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Implementation and evaluation of echo cancellation algorithmsSankaran, Sundar G. 13 February 2009 (has links)
Echo in telephones is generally undesirable but inevitable. There are two possible sources of echo in a telephone system. The impedance mismatch in hybrids generates network (electric) echo. The acoustic coupling between loudspeaker and microphone, in hands-free telephones, produces acoustic echo. Echo cancelers are used to control these echoes.
In this thesis, we analyze the Least Mean Squares (LMS), Normalized LMS (NLMS), Recursive Least Squares (RLS), and Subband NLMS (SNLMS) algorithms, and evaluate their performance as acoustic and network echo cancelers. The algorithms are compared based on their convergence rate, steady state echo return loss (ERL), and complexity of implementation. While LMS is simple, its convergence rate is dependent on the eigenvalue spread of the signal. In particular, it converges slowly with speech as input. This problem is mitigated in NLMS. The complexity of NLMS is comparable to that of LMS. The convergence rate of RLS is independent of the eigenvalue spread, and it has the fastest convergence. On the other hand, RLS is highly computation intensive. Among the four algorithms considered here, SNLMS has the least complexity of implementation, as well as the slowest rate of convergence.
Switching between the NLMS and SNLMS algorithms is used to achieve fast convergence with low computational requirements. For a given computational power, it is shown that switching between algorithms can give better performance than using either of the two algorithms exclusively, especially in rooms with long reverberation times.
We also discuss various implementation issues associated with an integrated echo cancellation system, such as double-talk detection, finite precision effects, nonlinear processing, and howling detection and control. The use of a second adaptive filter is proposed, to reduce near-end ambient noise. Simulation results indicate that this approach can reduce the ambient noise by about 20 dB.
A configuration is presented for the real time single-chip DSP implementation of acoustic and network echo cancelers, and an interface between the echo canceler and the telephone is proposed. Finally, some results obtained from simulations and implementations of individual modules, on the TMS320C31 and ADSP 2181 processors, are reported. The real time NLMS DSP implementations provide 15 dB of echo return loss. / Master of Science
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Estratégias incrementais em combinação de filtros adaptativos. / Incremental strategies in combination of adaptive filters.Wilder Bezerra Lopes 14 February 2012 (has links)
Neste trabalho uma nova estratégia de combinação de filtros adaptativos é apresentada e estudada. Inspirada por esquemas incrementais e filtragem adaptativa cooperativa, a combinação convexa usual de filtros em paralelo e independentes é reestruturada como uma configuração série-cooperativa, sem aumento da complexidade computacional. Dois novos algoritmos são projetados utilizando Recursive Least-Squares (RLS) e Least-Mean-Squares (LMS) como subfiltros que compõem a combinação. Para avaliar a performance da estrutura incremental, uma análise de média quadrática é realizada. Esta é feita assumindo que os combinadores têm valores fixos, de forma a permitir o estudo da universalidade da estrutura desacoplada da dinâmica do supervisor. As simulações realizadas mostram uma boa concordância com o modelo teórico obtido. / In this work a new strategy for combination of adaptive filters is introduced and studied. Inspired by incremental schemes and cooperative adaptive filtering, the standard convex combination of parallel-independent filters is rearranged into a series-cooperative configuration, while preserving computational complexity. Two new algorithms are derived employing Recursive Least-Squares (RLS) and Least-Mean-Squares (LMS) algorithms as the component filters. In order to assess the performance of the incremental structure, tracking and steady-state mean-square analysis is derived. The analysis is carried out assuming the combiners are fixed, so that the universality of the new structure may be studied decoupled from the supervisor\'s dynamics. The resulting analytical model shows good agreement with simulation results.
