No / An investigation into the wavelet packet transform (WPT) modulation scheme for Multiple Input Multiple Output (MIMO) band-limited systems is presented. The implementation involves using the WPT as the base multiplexing technology at baseband, instead of the traditional Fast Fourier Transform (FFT) common in Orthogonal Frequency Division Multiplexing (OFDM) systems. An investigation for a WPT-MIMO multicarrier system, using the Alamouti diversity technique, is presented. Results are consistent with those in the original Alamouti work. The scheme is then implemented for WPT-MIMO and FFTMIMO cases with extended receiver diversity, namely 2 ×Nr MIMO systems, where Nr is the number of receiver elements. It is found that the diversity gain decreases with increasing receiver diversity and that WPT-MIMO systems can be more advantageous than FFT-based MIMO-OFDM systems.

Dans cette thèse, nous proposons une méthodologie de conception d’architectures de communication dédiées aux réseaux de capteurs basées sur la technique de radio impulsionnelle pour les transmissions ultralarge bande (ULB). La technique impulsionnelle proposée ici repose sur la modulation de forme d’impulsion. L’approche de conception architecturale présentée dans cette thèse se focalise plus particulièrement sur la forme des impulsions et leur génération, qui revêt un intérêt majeur puisqu’elle constitue le support de l’information échangée. L’étude sur le choix de la forme d’impulsion nous a conduit à proposer deux architectures différentes. Une première architecture repose sur les polynômes orthogonaux, et plus particulièrement sur les polynômes d’Hermite, pour la génération des impulsions, et sur une architecture de corrélation pour la détection et la reconnaissance des trains d’impulsions transmis. La deuxième architecture est basée sur la transformée en paquets d’ondelettes discrète et peut être exploitée selon deux modes d’utilisation différents, mono et multiutilisateurs. L’utilisation d’une architecture de synthèse à l’émission et d’analyse à la réception ouvre une nouvelle orientation pour les communications numériques, permettant à la transformée en ondelettes d’assurer à la fois la génération des impulsions à l’émission et leur reconnaissance à la réception. Un intérêt immédiat de la technique proposée permet notamment de faciliter l’accès multiutilisateurs au canal ultralarge bande, et d’autoriser des communications simultanées (Many-to-one, des nœuds vers le puits) ou du broadcast (One-to-many, du puits vers les nœuds) sans surcharger la couche MAC. L’architecture proposée s’inscrit donc à l’interface des couches PHY et MAC et permet de relâcher les contraintes de conception spécifiques à ces couches / In this thesis, we propose a design methodology for communication architectures dedicated to wireless sensor network based on impulse radio techniques for UWB communications. The impulse technique proposed in this work relies on pulse shape modulation. The architecture design approach proposed in this thesis focuses on pulses shape and their generation, which is of major interest as it constitutes the carrier of the information exchanged. The study on the choice of pulse shape led us to propose two different architectures. The first one is based on orthogonal polynomials, more especially on the Hermite polynomials, for impulse generation, and on a correlation architecture for detection and recognition of transmitted impulses. The second architecture is based on discrete wavelet packet transform and can be used according two different modes, mono and multi-users. The use of both synthesis and analysis architectures for emitter and receiver, respectively, offers a new way for digital communications and allows the wavelet transform to ensure the impulses generation on the transmitter and their recognition on the receiver. A major interest point of the proposed technique is to facilitate the multi-users access to the ultra-wideband channel and to allow simultaneous communications (many-to-one, from the sensors to the sink) or broadcast (one-to-many, from the coordinator to the nodes) but without overloading the MAC layer. Hence, the proposed architecture is part of the interface between both PHY and MAC layers, and allows to release their specific design constraints

