A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

In this work, programmable analog techniques using floating-gate transistors have been developed to design precision analog circuits, low-power signal processing primitives and adaptive systems that learn on-chip. Traditional analog implementations lack programmability with the result that issues such as mismatch are corrected at the expense of area. Techniques have been proposed that use floating-gate transistors as an integral part of the circuit of interest to provide both programmability and the ability to correct for mismatch. Traditionally, signal processing has been performed in the digital domain with analog circuits handling the interface with the outside world. Such a partitioning of responsibilities is inefficient as signal processing involves repeated multiplication and addition operations that are both very power efficient in the analog domain. Using programmable analog techniques, fundamental signal processing primitives such as multipliers have been developed in a low-power fashion while preserving accuracy. This results in a paradigm shift in signal processing. A co-operative analog/digital signal processing framework is now possible such that the partitioning of tasks between the analog and digital domains is performed in a power efficient manner. Complex signal processing tasks such as adaptive filtering that learn the weight coefficients are implemented by exploiting the non-linearities inherent with floating-gate programming. The resulting floating-gate synapses are compact, low-power and offer the benefits of non-volatile weight storage. In summary, this research involves developing techniques for improving analog circuit performance and in developing power-efficient techniques for signal processing and on-chip learning.

As the size of an adaptive antenna array grows, the system is able to resist interference signals of increasing bandwidth. This is a result of the transmit pattern gain increasing, which raises the target's return power, and a greater number of degrees of freedom. However, once the interference signal decorrelates completely from one channel to the next, increasing array size will cease to improve detection capability. The use of tapped delay-line processing to improve correlation between channels has been studied for smaller arrays with single element antennas, but previous analyses have not considereded larger systems that are partitioned into subarrays. This thesis quantifies the effect that subarrays have on performance, as measured by the interference bandwidth that can be handled, and explains how tapped delay-line processing can maintain the ability to detect targets in an environment with high bandwidth interference. The analysis begins by deriving equations to estimate the half-power bandwidth of an array with no taps. Then we find that a single delay with optimal spacing is sufficient to completely restore performance if the interference angle is known exactly. However, in practice, the tap spacing will never be optimal because this angle will not be known exactly, so further consideration is given to this non-ideal case and possible solutions for arbitrary interference scenarios are presented. Simulations indicate that systems with multiple taps have more tolerance to increasing interference bandwidth and unknown directions of arrival. Finally, the tradeoffs between ideal and practical configurations are explained and suggestions are given for the design of real-world systems.

When computational resources are limited, especially multipliers, distributed arithmetic (DA) is used in lieu of the typical multiplier-based filtering structures. However, DA is not well suited for adaptive applications. The bottleneck is updating the memory table. Several attempts have been done to accelerate updating the memory, but at the expense of additional memory usage and of convergence speed. To develop an adaptive DA filter with an uncompromised convergence rate, the memory table must be fully updated. In this research, an efficient method for fully updating a DA memory table is proposed. The proposed update method is based on exploiting the temporal locality of the stored data and subexpression sharing. The proposed update method reduces the computational workload and requires no additional memory resources. DA using the proposed update method is called conjugate distributed arithmetic. Filters can also be constructed from analog components. Often, for lower precision computations, analog circuits use less power and less chip area than their digital counterparts. However, digital components are often used because of their ease of reprogrammability. Achieving such reprogrammability in analog is possible, but at the expense of additional chip area. A reprogrammable mixed-signal DA finite impulse response (FIR) filter is proposed to address the issues with reprogrammable analog FIR filters that are constructing compact reprogrammable filtering structures, non-symmetric and imprecise filter coefficients, inconsistent sampling of the input data, and input sample data corruption. These issues are successfully addressed using distributed arithmetic, digital registers, and epots. Also, a mixed-signal DA second-order section (SOS), which is used as the building block for higher order infinite impulse response filters, was proposed. The type of issues with an analog SOS filter are similar to those of an analog FIR filter, which are the lack of a compact reprogrammable filtering structure, the imprecise filter coefficients, the inconsistent sampling of the data, and the corruption of the data samples. These issues are successfully addressed using distributed arithmetic and digital registers.