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Algoritmo adaptativo tipo-LMS com soma do erro / LMS-like algorithm with adaptive sum of the errorNahuz, Charles Silva 11 March 2016 (has links)
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Previous issue date: 2016-03-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / In this paper, implemented a new lter similar to the LMS, but, with a coast function based in the sum of the error. As a result, we obtain a very simple function, producing a rapid convergence and a small mismatch when compared with the LMS algorithm and other algorithms. The adaptive lter is based on non-linear functions such as estimation of the gradient of a surface performance. We use the gradient algorithm to update the weights. this update is based on high-order statistics to obtain information about the signs involved in the process, in order to improve the performace of the adaptive lter. Derive the equations based on Taylor series of non-linear functions, to achieve the criteria that ensures their convergence. We also do a weight vector covariance study in steady state and determine the equations that calculate the time constants in an adaptive process. Here the algorithm proposed, which uses a cost function and were made simulacoes Monte Carlo with real signals to validate the theory presented. In this role the α coefficients have been optimized to provide increased stability and better performance in its convergence speed. / Neste trabalho, implementamos um novo filtro semelhante ao LMS, porém, com uma função de custo baseada na soma do erro. Como resultado, obtemos uma função bastante simples, produzindo uma rápida convergência e um pequeno desajuste quando comparado com o algoritmo LMS e com outros algoritmos. O filtro adaptativo é baseado em funções não lineares como estimativa do gradiente de uma superfície de desempenho. Utilizamos o gradiente do algoritmo para atualização dos pesos. Essa atualização baseia-se nas estatísticas de alta ordem para obtenção de informações sobre os sinais envolvidos no processo, com o objetivo de melhorar a performance do filtro adaptativo. As equações foram derivadas e baseadas em séries de Taylor das funções não lineares, para obtenção dos critérios que garante a sua convergência. Também fazemos um estudo da covariância do vetor peso em regime estacionário e determinamos as equações que calculam as constantes de tempo em um processo adaptativo. Apresentamos o algoritmo proposto, que utiliza uma função de custo onde foram feitas simulações de Monte Carlo com sinais reais para validar a teoria apresentada. Nessa função os coe cientes αk foram otimizados para dar maior estabilidade e melhor desempenho na sua velocidade de convergência.
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Non-Linear Adaptive Bayesian Filtering for Brain Machine InterfacesLi, Zheng January 2010 (has links)
<p>Brain-machine interfaces (BMI) are systems which connect brains directly to machines or computers for communication. BMI-controlled prosthetic devices use algorithms to decode neuronal recordings into movement commands. These algorithms operate using models of how recorded neuronal signals relate to desired movements, called models of tuning. Models of tuning have typically been linear in prior work, due to the simplicity and speed of the algorithms used with them. Neuronal tuning has been shown to slowly change over time, but most prior work do not adapt tuning models to these changes. Furthermore, extracellular electrical recordings of neurons' action potentials slowly change over time, impairing the preprocessing step of spike-sorting, during which the neurons responsible for recorded action potentials are identified.</p>
<p></p>
<p>This dissertation presents a non-linear adaptive Bayesian filter and an adaptive spike-sorting method for BMI decoding. The adaptive filter consists of the n-th order unscented Kalman filter and Bayesian regression self-training updates. The unscented Kalman filter estimates desired prosthetic movements using a non-linear model of tuning as its observation model. The model is quadratic with terms for position, velocity, distance from center of workspace, and velocity magnitude. The tuning model relates neuronal activity to movements at multiple time offsets simultaneously, and the movement model of the filter is an order n autoregressive model.</p>
<p>To adapt the tuning model parameters to changes in the brain, Bayesian regression self-training updates are performed periodically. Tuning model parameters are stored as probability distributions instead of point estimates. Bayesian regression uses the previous model parameters as priors and calculates the posteriors of the regression between filter outputs, which are assumed to be the desired movements, and neuronal recordings. Before each update, filter outputs are smoothed using a Kalman smoother, and tuning model parameters are passed through a transition model describing how parameters change over time. Two variants of Bayesian regression are presented: one uses a joint distribution for the model parameters which allows analytical inference, and the other uses a more flexible factorized distribution that requires approximate inference using variational Bayes.</p>