Dans cette thèse, nous présenterons une architecture de communication multi-utilisateurs à base des réseaux de capteurs sans fil dans un environnement industriel fortement bruité. Deux modes de fonctionnement de cette architecture sont présentés ; un mode Many-To-One reliant plusieurs capteurs émetteurs à un seul récepteur et un mode One-To-Many reliant un émetteur à plusieurs capteurs récepteurs. La couche physique de ce système est basée sur une modulation par transformée par paquets d'ondelettes inverse (IDWPT) à l'émission et une transformée par paquets d'ondelette discrète (DWPT) en réception. Pour tester notre architecture, un modèle de canal industriel est proposé qui tient compte des phénomènes des multi-trajets et des évanouissements en plus du bruit additif. Ce dernier étant modélisé comme un bruit gaussien auquel s'additionne un bruit impulsionnel causant une dégradation significative des signaux. L'architecture est testée pour différentes configurations de communications sans fil et pour différentes formes d'ondelettes afin de proposer un mode de communication optimal. Aussi, une amélioration de la robustesse de notre système est effectuée en ajoutant un codage correcteur d'erreur du canal et un seuillage du bruit impulsionnel à la réception pour minimiser les effets du bruit industriel sur les signaux reçus. En utilisant un code correcteur d'erreur, la détection et reconstitution des signaux se fait sans erreur à partir d'un SNR de 8dB pour un taux de codage 1/4 pour une transmission sur à canal à évanouissement. Pour un récepteur optimal à base du seuillage du bruit, les performances en termes de taux d'erreur binaires sont améliorées de 10dB pour une transmission sur un canal à bruit industriel. Enfin, une comparaison de la robustesse de notre architecture impulsionnelle avec un système à base d'une modulation multi-porteuse classique OFDM est effectuée. Ceci nous amène à proposer un système de communication multi-utilisateurs robuste à base des réseaux de capteurs sans fil pour des communications en milieu industriel difficile. / In this thesis, we will present a multi-user communication architecture based on wireless sensor networks in a noisy industrial environment. Two modes of operation of this architecture are presented ; a Many-To-One mode linking several transmitter sensors to a single receiver and a One-To-Many mode linking a transmitter to several receiver sensors. The physical layer of this system is based on the inverse transform (IDWPT) at transmission and the discrete wavelet packet transform (DWPT) at reception. To test our architecture, an industrial channel model is proposed that takes into account the phenomena of multipath and fading in addition to additive noise. The latter being modelled as Gaussian noise to which is added an impulse noise causing significant signal degradation. The architecture is tested for different wireless communication configurations and wavelet shapes to provide an optimal communication mode. Also, an improvement in the robustness of our system is achieved by adding channel error correction coding and pulse noise thresholding at reception to minimize the effects of industrial noise on the received signals. Using an error-correcting code, the detection and reconstruction of signals is error-free from an SNR of 8dB for a coding rate of 1/4 for transmission on a fading channel. For an optimal receiver based on noise thresholding, the performance in terms of binary error rates is improved by 10dB for transmission over an industrial noise channel. Finally, a comparison of the robustness of our pulse architecture with a system based on a conventional OFDM multi-carrier modulation is carried out. This leads us to propose a robust multi-user communication system based on wireless sensor networks for communications in difficult industrial environments.

Many of the existing ambient modal identification methods based on vibration data process information centrally to calculate the modal properties. Such methods demand relatively large memory and processing capabilities to interrogate the data. With the recent advances in wireless sensor technology, it is now possible to process information on the sensor itself. The decentralized information so obtained from individual sensors can be combined to estimate the global modal information of the structure. The main objective of this thesis is to present a new class of decentralized algorithms that can address the limitations stated above. The completed work in this regard involves casting the identification problem within the framework of underdetermined blind source separation (BSS). Time-frequency transformations of measurements are carried out, resulting in a sparse representation of the signals. Stationary wavelet packet transform (SWPT) is used as the primary means to obtain a sparse representation in the time-frequency domain. Several partial setups are used to obtain the partial modal information, which are then combined to obtain the global structural mode information. Most BSS methods in the context of modal identification assume that the excitation is white and do not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations (e.g., pedestrian bridges) when the excitation is a superposition of narrow-band harmonic(s) and broad-band disturbance. Under such conditions, traditional BSS methods yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In this research, a novel under-determined BSS algorithm is developed involving statistical characterization of the sources which are used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system. Moreover, the issue of computational burden involving an over-complete dictionary of sparse bases is alleviated through a new underdetermined BSS method based on a tensor algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. Finally, the proposed methods are validated using measurements obtained from both wired and wireless sensors on laboratory scale and full scale buildings and bridges.

