Este trabalho tem a finalidade de analisar algumas técnicas de processamento do sinal mioelétrico, de forma a possibilitar uma posterior implementação de um circuito, que reconheça este sinal e apresente como saída um sinal de controle a ser utilizado em sistemas de reabilitação. Foram simuladas e avaliadas três técnicas de filtragem para o sinal mioelétrico, a fim de atenuar a interferência dos principais ruídos que corrompem este sinal. As técnicas avaliadas foram: filtragem digital clássica; cancelamento de ruído adaptativo e reconstrução do sinal por meio das componentes obtidas pela transformada wavelet. Também foi implementado e analisado um sistema simplificado de reconhecimento dos padrões para este sinal, realizado por meio de uma rede neural artificial, em que foi aplicado em sua entrada o próprio sinal mioelétrico e não suas características obtidas por processamentos matemáticos. Diante dos resultados obtidos os canceladores de ruído adaptativos apresentaram melhores resultados com relação às outras técnicas de filtragem. Apesar de não ter sido adequada para a filtragem, a transformada wavelet mostrou-se uma poderosa ferramenta de análise de sinais, em virtude da sua característica multiresolução. A técnica utilizada para reconhecer os padrões do sinal mostrou bons resultados com os sinais analisados.; This work has the purpose to analyze some techniques for myoelectric signal processing...

Esta tese estuda modificações no sistema operacional Linux para a arquitetura x86 da Intel, com a finalidade de aumentar o desempenho, nessa plataforma, das aplicações de processamento digital de sinais em tempo real. Inicialmente são estabelecidos requisitos para um sistema operacional voltado para o processamento digital de sinais. Os requisitos são estabelecidos com base na estrutura dos programas de processamento digital de sinais em tempo real e nas situações mais comuns nesse tipo de processamento. Um fator chave quando se trata de processamento em tempo real é a latência para colocar o aplicativo em execução. Nesse contexto, o trabalho desenvolve um modelo para a latência no tratamento das interrupções externas no Linux. Usando esse modelo é desenvolvido um método para medir as várias componentes dessa latência, método este baseado na colocação de marcadores de tempo no núcleo do Linux. O método de medida proposto é usado para medir a latência do Linux no tratamento de uma interrupção externa em três condições diferentes. O estudo finaliza propondo, implementando e testando alterações no Linux que visam melhorar o desempenho, desse sistema, em aplicações de processamento digital de sinais em tempo real.; This work studies modifications in the Linux operating system for the Intel x86 architecture...

Nos últimos anos muito tem se pesquisado na área de arquiteturas paralelas de computadores, devido ao fato da melhora de desempenho nas arquiteturas sequenciais não estar acompanhando as necessidades crescentes de capacidade de processamento. Entre as arquiteturas paralelas, um grupo que tem recebido especial atenção por parte dos pesquisadores é o de redes neurais. Uma rede neural é uma arquitetura baseada em paralelismo massivo, na interconexão de numerosos elementos simples de processamento segundo uma determinada topologia e com uma regra de aprendizagem. As redes neurais tem tido grande importância na área de reconhecimento de padrões e diversas aplicações em reconhecimento de caracteres, imagem e voz tem sido desenvolvidas. Outra área de aplicação das redes neurais é o processamento de sinais. A característica de adaptabilidade das redes neurais torna-as apropriadas à utilização em aplicações, onde as características do sinal, ou do meio, são variáveis ou não totalmente conhecidas, como filtros adaptativos. O objetivo deste trabalho é mostrar as aplicações de redes neurais nesta área. Na primeira parte do trabalho foram implementadas aplicações de redes neurais à filtragem utilizando diversas topologias e modelos de neurônios. Os modelos implementados são aqui apresentados juntamente com os resultados das simulações. A segunda parte do trabalho consiste na aplicação de um modelo de redes neurais a um problema bem específico...