<p>To adapt spike-sorting parameters to changes in spike waveforms, variational Bayesian Gaussian mixture clustering updates are used to update the waveform clustering used to calculate these parameters. This Bayesian extension of expectation-maximization clustering uses the previous clustering parameters as priors and computes the new parameters as posteriors. The use of priors allows tracking of clustering parameters over time and facilitates fast convergence.</p>
<p>To evaluate the proposed methods, experiments were performed with 3 Rhesus monkeys implanted with micro-wire electrode arrays in arm-related areas of the cortex. Off-line reconstructions and on-line, closed-loop experiments with brain-control show that the n-th order unscented Kalman filter is more accurate than previous linear methods. Closed-loop experiments over 29 days show that Bayesian regression self-training helps maintain control accuracy. Experiments on synthetic data show that Bayesian regression self-training can be applied to other tracking problems with changing observation models. Bayesian clustering updates on synthetic and neuronal data demonstrate tracking of cluster and waveform changes. These results indicate the proposed methods improve the accuracy and robustness of BMIs for prosthetic devices, bringing BMI-controlled prosthetics closer to clinical use.</p> / Dissertation
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Desenvolvimento de um sistema de manutenção inteligente embarcadoGonçalves, Luiz Fernando January 2011 (has links)
A evolução tecnológica dos sensores, da eletrônica e dos sistemas embarcados melhorou o desempenho, a confiabilidade e a robustez dos sistemas assim como as atividades de manutenção, em especial, as de manutenção proativa. Estes avanços tecnológicos possibilitaram uma nova visão sobre as práticas de manutenção existentes. A expansão das áreas de processamento de sinais e inteligência artificial proporcionou novas abordagens aos sistemas de controle, promovendo a criação de novos modelos de confiabilidade e disponibilidade de equipamentos e sistemas. Além disso, aumentou a precisão no reconhecimento de padrões de falhas, ampliou a avaliação e o diagnóstico de danos em equipamentos e sistemas, e adicionou inteligência aos sistemas de manutenção existentes. Diversas técnicas de processamento de sinais (tais como a transformada de Fourier), de inteligência artificial (as redes neurais artificiais e a lógica nebulosa, por exemplo) e de filtragem adaptativa (os filtros adaptativos, como exemplo) já são utilizadas com sucesso para detectar e prevenir falhas em vários tipos de equipamentos. Os sistemas de manutenção que fazem uso das técnicas de processamento de sinais e inteligência artificial, em conjunto, por exemplo, são conhecidos como sistemas de manutenção inteligente. Através desses sistemas, é possível monitorar as condições físicas, tomar decisões, efetuar ações de manutenção e fornecer diagnósticos precisos de falhas. Este trabalho aborda a implementação de um sistema de manutenção inteligente embarcado que usa a transformada wavelet packet e os mapas auto-organizáveis ou os filtros adaptativos para detectar, classificar e prever falhas em atuadores elétricos. A idéia principal deste trabalho é determinar qual destas ferramentas, mapas auto-organizáveis ou filtros adaptativos, é a mais adequada para o embarque. Espera-se com a implantação embarcada desse sistema de manutenção, por exemplo, evitar falhas nos atuadores e promover uma maior reutilização de peças. / The technological evolution of sensors, electronics, and embedded systems has improved the performance, reliability and robustness of systems as well the maintenance activities, especially the proactive maintenance. These technological advances have provided a new view about the existing maintenance practices. The expansion of signal processing and artificial intelligence has provided new approaches in industrial control systems leading to the proposal of new reliability and availability models for equipments and systems. Moreover, it has increased the precision in failure pattern recognition, has extended the assessment and diagnosis of damages in equipments and systems, and has added intelligence to existing maintenance systems. Several techniques for signal processing (such as Fourier transform), artificial intelligence (artificial neural networks, for example) and adaptive filtering (adaptive filters, as an example) are already used successfully to detect and prevent failures in several kinds of equipments. The maintenance systems that use, for example, the techniques for signal processing and artificial intelligence together are known as intelligent maintenance systems. It is possible to control the physical conditions, make decisions, perform maintenance activities and do accurate diagnosis of failures using those systems. This work presents the implementation of an embedded intelligent maintenance system using wavelet packet analysis and self organizing maps or adaptive filters for detection, classification, and prediction of failures in electrical actuators. The main idea is to determine which of these tools, self-organizing maps or adaptive filters, is the most suitable for the implementation in embedded systems. It is expected that with the implementation of this maintenance system, failures in actuators are avoided, and that a greater reuse of parts is achieved.