Orientador: Eurípedes Guilherme de Oliveira Nóbrega / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-16T17:08:16Z (GMT). No. of bitstreams: 1 AlvesJunior_MarcoAntoniodeOliveira_D.pdf: 2426154 bytes, checksum: bc1d1ecd78ca881b519a4e56c9383c8f (MD5) Previous issue date: 2010 / Resumo: Esta monografia apresenta uma arquitetura para aplicação de Controle Tolerante a Falhas, seguindo uma abordagem de dupla malha de realimentação. A primeira malha apresenta um controlador regular, e a segunda segue uma estratégia de controle adaptativa baseada em rede neural, que faz uso de um mecanismo de ajustes de pesos em tempo real. O primeiro controlador foi escolhido como um projeto de controlador baseado em norma H?, objetivando estabilizar o sistema e garantir o bom desempenho na presença de erros de modelagem e distúrbios externos. O controlador tolerante a falhas, que atua complementarmente à malha externa, é o controlador que usa a técnica neuroadaptativa. A rede neural possui estados internos recorrentes, usando uma superfície de deslizamento para adaptar os seus pesos, de modo a acomodar as possíveis falhas. Também apresenta robustez contra as perturbações externas, além da capacidade do controlador regular. Uma nova topologia de dinâmica da rede neural, com estados internos recursivos e aprendizado em tempo-real, é proposta, e a estabilidade do sistema é provada com base em uma função de Lyapunov e em requisitos predefinidos. Para avaliar o método, foi usado um modelo matemático de um veículo aéreo não tripulado do tipo quadrirrotor. Os resultados simulados, com o sistema submetido a vários tipos de condições de falha, são apresentados, mostrando o bom desempenho da configuração proposta / Abstract: This monograph presents an architecture scheme for Fault Tolerant Control applications, following a dual-loop controller design approach, where the first closed loop is a regular controller and the second one is based on a neural network adaptive control strategy, with on-line adjustment of the weights. The first controller, which was here chosen as an H? norm designed controller, aims stabilize the system, and guarantee a good performance in presence of modeling errors and external disturbances. The fault tolerant controller, acting complementarily to the external loop, is the one using the neuro-adaptive technique. Its design is based on recurrent internal states, using a sliding surface to adapt the weights of the neural network, in order to accommodate the system faults, but also with a robust effect which includes correcting all external perturbations, beyond the capacity of the regular controller. A new neural network dynamic topology, with internal recursive states and on-line learning algorithm, is proposed, and its stability is proved based on a Lyapunov function and predefined requirements. To assess the method, an unmanned quad rotor flying vehicle is modeled, and the respective controllers designed. Results based on numerical simulation, with the system submitted to several different fault conditions, are presented, showing a good performance of the proposed configuration / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica

Orientador: João Marcos Travassos Romano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-09T16:56:34Z (GMT). No. of bitstreams: 1 Suyama_Ricardo_D.pdf: 28793623 bytes, checksum: cf06bdad425402b4624bbd169bfad249 (MD5) Previous issue date: 2007 / Resumo: O problema de separação cega de fontes (BSS - Blind Source Separation) vem despertando o interesse de um número crescente de pesquisadores. Esse destaque é devido, em grande parte, à formulação abrangente do problema, que torna possível o uso das técnicas desenvolvidas no contexto de BSS nas mais diversas áreas de aplicação. O presente trabalho tem como objetivo propor novos métodos de solução do problema de separação cega de fontes, nos casos de mistura convolutiva e mistura não-linear. Para o primeiro caso propomos um método baseado em predição não-linear, cujo intuito é eliminar o caráter convolutivo da mistura e, dessa forma, separar os sinais utilizando ferramentas bem estabelecidas no contexto de misturas lineares sem memória. No contexto de misturas não-lineares, propomos uma nova metodologia para separação de sinais em um modelo específico de mistura denominado modelo com não-linearidade posterior (PNL - Post Nonlinear ). Com o intuito de minimizar problemas de convergência para mínimos locais no processo de adaptação do sistema separador, o método proposto emprega um algoritmo evolutivo como ferramenta de otimização, e utiliza um estimador de entropia baseado em estatísticas de ordem para avaliar a função custo. A eficácia de ambos os métodos é verificada através de simulações em diferentes cenários / Abstract: The problem of blind source separation (BSS) has attracted the attention of agrowing number of researchers, mostly due to its potential applications in a significant number of different areas. The objective of the present work is to propose new methods to solve the problem of BSS in the cases of convolutive mixtures and nonlinear mixtures. For the first case, we propose a new method based on nonlinear prediction filters. The nonlinear structure is employed to eliminate the convolutive character of the mixture, hence converting the problem into an instantaneous mixture, to which several well established tools may be used to recover the sources. In the context of nonlinear mixtures, we present a new methodology for signal separation in the so-called post-nonlinear mixing models (PNL). In order to avoid convergence to local minima, the proposed method uses an evolutionary algorithm to perform the optimization of the separating system. In addition to that, we employ an entropy estimator based on order-statistics to evaluate the cost function. The effectiveness of both methods is assessed through simulations in different scenarios / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

Orientador: Dalton Soares Arantes / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-04T09:25:15Z (GMT). No. of bitstreams: 1 Cardoso_FabbryccioAkkazzhaChavesMachado_M.pdf: 6296555 bytes, checksum: 2190ae93699da79f815c9e5199564080 (MD5) Previous issue date: 2004 / Resumo: Nesta tese são investigados e propostos diferentes algoritmos adaptativos para antenas inteligentes e equalização em receptores de usuário no enlace de descida de sistemas WCDMA/HSDPA. Considerando o tema de equalização de canal, propõe-se um algoritmo adaptativo de retro-propagação com subsistemas concorrentes. Esta técnica de equalização explora características específicas de um sinal WCDMA e foi inspirada na técnica de equalização cega concorrente desenvolvida por Fernando C. C. de Castro et al. Com relação a antenas inteligentes, propõe-se um esquema adaptativo de recepção espaço-temporal de múltiplos alvos, denominado MT-STR, que emprega K processadores de antenas e um com binador de sinais dos vários alvos, ou fingers, do receptor. Esta técnica de recepção foi testada num cenário de soft-handover bastante severo, envolvendo muitos sinais interferentes. Ainda no escopo de antenas inteligentes, estudou-se também o efeito do acoplamento mútuo e do descasamento de amplitude e fase em arranjos co-lineares de antenas dipolo de meia onda / Abstract: This dissertation investigates and proposes different algorithms for smart antennas and channel equalization aiming at WCDMA HSDPA downlink receivers. Considering the channel equalization issue, we propose an adaptive back-propagation algorithm with concurrent sub-systems. This equalization technique exploits particular characteristics of a WCDMA downlink signal and is based on the concurrent blind equalization technique developed by Fernando C. C. de Castro et al. For smart antennas, a multi-target space-time receiver (MT-STR) which uses K array processors and an adaptive ratio combiner is proposed. This technique has been tested under a severe soft-handover scenario assuming many interferers. The effects of mutual coupling and amplitude phase mismatch on co-linear half-wave dipole arrays for WCDMA down1ink receivers are also investigated. / Mestrado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

