abstract: In recent years, there has been an increased interest in sharing available bandwidth to avoid spectrum congestion. With an ever-increasing number wireless users, it is critical to develop signal processing based spectrum sharing algorithms to achieve cooperative use of the allocated spectrum among multiple systems in order to reduce interference between systems. This work studies the radar and communications systems coexistence problem using two main approaches. The first approach develops methodologies to increase radar target tracking performance under low signal-to-interference-plus-noise ratio (SINR) conditions due to the coexistence of strong communications interference. The second approach jointly optimizes the performance of both systems by co-designing a common transmit waveform. When concentrating on improving radar tracking performance, a pulsed radar that is tracking a single target coexisting with high powered communications interference is considered. Although the Cramer-Rao lower bound (CRLB) on the covariance of an unbiased estimator of deterministic parameters provides a bound on the estimation mean squared error (MSE), there exists an SINR threshold at which estimator covariance rapidly deviates from the CRLB. After demonstrating that different radar waveforms experience different estimation SINR thresholds using the Barankin bound (BB), a new radar waveform design method is proposed based on predicting the waveform-dependent BB SINR threshold under low SINR operating conditions. A novel method of predicting the SINR threshold value for maximum likelihood estimation (MLE) is proposed. A relationship is shown to exist between the formulation of the BB kernel and the probability of selecting sidelobes for the MLE. This relationship is demonstrated as an accurate means of threshold prediction for the radar target parameter estimation of frequency, time-delay and angle-of-arrival. For the co-design radar and communications system problem, the use of a common transmit waveform for a pulse-Doppler radar and a multiuser communications system is proposed. The signaling scheme for each system is selected from a class of waveforms with nonlinear phase function by optimizing the waveform parameters to minimize interference between the two systems and interference among communications users. Using multi-objective optimization, a trade-off in system performance is demonstrated when selecting waveforms that minimize both system interference and tracking MSE. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

Resumo: A proposta deste trabalho consiste em investigar o comportamento de transformadores de distribuição de 25 KVA e de 45 KVA submetidos a ensaios de impulsos atmosféricos. Essa investigação consistiu da verificação da influência dos valores de tempos de frente e de cauda, da forma de onda do impulso, na amplitude das correntes produzidas nos enrolamentos do transformador durante o ensaio. Tais correntes são usualmente empregadas para avaliar o desempenho elétrico dos transformadores e, nessa dissertação, foram usadas para o treinamento e tese de Redes Neurais Artificiais desenvolvidas como ferramentas inteligentes computacionais. Neste contexto, o desempenho de duas Redes Neurais foi avaliado. A primeira rede usou como variável de entrada, os valores de tempo de frente, de caula e da tensão máxima (crista) e, como saída, a corrente máxima no transformador. Na segunda rede neural, a entrada correspondente ao valor da tensão máxima, da primeira rede neural, é substituída pelo valor da taxa de crescimento da tensão. Com base nos resultados obtidos, pode-se verificar que, para determinados valores de tempos de frente e de cauda, a amplitude da corrente máxima, aumenta ou diminui, apresentando um comportamento não-linear. A utilização das Redes Neurais desenvolvidas neste trabalho poderá auxiliar na escolha das características das formas de onda de impulso que tornem mais sensíveis os ensaios de impulsos atmosféricos em transformadores de distribuição. O objetivo é que esse aumento da sensibilidade do ensaio minimize o empirismo e erros de avaliação, contribuindo para tornar mínima a taxa de falha em transformadores / Abstract: The proposal of this work is to investigate and to analyze the behavior of 25 kVA and 45 KVA distribution transformers under impulses/surge tests. This research consited in a verification of the influence of front and and tail time values, from surge waverfom, in the current magnitude produced in transformer windings during the test. Such currents are usually employed to evalute the perfomers and, in this dissertation, were used for the training and test the Artificial Neural Networks, developed as intelligent computational tools. In this context, the performance of two Neural Networks was evaluated. This first network has used as entry variables: front and tail time and the maximun voltage (crest) values and, as an exist, the maximum current in transformer. In the second neural network, the entry corresponding to the maximum voltage value, from the first neural network, is replaced by the value of the rate of growth of the voltage. Based on the obtained results, one may find out, for certain values front and tail times values, the amplitude of maximum current, increases or decreases, presenting a non-linear behavior. The use of Neural Networks developed in this work can help someone to choose the best impulse waveform characteristics which make the impulse test in distribution transformers more sinsitive. The objective is that the rising the test sensitivity will minimize the empiricism and errors of assessment, helping to reduce the failure rate in transformers / Orientador: André Nunes de Souza / Coorientador: Pedro da Costa Junior / Banca: Oscar Armando Maldonado Astorga / Banca: André Christóvão Pio Martins / Mestre

Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-08-20Bitstream added on 2014-06-13T20:48:00Z : No. of bitstreams: 1 amaral_fcl_me_bauru.pdf: 878445 bytes, checksum: 4f078f3133073c4bc5a0882fbff35667 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A proposta deste trabalho consiste em investigar o comportamento de transformadores de distribuição de 25 KVA e de 45 KVA submetidos a ensaios de impulsos atmosféricos. Essa investigação consistiu da verificação da influência dos valores de tempos de frente e de cauda, da forma de onda do impulso, na amplitude das correntes produzidas nos enrolamentos do transformador durante o ensaio. Tais correntes são usualmente empregadas para avaliar o desempenho elétrico dos transformadores e, nessa dissertação, foram usadas para o treinamento e tese de Redes Neurais Artificiais desenvolvidas como ferramentas inteligentes computacionais. Neste contexto, o desempenho de duas Redes Neurais foi avaliado. A primeira rede usou como variável de entrada, os valores de tempo de frente, de caula e da tensão máxima (crista) e, como saída, a corrente máxima no transformador. Na segunda rede neural, a entrada correspondente ao valor da tensão máxima, da primeira rede neural, é substituída pelo valor da taxa de crescimento da tensão. Com base nos resultados obtidos, pode-se verificar que, para determinados valores de tempos de frente e de cauda, a amplitude da corrente máxima, aumenta ou diminui, apresentando um comportamento não-linear. A utilização das Redes Neurais desenvolvidas neste trabalho poderá auxiliar na escolha das características das formas de onda de impulso que tornem mais sensíveis os ensaios de impulsos atmosféricos em transformadores de distribuição. O objetivo é que esse aumento da sensibilidade do ensaio minimize o empirismo e erros de avaliação, contribuindo para tornar mínima a taxa de falha em transformadores / The proposal of this work is to investigate and to analyze the behavior of 25 kVA and 45 KVA distribution transformers under impulses/surge tests. This research consited in a verification of the influence of front and and tail time values, from surge waverfom, in the current magnitude produced in transformer windings during the test. Such currents are usually employed to evalute the perfomers and, in this dissertation, were used for the training and test the Artificial Neural Networks, developed as intelligent computational tools. In this context, the performance of two Neural Networks was evaluated. This first network has used as entry variables: front and tail time and the maximun voltage (crest) values and, as an exist, the maximum current in transformer. In the second neural network, the entry corresponding to the maximum voltage value, from the first neural network, is replaced by the value of the rate of growth of the voltage. Based on the obtained results, one may find out, for certain values front and tail times values, the amplitude of maximum current, increases or decreases, presenting a non-linear behavior. The use of Neural Networks developed in this work can help someone to choose the best impulse waveform characteristics which make the impulse test in distribution transformers more sinsitive. The objective is that the rising the test sensitivity will minimize the empiricism and errors of assessment, helping to reduce the failure rate in transformers