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Desenvolvimento de um sistema de manutenção inteligente embarcadoGonçalves, Luiz Fernando January 2011 (has links)
A evolução tecnológica dos sensores, da eletrônica e dos sistemas embarcados melhorou o desempenho, a confiabilidade e a robustez dos sistemas assim como as atividades de manutenção, em especial, as de manutenção proativa. Estes avanços tecnológicos possibilitaram uma nova visão sobre as práticas de manutenção existentes. A expansão das áreas de processamento de sinais e inteligência artificial proporcionou novas abordagens aos sistemas de controle, promovendo a criação de novos modelos de confiabilidade e disponibilidade de equipamentos e sistemas. Além disso, aumentou a precisão no reconhecimento de padrões de falhas, ampliou a avaliação e o diagnóstico de danos em equipamentos e sistemas, e adicionou inteligência aos sistemas de manutenção existentes. Diversas técnicas de processamento de sinais (tais como a transformada de Fourier), de inteligência artificial (as redes neurais artificiais e a lógica nebulosa, por exemplo) e de filtragem adaptativa (os filtros adaptativos, como exemplo) já são utilizadas com sucesso para detectar e prevenir falhas em vários tipos de equipamentos. Os sistemas de manutenção que fazem uso das técnicas de processamento de sinais e inteligência artificial, em conjunto, por exemplo, são conhecidos como sistemas de manutenção inteligente. Através desses sistemas, é possível monitorar as condições físicas, tomar decisões, efetuar ações de manutenção e fornecer diagnósticos precisos de falhas. Este trabalho aborda a implementação de um sistema de manutenção inteligente embarcado que usa a transformada wavelet packet e os mapas auto-organizáveis ou os filtros adaptativos para detectar, classificar e prever falhas em atuadores elétricos. A idéia principal deste trabalho é determinar qual destas ferramentas, mapas auto-organizáveis ou filtros adaptativos, é a mais adequada para o embarque. Espera-se com a implantação embarcada desse sistema de manutenção, por exemplo, evitar falhas nos atuadores e promover uma maior reutilização de peças. / The technological evolution of sensors, electronics, and embedded systems has improved the performance, reliability and robustness of systems as well the maintenance activities, especially the proactive maintenance. These technological advances have provided a new view about the existing maintenance practices. The expansion of signal processing and artificial intelligence has provided new approaches in industrial control systems leading to the proposal of new reliability and availability models for equipments and systems. Moreover, it has increased the precision in failure pattern recognition, has extended the assessment and diagnosis of damages in equipments and systems, and has added intelligence to existing maintenance systems. Several techniques for signal processing (such as Fourier transform), artificial intelligence (artificial neural networks, for example) and adaptive filtering (adaptive filters, as an example) are already used successfully to detect and prevent failures in several kinds of equipments. The maintenance systems that use, for example, the techniques for signal processing and artificial intelligence together are known as intelligent maintenance systems. It is possible to control the physical conditions, make decisions, perform maintenance activities and do accurate diagnosis of failures using those systems. This work presents the implementation of an embedded intelligent maintenance system using wavelet packet analysis and self organizing maps or adaptive filters for detection, classification, and prediction of failures in electrical actuators. The main idea is to determine which of these tools, self-organizing maps or adaptive filters, is the most suitable for the implementation in embedded systems. It is expected that with the implementation of this maintenance system, failures in actuators are avoided, and that a greater reuse of parts is achieved.