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica; Patients with neuromuscular and cardiorespiratory diseases need to be monitored continuously. This constant monitoring gives rise to huge amounts of multivariate data which need to be processed as soon as possible, so that their most relevant features can be extracted. The field of parallel processing, an area from the computational sciences, comes naturally as a way to provide an answer to this problem. For the parallel processing to succeed it is necessary to adapt the pre-existing signal processing algorithms to the modern architectures of computer systems with several processing units. In this work parallel processing techniques are applied to biosignals, connecting the area of computer science to the biomedical domain. Several considerations are made on how to design parallel algorithms for signal processing, following the data parallel paradigm. The emphasis is given to algorithm design, rather than the computing systems that execute these algorithms. Nonetheless, shared memory systems and distributed memory systems are mentioned in the present work. Two signal processing tools integrating some of the parallel programming concepts mentioned throughout this work were developed. These tools allow a fast and efficient analysis of long-term biosignals. The two kinds of analysis are focused on heart rate variability and breath frequency...

This thesis investigates the design and the analysis of acoustic signal processing algorithms in reverberant rooms. Reverberation poses a major challenge to acoustic signal processing problems. It degrades speech intelligibility and causes many acoustic algorithms that process sound to perform poorly. Current solutions to the reverberation problem frequently only work in lightly reverberant environments. There is need to improve the reverberant performance of acoustic algorithms.¶ The approach of this thesis is to explore how the intrinsic properties of reverberation can be exploited to improve acoustic signal processing algorithms. A general approach to soundfield modelling using statistical room acoustics is applied to analyze the reverberant performance of several acoustic algorithms. A model of the underlying structure of reverberation is incorporated to create a new method of soundfield reproduction.¶ Several outcomes resulting from this approach are: (i) a study of how more sound capture with directional microphones and beamformers can improve the robustness of acoustic equalization, (ii) an assessment of the extent to which source tracking can improve accuracy of source localization, (iii) a new method of soundfield reproduction for reverberant rooms...

Deep cerebellar nuclei (DCN) neurons generate the final output of cerebellum and receive abundant modulatory serotonergic inputs from brainstem neurons. The aim of this present study was to elucidate the influence of serotonin on signal processing performed by DCN neurons. Since signal processing is determined by the interplay between intrinsic and synaptic properties, the impact of serotonin on intrinsic as well as synaptic properties was investigated. To this end whole-cell patch clamp recordings were performed in rat cerebellar slices. Serotonin caused a persistent membrane depolarization at current clamp recordings, which was mediated by an increase of tonic cationic currents and a concomitant decrease of tonic potassium currents. At the same time, serotonin influenced the waveform of action potentials that showed a reduced depolarization slope and peak amplitude, both indicating a reduced availability of voltage-gated sodium channels. However, serotonin showed a complicated effect at dynamic clamp recordings where the neuronal response depended on the average activity level before drug application. Spike rate was reduced by serotonin for depolarized high activity states and unaltered or slightly increased for hyperpolarized low activity states. The spike timing precision was not altered...

Journal Paper; Unitary similarity transformations furnish a powerful vehicle for generating infinite generic classes of signal analysis and processing tools based on concepts different from time, frequency, and scale. Implementation of these new tools involves simply preprocessing the signal by a unitary transformation, performing standard processing techniques on the transformed signal, and then (in some cases) transforming the resulting output. The resulting unitarily equivalent systems can focus on the critical signal characteristics in large classes of signals and, hence, prove useful for representing and processing signals that are not well matched by current techniques. As specific examples of this procedure, we generalize linear time-invariant systems, orthonormal basis and frame decompositions, and joint time-frequency and time-scale distributions. These applications illustrate the utility of the unitary equivalence concept for uniting seemingly disparate approaches proposed in the literature.

Journal Paper; The textbook has traditionally been the fundamental tool of university teaching. The text both serves as the repository of facts and information and provides the recommended structure and sequence for teaching and learning the material. Today, textbooks can be in traditional paper form or electronically available over the World Wide Web. However, the material in domains like signal processing changes rapidly as new theory, applications, and hardware continually come on the scene. In some ways, having a textbook as a courseâ s main tool actually impedes course and curriculum development.

