste de diversos equipamentos mamográficos, avaliadas respectivamente pelas funções de transferência óptica e espectros de Wiener do ruído. Isto permitiu, numa segunda etapa, o desenvolvimento de um filtro digital específico para o pré-processamento de diferentes conjuntos de mamogramas digitais, separados de acordo com os equipamentos utilizados no processo de aquisição. Dessa forma, cada imagem mamográfica teve sua qualidade melhorada de acordo com as características do equipamento que a gerou, determinadas na primeira etapa. Essas imagens, depois de realçadas, foram utilizadas em um esquema CAD previamente desenvolvido, onde pôde ser observada uma melhora em até 12% no seu desempenho quando comparado aos resultados obtidos com imagens mamográficas não realçadas. / The purpose of this work is to evaluate the quality of radiological equipment and their images in order to use these evaluations to improve the performance of a computer-aided diagnosis (CAD) scheme. The mean idea is about the development of image processing techniques to enhance digital mammograms according to the characteristics of the X-ray unit used for image acquisition. This work is basically divided in two parts. In the first one, it were determined the characteristics related to spatial and contrast resolution of several mammographic equipment, evaluated respectively from the optical transfer function and noise Wiener spectrum. This evaluation allowed, in a second part, the development of a preprocessing technique to enhance different set of digital mammographic images, gathered according to the equipment used on its acquisition process. Thus, each mammographic image had its quality improved in conformity with the characteristics of the equipment used on its acquisition, determined in the first part of this work. These images, after the enhancement process, were used on a previously developed CAD scheme. It was observed an improvement of 12% on the CAD performance using pre-processed mammograms compared to the results obtained when using non-enhanced mammographic images.

A eletromiografia consiste no estudo do movimentos dos músculos através dos sinais elétricos emitidos pelos mesmos. Esses sinais são captados por meio de eletrodos (de surpefície ou de agulha), sendo muito suscetíveis a variações e interferências não relacionadas diretamente com o movimento muscular (ruídos). Visando obter dados qualitativamente confiáveis, o processamento digital de sinais fornece como ferramentas os filtros ótimos e adaptativos, que são utilizados quando o sinal desajado está contaminado por ruído. Com a finalidade de diminuir o ruído presente no sinal eletromiográfico, foram implementados os filtros de wiener e wiener adaptativo ao algoritmo LMS (least mean square), tendo a análise da relação sinal/ruído dos sinais obtidos demonstrado que não há diferença significativa entre os filtros. Como conclusão, no tratamento de sinais eletromiográficos, pode-se aplicar tanto o filtro de wiener como o de wiener adaptativo, observando-se que este último apresenta a vantagem de consumir menos tempo de processamento. / Electromyography is the study of muscle moviments through the electrical signal that they emanate. These signals are detected with eletrodes (surface or needle), where variations and interferences not directly related with movement are present (noises). Digital signal processing provides optimal and adaptative filters with the aim to get qualitative reliable data. The filters are used when desired signal is corrupted by noise. With the purpose of noise reduction in electromyography signal, wiener and adaptative wiener filters (the last one with least mean square algorithm) were implemented. However, signal-to-noise ratio analysis gave evidence that there is no significative difference between both the filters. As conclusion, in electromygraphy signal treatment, wiener and adaptative wiener filters could be used, with the only difference that the last one takes less processing time.

Este trabalho tem por objetivo a investigação do comportamento de equipamentos de radiodiagnóstico em termos da qualidade da imagem produzida e a subseqüente aplicação desses resultados na otimização do desempenho de esquemas computacionais de auxílio ao diagnóstico, também conhecidos como esquemas CAD (do inglês, Computer-Aided Diagnosis). A principal meta consiste no desenvolvimento de técnicas de pré-processamento para imagens mamográficas digitalizadas que as realçasse de acordo com as características e limitações dos equipamentos utilizados na sua aquisição. A proposta está dividida em duas etapas. Na primeira, foram determinadas as características relativas tanto à resolução espacial como à resolução de contraste de diversos equipamentos mamográficos, avaliadas respectivamente pelas funções de transferência óptica e espectros de Wiener do ruído. Isto permitiu, numa segunda etapa, o desenvolvimento de um filtro digital específico para o pré-processamento de diferentes conjuntos de mamogramas digitais, separados de acordo com os equipamentos utilizados no processo de aquisição. Dessa forma, cada imagem mamográfica teve sua qualidade melhorada de acordo com as características do equipamento que a gerou, determinadas na primeira etapa. Essas imagens, depois de realçadas, foram utilizadas em um esquema CAD previamente desenvolvido, onde pôde ser observada uma melhora em até 12% no seu desempenho quando comparado aos resultados obtidos com imagens mamográficas não realçadas. / The purpose of this work is to evaluate the quality of radiological equipment and their images in order to use these evaluations to improve the performance of a computer-aided diagnosis (CAD) scheme. The mean idea is about the development of image processing techniques to enhance digital mammograms according to the characteristics of the X-ray unit used for image acquisition. This work is basically divided in two parts. In the first one, it were determined the characteristics related to spatial and contrast resolution of several mammographic equipment, evaluated respectively from the optical transfer function and noise Wiener spectrum. This evaluation allowed, in a second part, the development of a preprocessing technique to enhance different set of digital mammographic images, gathered according to the equipment used on its acquisition process. Thus, each mammographic image had its quality improved in conformity with the characteristics of the equipment used on its acquisition, determined in the first part of this work. These images, after the enhancement process, were used on a previously developed CAD scheme. It was observed an improvement of 12% on the CAD performance using pre-processed mammograms compared to the results obtained when using non-enhanced mammographic images.