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Desenvolvimento de um sistema de manutenção inteligente embarcadoGonçalves, Luiz Fernando January 2011 (has links)
A evolução tecnológica dos sensores, da eletrônica e dos sistemas embarcados melhorou o desempenho, a confiabilidade e a robustez dos sistemas assim como as atividades de manutenção, em especial, as de manutenção proativa. Estes avanços tecnológicos possibilitaram uma nova visão sobre as práticas de manutenção existentes. A expansão das áreas de processamento de sinais e inteligência artificial proporcionou novas abordagens aos sistemas de controle, promovendo a criação de novos modelos de confiabilidade e disponibilidade de equipamentos e sistemas. Além disso, aumentou a precisão no reconhecimento de padrões de falhas, ampliou a avaliação e o diagnóstico de danos em equipamentos e sistemas, e adicionou inteligência aos sistemas de manutenção existentes. Diversas técnicas de processamento de sinais (tais como a transformada de Fourier), de inteligência artificial (as redes neurais artificiais e a lógica nebulosa, por exemplo) e de filtragem adaptativa (os filtros adaptativos, como exemplo) já são utilizadas com sucesso para detectar e prevenir falhas em vários tipos de equipamentos. Os sistemas de manutenção que fazem uso das técnicas de processamento de sinais e inteligência artificial, em conjunto, por exemplo, são conhecidos como sistemas de manutenção inteligente. Através desses sistemas, é possível monitorar as condições físicas, tomar decisões, efetuar ações de manutenção e fornecer diagnósticos precisos de falhas. Este trabalho aborda a implementação de um sistema de manutenção inteligente embarcado que usa a transformada wavelet packet e os mapas auto-organizáveis ou os filtros adaptativos para detectar, classificar e prever falhas em atuadores elétricos. A idéia principal deste trabalho é determinar qual destas ferramentas, mapas auto-organizáveis ou filtros adaptativos, é a mais adequada para o embarque. Espera-se com a implantação embarcada desse sistema de manutenção, por exemplo, evitar falhas nos atuadores e promover uma maior reutilização de peças. / The technological evolution of sensors, electronics, and embedded systems has improved the performance, reliability and robustness of systems as well the maintenance activities, especially the proactive maintenance. These technological advances have provided a new view about the existing maintenance practices. The expansion of signal processing and artificial intelligence has provided new approaches in industrial control systems leading to the proposal of new reliability and availability models for equipments and systems. Moreover, it has increased the precision in failure pattern recognition, has extended the assessment and diagnosis of damages in equipments and systems, and has added intelligence to existing maintenance systems. Several techniques for signal processing (such as Fourier transform), artificial intelligence (artificial neural networks, for example) and adaptive filtering (adaptive filters, as an example) are already used successfully to detect and prevent failures in several kinds of equipments. The maintenance systems that use, for example, the techniques for signal processing and artificial intelligence together are known as intelligent maintenance systems. It is possible to control the physical conditions, make decisions, perform maintenance activities and do accurate diagnosis of failures using those systems. This work presents the implementation of an embedded intelligent maintenance system using wavelet packet analysis and self organizing maps or adaptive filters for detection, classification, and prediction of failures in electrical actuators. The main idea is to determine which of these tools, self-organizing maps or adaptive filters, is the most suitable for the implementation in embedded systems. It is expected that with the implementation of this maintenance system, failures in actuators are avoided, and that a greater reuse of parts is achieved.
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UM ALGORITMO TIPO RLS BASEADO EM SUPERFÍCIES NÃO QUADRÁTICAS / A ALGORITHM TYPE RLS BASED IN NON QUADRATIC SURFACESSilva, Cristiane Cristina Sousa da 19 July 2013 (has links)
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Previous issue date: 2013-07-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In adaptive filtering many adaptive filter are based on the mean square error method (MSE). These filters were developed to improve convergence spedd with a lower misadjustment. The least mean square (LMS) and the recursive least square (RLS) algorithms have been the hallmark of adaptive filtering. In this work we develop adaptive algorithms based on the even powers of the error inspired in the recursive lest square (RLS) algorithm. Namely recursive nom quadratic (RNQ) algorithm. The ideas is based on Widrow s least mean square fourth (LMF) algorithm. Fisrt we derive equations based on a singal even power of the error in order to obtain criterions that guarantee convergence. We also determine equations that measure the misadjustment and the time constant of the adaptive process of the RNQ algorithm. We work also, toward making the algorithm less sensitive to the size of the error in na alternative direction, by