Conference Paper; Wavelet-domain hidden Markov models (HMMs) provide a powerful new approach for statistical modeling and processing of wavelet coefficients. In addition to characterizing the statistics of individual wavelet coefficients, HMMs capture some of the key interactions between wavelet coefficients. However, as HMMs model an increasing number of wavelet coefficient interactions, HMM-based signal processing becomes increasingly complicated. In this paper, we propose a new approach to HMMs based on the notion of context. By modeling wavelet coefficient inter-dependencies via contexts, we retain the approximation capabilities of HMMs, yet substantially reduce their complexity. To illustrate the power of this approach, we develop new algorithms for signal estimation and for efficient synthesis of nonGaussian, long-range-dependent network traffic.

Journal Paper; Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the statistical dependencies and non-Gaussian Statistics encountered with real-world signals. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal modes. Efficient Expectation Maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classificaion, and detection.

Conference Paper; The SPIB (Signal Processing Information Base) project at Rice University is discussed. This information base will provide the signal processing researcher and the applications engineer with data, programs, and papers that can be accessed immediately through Internet. An overview of the initial contents of the information base is presented, and the various ways in which it may be accessed are described

Journal Paper; Nonlinearities are often encountered in the analysis and processing of real-world signals. In this paper, we introduce two new structures for nonlinear signal processing. The new structures simplify the analysis, design, and implementation of nonlinear filters and can be applied to obtain more reliable estimates of higher-order statistics. Both structures are based on a two-step decomposition consisting of a linear orthogonal signal expansion followed by scalar polynomial transformations of the resulting signal coefficients. Most existing approaches to nonlinear signal processing characterize the nonlinearity in the time domain or frequency domain; in our framework any orthogonal signal expansion can be employed. In fact, there are good reasons for characterizing nonlinearity using more general signal representations like the wavelet transform. Wavelet expansions often provide very concise signal representation and thereby can simplify subsequent nonlinear analysis and processing. Wavelets also enable local nonlinear analysis and processing in both time and frequency, which can be advantageous in non-stationary problems. Moreover, we show that the wavelet domain offers significant theoretical advantages over classical time or frequency domain approaches to nonlinear signal analysis and processing.

Journal Paper; Nonlinearities are often encountered in the analysis and processing of real-world signals. We introduce two new structures for nonlinear signal processing. The new structures simplify the analysis, design, and implementation of nonlinear filters and can be applied to obtain more reliable estimates of higher order statistics. Both structures are based on a two-step decomposition consisting of a linear orthogonal signal expansion followed by scalar polynomial transformations of the resulting signal coefficients. Most existing approaches to nonlinear signal processing characterize the nonlinearity in the time domain or frequency domain; in our framework any orthogonal signal expansion can be employed. In fact, there are good reasons for characterizing nonlinearity using more general signal representations like the wavelet expansion. Wavelet expansions often provide very concise signal representations and thereby can simplify subsequent nonlinear analysis and processing. Wavelets also enable local nonlinear analysis and processing in both time and frequency, which can be advantageous in nonstationary problems. Moreover, we show that the wavelet domain offers significant theoretical advantages over classical time or frequency domain approaches to nonlinear signal analysis and processing.

The Signal Processing Group (Department of Signal Theory and Communications, University Carlos III, Madrid, Spain) offers the expertise of its members in the automatic processing of biomedical signals. The main advantages in this technology are the decreased cost, the time saved and the increased reliability of the results. Technical cooperation for the research and development with internal and external funding is sought.

With increasing environmental concern for energy conservation and mitigating climate change, next generation smart networks are bound to provide improved performance in terms of security, reliability, and energy efficiency. For instance, future smart networks will work in highly complex and dynamic environments and will have distributed nodes that need to interact with each other and may also interact with an energy provider in order to improve their performance. In this context, advanced signal processing tools such as game theory and distributed transmit beamforming can yield tremendous performance gains in terms of energy efficiency for demand management and signal trans-mission in smart networks. The central theme of this dissertation is the modeling of energy usage behavior of self-seeking distributed nodes in smart networks. The thesis mainly looks into two key areas of smart networks: 1) smart grid networks and 2) wireless sensor networks, and contains: an analytical framework of the economics of electric vehicle charging in smart grids in an energy constrained environment; a study of a consumer-centric energy management scheme for encouraging the consumers in a smart grid to voluntarily take part in energy management; an outage management scheme for efficiently curtailing energy from the consumers in smart grids in the event of a power outage; a comprehensive study of power control of sensors in a wireless sensor network using game theory and distributed transmit beamforming; and finally...