Many of the existing ambient modal identification methods based on vibration data process information centrally to calculate the modal properties. Such methods demand relatively large memory and processing capabilities to interrogate the data. With the recent advances in wireless sensor technology, it is now possible to process information on the sensor itself. The decentralized information so obtained from individual sensors can be combined to estimate the global modal information of the structure. The main objective of this thesis is to present a new class of decentralized algorithms that can address the limitations stated above. The completed work in this regard involves casting the identification problem within the framework of underdetermined blind source separation (BSS). Time-frequency transformations of measurements are carried out, resulting in a sparse representation of the signals. Stationary wavelet packet transform (SWPT) is used as the primary means to obtain a sparse representation in the time-frequency domain. Several partial setups are used to obtain the partial modal information, which are then combined to obtain the global structural mode information. Most BSS methods in the context of modal identification assume that the excitation is white and do not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations (e.g., pedestrian bridges) when the excitation is a superposition of narrow-band harmonic(s) and broad-band disturbance. Under such conditions, traditional BSS methods yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In this research, a novel under-determined BSS algorithm is developed involving statistical characterization of the sources which are used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system. Moreover, the issue of computational burden involving an over-complete dictionary of sparse bases is alleviated through a new underdetermined BSS method based on a tensor algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. Finally, the proposed methods are validated using measurements obtained from both wired and wireless sensors on laboratory scale and full scale buildings and bridges.

No description available.

Made available in DSpace on 2014-12-17T14:54:49Z (GMT). No. of bitstreams: 1 HelianaBS_da_capa_ate_cap4.pdf: 2361373 bytes, checksum: 3e1e43e8ba1aadc274663b8b8e3de72f (MD5) Previous issue date: 2008-02-22 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries / O c?ncer de pele ? o mais comum de todos os c?nceres e o aumento da sua incid?ncia deve-se, em parte, ao comportamento das pessoas em rela??o ? exposi??o ao sol. No Brasil, o c?ncer de pele n?o melanoma ? o mais incidente na maioria das regi?es. A dermatoscopia e ideodermatoscopia s?o os principais tipos de exames para o diagn?stico de doen?as da pele dermatol?gicas. O campo que envolve o uso de ferramentas computacionais para o aux?lio ou acompanhamento do diagn?stico m?dico em les?es dermatol?gicas ainda ? visto como muito recente. V?rios m?todos foram propostos para classifica??o autom?tica de patologias da pele utilizando imagens. O presente trabalho tem como objetivo apresentar uma nova metodologia inteligente para an?lise e classifica??o de imagens de c?ncer de pele, baseada nas t?cnicas de processamento digital de imagens para extra??o de caracter?sticas de cor, forma e textura, utilizando a Transformada Wavelet Packet (TWP) e a t?cnicas de aprendizado de m?quina denominada M?quina de Vetor de Suporte (SVM Support Vector Machine). A Transformada Wavelet Packet ? aplicada para extra??o de caracter?sticas de textura nas imagens. Esta consiste de um conjunto de fun??es base que representa a imagem em diferentes bandas de freq??ncia, cada uma com resolu??es distintas correspondente a cada escala. Al?m disso, s?o calculadas tamb?m as caracter?sticas de cor da les?o que s?o dependentes de um contexto visual, influenciada pelas cores existentes em sua volta, e os atributos de forma atrav?s dos descritores de Fourier. Para a tarefa de classifica??o ? utilizado a M?quina de Vetor de Suporte, que baseia-se nos princ?pios da minimiza??o do risco estrutural, proveniente da teoria do aprendizado estat?stico. A SVM tem como objetivo construir hiperplanos ?timos que apresentem a maior margem de separa??o entre classes. O hiperplano gerado ? determinado por um subconjunto dos pontos das classes, chamado vetores de suporte. Para o banco de dados utilizado neste trabalho, os resultados apresentaram um bom desempenho obtendo um acerto global de 92,73% para melanoma, e 86% para les?es n?o-melanoma e benigna. O potencial dos descritores extra?dos aliados ao classificador SVM tornou o m?todo capaz de reconhecer e classificar as les?es analisadas