proposing a cost function which is a sum of the even powers of the error. This second approach bring the error explicitly to the RLS algorithm formulation by proposing a new cost function that preserves the measnsquare-error (MSE) solution, but allows for the exploitation of higher order moments of the error to speedup the converge of the algorithm. The main goal this work is to create form first principles (new cost functions ) a mechanism to include instantaneous error information in the RLS algorithm, make it track better, and allow for the design of the forgetting factor. As we will see the key aspecto of our approach is to include the error in the Kalman gain that effectively controls the speed of adaptation of the RLS algorithm. / Em filtragem adaptativa, vários filtros são baseados no método do erro quadrático médio (do inglês, MSE- mean squared error ) e muitos desses foram desenvolvidos para obter uma convergência rápida com um menos desajuste. Os algoritmos mínimos quadrático médio (do inglês, LMS- least mean square ) e mínimos quadrados recursivos (do inglês, RLS- recursive least square ) foram um marco em filtragem adaptativa. Nesse trabalho apresentamos o desenvolvimento de uma família de algoritmos adaptativos baseados nas potências pares do erro, inspirado na dedução do algoritmo RLS padrão. Chamaremos esses novos algoritmos de recursivo não-quadrático (RNQ). A ideia básica é baseada na função de custo apresentada por Widrow no algoritmo mínimo quarto médio ( do inglês, LMF least mean square fourth). Inicialmente derivamos equações baseados em uma potência par do erro para obter critérios que garantam a convergência. Determinamos também, equações que definem o desajuste e o tempo de aprendizagem do processo de adaptação do algoritmo RNQ baseado em potência para arbitrária. Trabalhamos também, no sentido de tornar o algoritmo menos sensível ao tamanho do erro numa direção alternativa, propondo uma função de custo baseado na soma das potências pares do erro. Essa segunda abordagem torna explícito o papel do erro na formulação do RLS ao propor uma nova função de custo que preserve a solução MSE, mas permite a utilização dos momentos de alta ordem do erro para aumentar a velocidade de convergência do algoritmo. O principal objetivo do nosso trabalho é criar a partir dos primeiros princípios (novas funções de custo) um mecanismo para incluir informações de erro instantâneo no algoritmo RLS e torná-lo um seguidor melhor. Assim, o aspecto-chave dessa nova abordagem é incluir o erro no ganho de Kalman que controla efetivamente a velocidade de adaptação do algoritmo de RLS.
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Adaptive signal processing for multichannel sound using high performance computingLorente Giner, Jorge 02 December 2015 (has links)
[EN] The field of audio signal processing has undergone a major development in recent years. Both the consumer and professional marketplaces continue to show growth in audio applications such as immersive audio schemes that offer optimal listening experience, intelligent noise reduction in cars or improvements in audio teleconferencing or hearing aids. The development of these applications has a common interest in increasing or improving the number of discrete audio channels, the quality of the audio or the sophistication of the algorithms. This often gives rise to problems of high computational cost, even when using common signal processing algorithms, mainly due to the application of these algorithms to multiple signals with real-time requirements. The field of High Performance Computing (HPC) based on low cost hardware elements is the bridge needed between the computing problems and the real multimedia signals and systems that lead to user's applications. In this sense, the present thesis goes a step further in the development of these systems by using the computational power of General Purpose Graphics Processing Units (GPGPUs) to exploit the inherent parallelism of signal processing for multichannel audio applications.
The increase of the computational capacity of the processing devices has been historically linked to the number of transistors in a chip. However, nowadays the improvements in the computational capacity are mainly given by increasing the number of processing units and using parallel processing. The Graphics Processing Units (GPUs), which have now thousands of computing cores, are a representative example. The GPUs were traditionally used to graphic or image processing, but new releases in the GPU programming environments such as CUDA have allowed the use of GPUS for general processing applications. Hence, the use of GPUs is being extended to a wide variety of intensive-computation applications among which audio processing is included. However, the data transactions between the CPU and the GPU and viceversa have questioned the viability of the use of GPUs for audio applications in which real-time interaction between microphones and loudspeakers is required. This is the case of the adaptive filtering applications, where an efficient use of parallel computation in not straightforward. For these reasons, up to the beginning of this thesis, very few publications had dealt with the GPU implementation of real-time acoustic applications based on adaptive filtering. Therefore, this thesis