Esta dissertação apresenta um sistema para processamento digital de sinais através de dispositivos de hardware reconfigurável. Uma implementação do algoritmo FFT foi adotada como meio para avaliar o desempenho da arquitetura proposta para o sistema. O processamento digital de sinais tradicionalmente tem um alto custo computacional, pois os algoritmos são implementados em software, o que pode não atender as restrições de tempo de aplicações reais. O objetivo principal deste trabalho é desenvolver uma arquitetura para adquirir os sinais através de módulos de aquisição de dados distribuídos em uma rede e processá-los usando um FPGA. Um microcontrolador da FreeScale Semiconductors'MARCA REGISTRADA' foi adotado como módulo de aquisição de dados, executando um sistema operacional de tempo real (RTOS) para garantir os requisitos temporais. Foi implementado o processador soft-core NIOS 2 da Altera'MARCA REGISTRADA' executando também um RTOS com recursos de comunicação em rede, incluindo um periférico escrito em VHDL para o processamento da FFT usando uma estrutura de pipeline baseada em estágios e comunicação direta ao barramento do processador. A versão em hardware do algoritmo obteve uma redução de até 2000 vezes no tempo de processamento da FFT comparado com a mesma versão implementada em software...

Humans have always seen the world in color but only recently have we been able to generate vast quantities of color images with such ease. In the last three decades, we have seen a rapid and enormous transition from grayscale images to color ones. Today, we are exposed to color images on a daily basis in print, photographs, television, computer displays, and cinema movies, where color now plays a vital role in the advertising and dissemination of information throughout the world. Color monitors, printers, and copiers now dominate the office and home environments, with color becoming increasingly cheaper and easier to generate and reproduce. Color demands have soared in the marketplace and are projected to do so for years to come. With this rapid progression, color and multispectral properties of images are becoming increasingly crucial to the field of image processing, often extending and/or replacing previously known grayscale techniques. We have seen the birth of color algorithms that range from direct extensions of grayscale ones, where images are treated as three monochrome separations, to more sophisticated approaches that exploit the correlations among the color bands, yielding more accurate results. Hence, it is becoming increasingly necessary for the signal processing community to understand the fundamental differences between color and grayscale imaging. There are more than a few extensions of concepts and perceptions that must be understood in order to produce successful research and products in the color world.; Personal use of this material is permitted. However...

B-Splines are a useful tool in signal processing, and are widely used in the analysis of two and three-dimensional images. B-Splines provide a continuous representation of the signal, image, or volume, which is useful for interpolation, resampling, noise removal, and differentiation - all important steps in many signal processing algorithms. These splines are defined entirely by an array of coefficients that is roughly the same size as the original signal and of values in the same order of magnitude, making storage and representation trivial. What is not trivial, however, is the quick calculation and processing of those coefficients, especially for very large data. As technology improves in fields such as medical imaging, algorithms that use B-Splines will need to process increasingly higher resolution images and voxel volumes. New implementations are needed to make use of modern parallel architectures to keep these algorithms practical. This thesis presents a library for performing many common B-Splines operations in CUDA, the parallel programming framework for NVIDIA GPUs, and analyzes the considerations necessary when implementing a large-scale parallel version of such a well-established sequential algorithm. This library is meant to be used both by C++ programs as well as algorithms implemented in MATLAB without requiring significant changes. Significant speedups are obtained using this library to perform various common B-Spline image processing operations (as much as 30x for some)...

Multimodal signal processing has gained a lot of significance in recent years due to advances in computer technology as well as more sophisticated sensors being available. One example is the joint processing of audio and video signals in a variety of applications. This paper serves as a broad introduction to the special session on "Audio-Video Signal Processing and its Applications". The paper reviews current trends and developments in joint audio-video (AV) signal processing and gives an overview of current issues in theory and application in this area. We focus on speech processing, person authentication, and affective sensing as examples. An overview of available AV data corpora is given.