It is known that a matched filter is optimal for a signal corrupted by Gaussian noise. In a wireless environment, the received signal may be corrupted by Gaussian noise and a variety of other channel disturbances: cochannel interference, multiple access interference, large and small-scale fading, etc. Adaptive filtering is the usual approach to mitigating this channel distortion. Existing adaptive filtering techniques usually attempt to minimize the mean square error (MSE) of some aspect of the received signal, with respect to the desired aspect of that signal. Adaptive minimization of MSE does not always guarantee minimization of bit error rate (BER). The main focus of this research involves estimation of the probability density function (PDF) of the received signal; this PDF estimate is used to adaptively determine a solution that minimizes BER. To this end, a new adaptive procedure called the Minimum BER Estimation (MBE) algorithm has been developed. MBE shows improvement over the Least Mean Squares (LMS) algorithm for most simulations involving interference and in some multipath situations. Furthermore, the new algorithm is more robust than LMS to changes in algorithm parameters such as stepsize and window width. / Master of Science

University of Macau / Faculty of Science and Technology / Department of Mathematics

[pt] Nesta dissertação, diversos algoritmos adaptativos para processamento de sinais com seleção de dados são desenvolvidos e estudados, com o objetivo de resolver dois problemas diferentes. O primeiro problema envolve ambientes com sistemas esparsos, onde uma função penalidade é incorporada na função de custo para aproveitar a esparsidade do modelo. Nesta perspectiva, são propostos três algoritmos com função penalidade ajustável, o primeiro baseado na função penalidade l1 é denominado SM-NLMS com atração para zero e função penalidade ajustável (ZA-SM-NLMS-ADP). O segundo algoritmo está baseado na função penalidade log-sum e o terceiro na função penalidade l0 , denominados SM-NLMS com atração ponderada para zero e função de penalidade ajustável (RZA-SM-NLMS-ADP) e SM-NLMS com atração para zero exponencial e função de penalidade ajustável (EZA-SM-NLMSADP), respectivamente. Além disso, foi desenvolvida uma análise estatística do algoritmo SM-NLMS com uma função penalidade genérica, obtendo expressões matemáticas para o erro médio quadrático em estado estacionário. O segundo problema abordado, considera algoritmos adaptativos não lineares baseados em funções de kernels. Neste contexto, são desenvolvidos dois algoritmos com seleção de dados, o algoritmo SM-NKLMS e o algoritmo SM-KAP, os quais possuem a capacidade de limitar o crescimento da estrutura criada pelas funções de kernels, tratando um dos maiores problemas que surge quando se utilizam algoritmos baseados em kernels. Os algoritmos baseados em kernels foram testados para predição de séries temporais. Também é realizada uma análise estatística do algoritmo SM-NKLMS. As simulações mostram que os algoritmos desenvolvidos superam os algoritmos lineares e não lineares convencionais tanto na velocidade de convergência quanto no erro médio quadrático atingido. / [en] In this dissertation, several data-selective adaptive signal processing algorithms are derived and investigated for solving two different problems. The first one involves scenarios handling sparse systems, where we introduce a framework in which a general penalty function is incorporated into the cost function for exploiting the sparsity of the model. Under this scope, we propose three algorithms with an adjustable penalty function, the first one based on the l1 - norm, which we term zero-attracting SM-NLMS with adjustable penalty function (ZA-SM-NLMS-ADP). The second algorithm is based on the log-sum penalty function and the third one on the l0 - norm, named reweighted ZASM- NLMS (RZA-SM-NLMS-ADP) and the exponential ZA-SM-NLMS (EZASM- NLMS-ADP), respectively. We also carry out a statistical analysis of the sparsity-aware SM-NLMS algorithms with a general penalty function, arriving at mathematical expressions for the mean-square error at steady state. The second problem addressed considers nonlinear adaptive algorithms based on kernel functions. In this context, we develop two data selective algorithms, the Set-Membership Normalized Kernel Least Mean Squares (SM-NKLMS) algorithm and the Set-Membership Kernel Affine Projection (SM-KAP) algorithm, which have the capability of naturally limiting the growing structure created by the kernels, dealing with one of the major problems presented when working with kernel algorithms. The kernel algorithms developed have been tested for a time series prediction task. A statistical analysis of the proposed SM-NKLMS algorithm is also developed. Simulation results show that the proposed algorithms, outperform standard linear and nonlinear adaptive algorithms in both convergence rate and steady state performance.