aims to demonstrate that GPUs are totally valid tools to carry out audio applications based on adaptive filtering that require high computational resources. To this end, different adaptive applications in the field of audio processing are studied and performed using GPUs. This manuscript also analyzes and solves possible limitations in each GPU-based implementation both from the acoustic point of view as from the computational point of view. / [ES] El campo de procesado de señales de audio ha experimentado un desarrollo importante en los últimos años. Tanto el mercado de consumo como el profesional siguen mostrando un crecimiento en aplicaciones de audio, tales como: los sistemas de audio inmersivo que ofrecen una experiencia de sonido óptima, los sistemas inteligentes de reducción de ruido en coches o las mejoras en sistemas de teleconferencia o en audífonos. El desarrollo de estas aplicaciones tiene un propósito común de aumentar o mejorar el número de canales de audio, la propia calidad del audio o la sofisticación de los algoritmos. Estas mejoras suelen dar lugar a sistemas de alto coste computacional, incluso usando algoritmos comunes de procesado de señal. Esto se debe principalmente a que los algoritmos se suelen aplicar a sistemas multicanales con requerimientos de procesamiento en tiempo real. El campo de la Computación de Alto Rendimiento basado en elementos hardware de bajo coste es el puente necesario entre los problemas de computación y los sistemas multimedia que dan lugar a aplicaciones de usuario. En este sentido, la presente tesis va un paso más allá en el desarrollo de estos sistemas mediante el uso de la potencia de cálculo de las Unidades de Procesamiento Gráfico (GPU) en aplicaciones de propósito general. Con ello, aprovechamos la inherente capacidad de paralelización que poseen las GPU para procesar señales de audio y obtener aplicaciones de audio multicanal.
El aumento de la capacidad computacional de los dispositivos de procesado ha estado vinculado históricamente al número de transistores que había en un chip. Sin embargo, hoy en día, las mejoras en la capacidad computacional se dan principalmente por el aumento del número de unidades de procesado y su uso para el procesado en paralelo. Las GPUs son un ejemplo muy representativo. Hoy en día, las GPUs poseen hasta miles de núcleos de computación. Tradicionalmente, las GPUs se han utilizado para el procesado de gráficos o imágenes. Sin embargo, la aparición de entornos sencillos de programación GPU, como por ejemplo CUDA, han permitido el uso de las GPU para aplicaciones de procesado general. De ese modo, el uso de las GPU se ha extendido a una amplia variedad de aplicaciones que requieren cálculo intensivo. Entre esta gama de aplicaciones, se incluye el procesado de señales de audio. No obstante, las transferencias de datos entre la CPU y la GPU y viceversa pusieron en duda la viabilidad de las GPUs para aplicaciones de audio en las que se requiere una interacción en tiempo real entre micrófonos y altavoces. Este es el caso de las aplicaciones basadas en filtrado adaptativo, donde el uso eficiente de la computación en paralelo no es sencillo. Por estas razones, hasta el comienzo de esta tesis, había muy pocas publicaciones que utilizaran la GPU para implementaciones en tiempo real de aplicaciones acústicas basadas en filtrado adaptativo. A pesar de todo, esta tesis pretende demostrar que las GPU son herramientas totalmente válidas para llevar a cabo aplicaciones de audio basadas en filtrado adaptativo que requieran elevados recursos computacionales. Con este fin, la presente tesis ha estudiado y desarrollado varias aplicaciones adaptativas de procesado de audio utilizando una GPU como procesador. Además, también analiza y resuelve las posibles limitaciones de cada aplicación tanto desde el punto de vista acústico como desde el punto de vista computacional. / [CAT] El camp del processament de senyals d'àudio ha experimentat un desenvolupament important als últims anys. Tant el mercat de consum com el professional segueixen mostrant un creixement en aplicacions d'àudio, com ara: els sistemes d'àudio immersiu que ofereixen una experiència de so òptima, els sistemes intel·ligents de reducció de soroll en els cotxes o les millores en sistemes de teleconferència o en audiòfons. El desenvolupament d'aquestes aplicacions té un propòsit comú d'augmentar o millorar el nombre de canals d'àudio, la pròpia qualitat de l'àudio o la sofisticació dels algorismes que s'utilitzen. Això, sovint dóna lloc a sistemes d'alt cost computacional, fins i tot quan es fan servir algorismes comuns de processat de senyal. Això es deu principalment al fet que els algorismes se solen aplicar a sistemes multicanals amb requeriments de processat en temps real. El camp de la Computació d'Alt Rendiment basat en elements hardware de baix cost és el pont necessari entre els problemes de computació i els sistemes multimèdia que donen lloc a aplicacions d'usuari. En aquest sentit, aquesta tesi va un pas més enllà en el desenvolupament d'aquests sistemes mitjançant l'ús de la potència de càlcul de les Unitats de Processament Gràfic (GPU) en aplicacions de propòsit general. Amb això, s'aprofita la inherent capacitat de paral·lelització que posseeixen les GPUs per processar senyals d'àudio i obtenir aplicacions d'àudio multicanal.