Dans le domaine du radar aéroporté, les enjeux industriels actuels sont nombreux et portent,entre autres, sur l'établissement de profils distance de cibles aériennes, terrestres et maritimes pour leur identification. Cela implique en particulier la mise en oeuvre de chaînes d'émission/réception pour des modes de fonctionnement haute résolution. Dans ce contexte, les problématiques à traiter comprennent alors la conception et l'analyse de performances de détecteurs pour des modèles de cibles étendues, la conception de formes d'ondes multi-résolutions et le développement des traitements associés, l'optimisation de formes d'onde robustes au fouillis, etc. Le travail de cette thèse, qui s'intègre dans ce cadre, se décompose en deux parties. Dans un premier temps, nous traitons la détection d'une cible dite "étendue", c'est-à-dire caractérisée par plusieurs réflecteurs élémentaires prépondérants répartis sur plusieurs cases distance non nécessairement consécutives. Ce modèle est notamment approprié lorsque la résolution en distance est suffisamment fine, et s'intègre dans les problématiques d'identification de cible. Dans ce cadre, nous étudions un test de détection fondé sur le rapport de vraisemblances généralisé (GLRT) intégrant la localisation inconnue des réflecteurs, et lorsque la perturbation est du bruit blanc gaussien. En utilisant des résultats issus des statistiques d'ordre, nous déduisons des approximations de la probabilité de fausse alarme et de la probabilité de détection. Des comparaisons numériques avec des détecteurs existants sont fournies. Dans un second temps, nous étudions une forme d'onde correspondant à un train d'impulsions contenant deux codes de phase, l'un intra impulsion et l'autre inter impulsion. Pour un modèle de cible ponctuelle et un fouillis gaussien, nous proposons de sélectionner ces codes en tenant compte de différents critères tels que la maximisation de la probabilité de détection ou encore la minimisation des lobes secondaires du signal reçu après traitement. Pour un type donné de fouillis modélisé par un processus autorégressif (AR), nous abordons le problème d'optimisation multi-objectifs en utilisant les fronts de Pareto. La modélisation AR permettant de considérer plusieurs types de fouillis à partir d'un nombre réduit de paramètres, nous étudions alors la robustesse des codes de phase optimaux à des variations de fouillis. / In the field of airborne radar, one of the current industrial stakes, among others, is the identification of a target, whether airborne, terrestrial or maritime, through the establishment of its range profile. This implies to set up a transmit/receive processing for high resolution modes. In this context, the issues to be addressed include the design and the performance analysis of detectors for extended target models, the design of multi-resolution waveforms and the associated processing, the optimization of waveforms that are robust to clutter, etc. Within this frame, the work of this thesis is twofold. The first part deals with the detection of a so-called "extended" target, i.e. which is characterized by a few main scatterers spread over several range gates not necessarily consecutive. This model is appropriate when the range resolution is thin enough and it is suited for target identification issues. In this context, we study a detection test based on the generalized likelihood test (GLRT) which includes the unknown locations of the scatterers, and when the disturbance is white Gaussian noise. By using ordered statistics, we deduce approximations of the probability of false alarm and the probability of detection. Numerical comparison with existing detectors are also provided. Secondly, we study a waveform based on a pulse train which contains two phase codes: the first one is intrapulse whereas the second one is interpulse. Assuming a point target and Gaussian clutter, we propose to select these codes taking into account several criteria such as the maximization of the probability of detection or the minimization of the sidelobes of the received signal after processing. For a given type of clutter modeled by an autoregressive (AR) process, we address this multi-objective optimization problem using the Pareto fronts. Since the AR modeling makes it possible to consider several types of clutter from a reduced number of parameters, we study the robustness of optimal phase codes to clutter variations.