Tradicionalmente, la detección de faltas en máquinas eléctricas se basa en el uso de la Transformada Rápida de Fourier ya que la mayoría de las faltas pueden ser diagnosticadas con ella con seguridad si las máquinas operan en condiciones de régimen estacionario durante un intervalo de tiempo razonable. Sin embargo, para aplicaciones en las que las máquinas operan en condiciones de carga y velocidad fluctuantes (condiciones no estacionarias) como por ejemplo los aerogeneradores, el uso de la Transformada Rápida de Fourier debe ser reemplazado por otras técnicas. La presente tesis desarrolla una nueva metodología para el diagnóstico de máquinas de inducción de rotor de jaula y rotor bobinado operando en condiciones no estacionarias, basada en el análisis de las componentes de falta de las corrientes en el plano deslizamiento frecuencia. La técnica es aplicada al diagnóstico de asimetrías estatóricas, rotóricas y también para la falta de excentricidad mixta. El diagnóstico de las máquinas eléctricas en el dominio deslizamiento-frecuencia confiere un carácter universal a la metodología ya que puede diagnosticar máquinas eléctricas independientemente de sus características, del modo en el que la velocidad de la máquina varía y de su modo de funcionamiento (motor o generador). El desarrollo de la metodología conlleva las siguientes etapas: (i) Caracterización de las evoluciones de las componentes de falta de asimetría estatórica, rotórica y excentricidad mixta para las máquinas de inducción de rotores de jaula y bobinados en función de la velocidad (deslizamiento) y la frecuencia de alimentación de la red a la que está conectada la máquina. (ii) Debido a la importancia del procesado de la señal, se realiza una introducción a los conceptos básicos del procesado de señal antes de centrarse en las técnicas actuales de procesado de señal para el diagnóstico de máquinas eléctricas. (iii) La extracción de las componentes de falta se lleva a cabo a través de tres técnicas de filtrado diferentes: filtros basados en la Transformada Discreta Wavelet, en la Transformada Wavelet Packet y con una nueva técnica de filtrado propuesta en esta tesis, el Filtrado Espectral. Las dos primeras técnicas de filtrado extraen las componentes de falta en el dominio del tiempo mientras que la nueva técnica de filtrado realiza la extracción en el dominio de la frecuencia. (iv) La extracción de las componentes de falta, en algunos casos, conlleva el desplazamiento de la frecuencia de las componentes de falta. El desplazamiento de la frecuencia se realiza a través de dos técnicas: el Teorema del Desplazamiento de la Frecuencia y la Transformada Hilbert. (v) A diferencia de otras técnicas ya desarrolladas, la metodología propuesta no se basa exclusivamente en el cálculo de la energía de la componente de falta sino que también estudia la evolución de la frecuencia instantánea de ellas, calculándola a través de dos técnicas diferentes (la Transformada Hilbert y el operador Teager-Kaiser), frente al deslizamiento. La representación de la frecuencia instantánea frente al deslizamiento elimina la posibilidad de diagnósticos falsos positivos mejorando la precisión y la calidad del diagnóstico. Además, la representación de la frecuencia instantánea frente al deslizamiento permite realizar diagnósticos cualitativos que son rápidos y requieren bajos requisitos computacionales. (vi) Finalmente, debido a la importancia de la automatización de los procesos industriales y para evitar la posible divergencia presente en el diagnóstico cualitativo, tres parámetros objetivos de diagnóstico son desarrollados: el parámetro de la energía, el coeficiente de similitud y los parámetros de regresión. El parámetro de la energía cuantifica la severidad de la falta según su valor y es calculado en el dominio del tiempo y en el dominio de la frecuencia (consecuencia de la extracción de las componentes de falta en el dominio de la frecuencia). El coeficiente de similitud y los parámetros de regresión son parámetros objetivos que permiten descartar diagnósticos falsos positivos aumentando la robustez de la metodología propuesta. La metodología de diagnóstico propuesta se valida experimentalmente para las faltas de asimetría estatórica y rotórica y para el fallo de excentricidad mixta en máquinas de inducción de rotor de jaula y rotor bobinado alimentadas desde la red eléctrica y desde convertidores de frecuencia en condiciones no estacionarias estocásticas. / Vedreño Santos, FJ. (2013). Diagnosis of electric induction machines in non-stationary regimes working in randomly changing conditions [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34177 / TESIS

This work deals with the possibilities of filtering the ECG signal, representing the first part, which is the basis for successful delineation and follow diagnosis of the ECG signal. Filtration in this case is mean to suppress interference from electrical grid, noise of electrical grid. The content of the work is description of filters realized trough wavelet transform and linear filtering as a means to successful filtration of interference. There are method of stationary wavelet transform - dyadic wavelet transform, wavelet packet transform and wavelet wiener filtering method. Linear filtering includes two narrow-band FIR filters. The objective of this work is to propose different methods of wavelet and linear filters in Matlab, filtering of ECG signals and compare the success of filtration methods. ECG signals used in this work are from the CSE database.