L'augment de la capacitat computacional dels dispositius de processat ha estat històricament vinculada al nombre de transistors que hi havia en un xip. No obstant, avui en dia, les millores en la capacitat computacional es donen principalment per l'augment del nombre d'unitats de processat i el seu ús per al processament en paral·lel. Un exemple molt representatiu són les GPU, que avui en dia posseeixen milers de nuclis de computació. Tradicionalment, les GPUs s'han utilitzat per al processat de gràfics o imatges. No obstant, l'aparició d'entorns senzills de programació de la GPU com és CUDA, han permès l'ús de les GPUs per a aplicacions de processat general. D'aquesta manera, l'ús de les GPUs s'ha estès a una àmplia varietat d'aplicacions que requereixen càlcul intensiu. Entre aquesta gamma d'aplicacions, s'inclou el processat de senyals d'àudio. No obstant, les transferències de dades entre la CPU i la GPU i viceversa van posar en dubte la viabilitat de les GPUs per a aplicacions d'àudio en què es requereix la interacció en temps real de micròfons i altaveus. Aquest és el cas de les aplicacions basades en filtrat adaptatiu, on l'ús eficient de la computació en paral·lel no és senzilla. Per aquestes raons, fins al començament d'aquesta tesi, hi havia molt poques publicacions que utilitzessin la GPU per implementar en temps real aplicacions acústiques basades en filtrat adaptatiu. Malgrat tot, aquesta tesi pretén demostrar que les GPU són eines totalment vàlides per dur a terme aplicacions d'àudio basades en filtrat adaptatiu que requereixen alts recursos computacionals. Amb aquesta finalitat, en la present tesi s'han estudiat i desenvolupat diverses aplicacions adaptatives de processament d'àudio utilitzant una GPU com a processador. A més, aquest manuscrit també analitza i resol les possibles limitacions de cada aplicació, tant des del punt de vista acústic, com des del punt de vista computacional. / Lorente Giner, J. (2015). Adaptive signal processing for multichannel sound using high performance computing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/58427 / TESIS
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Control and Estimation Theory in Ranging ApplicationsJanuary 2020 (has links)
abstract: For the last 50 years, oscillator modeling in ranging systems has received considerable
attention. Many components in a navigation system, such as the master oscillator
driving the receiver system, as well the master oscillator in the transmitting system
contribute significantly to timing errors. Algorithms in the navigation processor must
be able to predict and compensate such errors to achieve a specified accuracy. While
much work has been done on the fundamentals of these problems, the thinking on said
problems has not progressed. On the hardware end, the designers of local oscillators
focus on synthesized frequency and loop noise bandwidth. This does nothing to
mitigate, or reduce frequency stability degradation in band. Similarly, there are not
systematic methods to accommodate phase and frequency anomalies such as clock
jumps. Phase locked loops are fundamentally control systems, and while control
theory has had significant advancement over the last 30 years, the design of timekeeping
sources has not advanced beyond classical control. On the software end,
single or two state oscillator models are typically embedded in a Kalman Filter to
alleviate time errors between the transmitter and receiver clock. Such models are
appropriate for short term time accuracy, but insufficient for long term time accuracy.
Additionally, flicker frequency noise may be present in oscillators, and it presents
mathematical modeling complications. This work proposes novel H∞ control methods
to address the shortcomings in the standard design of time-keeping phase locked loops.
Such methods allow the designer to address frequency stability degradation as well
as high phase/frequency dynamics. Additionally, finite-dimensional approximants of
flicker frequency noise that are more representative of the truth system than the
tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in
a wide variety of operating environments, novel Banks of Adaptive Extended Kalman
Filters are used to address both stochastic and dynamic uncertainty. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020
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