L’analyse de stabilité en régime transitoire du réseau de transport électrique permet de contrôler le bon retour au régime stationnaire du système soumis à une perturbation. Cette analyse systématique des systèmes de réseaux en développement permet notamment d’optimiser la production et la consommation de l’énergie électrique, et de protéger les équipements tels que les centrales électriques, les transformateurs, les lignes haute-tension, etc. Afin d’améliorer la stabilité, la robustesse et la viabilité de ces systèmes, la tendance est à l’interconnexion des réseaux de transport régionaux et nationaux, et ainsi, au développement et à l’analyse de systèmes toujours plus grands. Le problème de stabilité électrique peut être simulé numériquement grâce à l’intégration d’un système d’équations algébro-différentielles non-linéaire et raide. Lorsque le problème traité est très grand, la simulation numérique devient très coûteuse en temps de calcul et ralentit considérablement le travail des professionnels du secteur. Cette thèse a pour but de proposer, d’étudier, et de développer des méthodes innovantes de calcul parallèle pour la résolution des systèmes d’équations différentielles issus de la simulation de grands réseaux électriques tel que le réseau européen. Dans ce manuscrit, on livre une analyse des propriétés de ces systèmes assez spécifiques : creux, irréguliers, non-linéaires, raides et hétérogènes. On discute notamment de la structure particulière de ces systèmes qui rend attrayante l’application d’une méthode de décomposition de domaine. On étudie ainsi plusieurs méthodes de parallélisation en espace : la parallélisation fine de chaque opération coûteuse, la résolution du système non-linéaire par décomposition en sous-réseaux faiblement couplés, d’abord sur chaque étape d’intégration, puis par méthode de relaxation d’ondes. On aborde aussi la parallélisation en temps de type algorithme Pararéel ainsi qu’une méthode parallèle espace-temps bénéficiant des propriétés couplées des méthodes de relaxation d’ondes et de Pararéel. Dans ces travaux, nous proposons des méthodes assurant la convergence rapide des méthodes de décomposition de domaine quel que soit le nombre de sous-domaines et de processeurs employés. Nous introduisons pour cela des techniques de préconditionnement en espace adéquates afin d’améliorer la scalabilité des méthodes de parallélisation envisagées. / Power system transient stability analysis enables to control the return to equilibrium of the system subjected to a disturbance. This systematic analysis of developing transport networks allows to optimize the production and the consumption of electric power and to protect the equipments such as power plants, transformers, highvoltage lines and so on. In order to improve the stability, the robustness, and the sustainability of these systems, a worldwide trend is to interconnect regional and national transport networks. This leads to analyze ever larger systems. The power-stability problem can be numerically simulated owing to the integration of a differential-algebraic system which is nonlinear and stiff. When considering a very large problem, numerical simulation is very time consuming and significantly slows down the work of professionals. This thesis aims at studying innovative parallel computing methods for the resolution of differential systems arising from the transient stability analysis of large power systems such as the European Transport Network. In this manuscript, we first deliver an analysis of the properties of these rather specific systems: sparse, irregular, nonlinear, stiff, and heterogeneous. We discuss the particular structure of these systems making the application of a domain decomposition method interesting. Thus, we study several space parallelization methods: the fine parallelization of each costly tasks, the resolution of the nonlinear system by decomposition into weakly coupled subnetworks, first on each integration step separately, and then by waveform relaxation method. We also address the time parallelization with a Parareal-based algorithm and a space-time parallel method which benefits from the coupled properties of waveform relaxation and Parareal methods. In this work, we focus on methods which ensure a fast convergence of domain decomposition methods whatever the number of subdomains/processors used. In order to achieve such a goal, we introduce space preconditioning techniques to improve the scalability of the parallelization methods considered.

Le speckle laser est un effet d'interférence longtemps considéré comme néfaste lors de l'utilisation de sources de lumière cohérente. Cependant, pour certaines applications, cet effet peut être bénéfique et utilisé comme source d'information. C’est le cas du domaine biomédical.Ainsi, le speckle laser est utilisé depuis des décennies pour la surveillance du flux sanguin microvasculaire. Il commence à être considéré aussi pour l'extraction de paramètres de la macrocirculation sanguine. Ce travail vise donc tout d’abord à démontrer que le speckle laser permet d'évaluer les paramètres hémodynamiques de la macrocirculation avec fiabilité et à partir d’une technique identique à celle employée dans l’étude de la microcirculation. Ceci conduira à une intégration rapide du dispositif dans les instruments existants. Par ailleurs, l'un des problèmes les plus importants du speckle laser,empêchant l’obtention d’une analyse totalement quantitative, est l'effet des diffuseurs statiques. Ce type de diffuseurs influence fortement le contraste de speckle, conduisant à une mauvaise interprétation des données. Le second objectif de ce travail est donc d'étudier l'effet des diffuseurs statiques sur la corrélation et le contraste du speckle laser. Nos résultats montrent tout d’abord que le speckle laser est un phénomène intéressant pour extraire les paramètres hémodynamiques de la macrocirculation. Par ailleurs, nos études révèlent que le calcul de la corrélation du speckle laser permet d'estimer le rapport entre diffuseurs statiques / dynamiques avec une bonne fiabilité. En outre, le contraste temporel permet de déterminer les diffuseurs dynamiques possédant des vitesses différentes. / The laser speckle is an interference effect that has been considered as a main drawback in the use of coherent light sources. However, for a specific set of applications, this effect can become a source of information. Among these applications there are the biomedical ones. The laser speckle has been used for decades to monitor microvascular blood flow but only now starts to be considered as a method that can also be used for macrocirculation parameters extraction. This work first aims at demonstrating that laser speckle can be used for macrocirculation assessment with good reliability, using the same technique as the one employed in microcirculation assessment. The use of the same methods could lead to a rapid inclusion of this new evaluation in the existing devices. Furthermore, one of the most important laser speckle issues, that prevents a fully quantitative analysis, is the effect of static scatterers. This type of scatterers strongly influences the speckle contrast, leading to a wrong interpretation of the data. The second objective of this work is to study the effect of statics catterers on the laser speckle correlation and contrast. Our results show that the laser speckle is an interesting phenomenon to extract hemodynamic parameters in the macrocirculation. This work also demonstrates that the laser speckle correlation is able to estimate the ratio between static/dynamic scatterers with good reliability. Moreover, the temporal speckle contrast achieved a very good performance in discerning dynamic scatterers with different velocities.

This thesis is focused on the improvement of an existing implementation of a waveform visualizer. The problem area handled in this work has its focus on how to reduce the number of points to be plotted. The given waveform visualizer was extended by the use of two additional algorithms. First, a Level Of Detail (LOD) algorithm that gives the subset of points that are necessary to plot the waveform in the current zoom level. Second, a straight line identification algorithm to find a series of points aligned in a straight line, only leaving the end points and then drawing a line between them. These two optimizations are the main focus of this work.Additionally, an exporting functionality was implemented to export the plot data into several different data formats. Also some improvements of zooming, panning, some GUI design, and a new drag and drop functionality was constructed.

Advances in machine condition monitoring technologies are driven by the rise in complexity of modern machines and the increased demand for product reliability. Condition monitoring research tends to focus on the development of signal processing algorithms that are sensitive to machine faults, robust under time-varying operating conditions, and informative regarding the nature and extent of machine faults. A significant challenge remains for monitoring the condition of machines that are subject to time-varying operating conditions. The here presented work is concerned with the development of cost effective condition monitoring algorithms. It is investigated how empirical models (including probability density distributions and regression functions) may be used to extract diagnostic information from machine response signals that have been generated under fluctuating operating conditions. The proposed methodology is investigated on a number of case studies, including gearboxes, alternator end windings, and haul roads. It is shown how empirical models for machine condition monitoring may generally be implemented according to one of two basic approaches. The two approaches are referred to as discrepancy analysis and waveform reconstruction. Discrepancy analysis is concerned with the comparison of a novel signal to a reference model. The reference model is sufficiently expressive to represent vibration response as measured on a healthy machine over a range of operating conditions. The novel signal is compared to the reference model in such a manner that a discrepancy signal transform is obtained. A discrepancy signal is sensitive to faults, robust to time-varying operating conditions, and inherently simple. As such it may further beWaveform reconstruction implements a regression function to model machine response as a function of different state space variables. The regression function may subsequently be exploited to extract diagnostic information. The machine response may for instance be reconstructed at a specified steady state operating condition. This renders the signal wide-sense stationary so that Fourier analysis may be applied. analysed in order to extract periodicities and magnitudes as diagnostic markers. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

No description available.

Speech sounds generated by a simple waveform synthesizer were used to create a vector quantization codebook for use in speech recognition. Recognition was tested over the TI-20 isolated word data base using a conventional DTW matching algorithm. Input speech was band limited to 300 - 3300 Hz, then passed through the Scott Instruments Corp. Coretechs process, implemented on a VET3 speech terminal, to create the speech representation for matching. Synthesized sounds were processed in software by a VET3 signal processing emulation program. Emulation and recognition were performed on a DEC VAX 11/750. The experiments were organized in 2 series. A preliminary experiment, using no vector quantization, provided a baseline for comparison. The original codebook contained 109 vectors, all derived from 2 formant synthesized sounds. This codebook was decimated through the course of the first series of experiments, based on the number of times each vector was used in quantizing the training data for the previous experiment, in order to determine the smallest subset of vectors suitable for coding the speech data base. The second series of experiments altered several test conditions in order to evaluate the applicability of the minimal synthesized codebook to conventional codebook training. The baseline recognition rate was 97%. The recognition rate for synthesized codebooks was approximately 92% for sizes ranging from 109 to 16 vectors. Accuracy for smaller codebooks was slightly less than 90%. Error analysis showed that the primary loss in dropping below 16 vectors was in coding of voiced sounds with high frequency second formants. The 16 vector synthesized codebook was chosen as the seed for the second series of experiments. After one training iteration, and using a normalized distortion score, trained codebooks performed with an accuracy of 95.1%. When codebooks were trained and tested on different sets of speakers, accuracy was 94.9%, indicating that very little speaker dependence was introduced by the training.