Cognitive Radio (CR) is an emerging wireless communications technology that addresses the inefficiency of current radio spectrum usage. CR also supports the evolution of existing wireless applications and the development of new civilian and military applications. In military and public safety applications, there is no information available about the signal present in a frequency band and hence there is a need for a CR receiver to identify the modulation format employed in the signal. The automatic modulation classifier (AMC) is an important signal processing component that helps the CR in identifying the modulation format employed in the detected signal. AMC algorithms developed so far can classify only signals from a single user present in a frequency band. In a typical CR scenario, there is a possibility that more than one user is present in a frequency band and hence it is necessary to develop an AMC that can classify signals from multiple users simultaneously. One of the main objectives of this dissertation is to develop robust multiuser AMC's for CR. It will be shown later that multiple antennas are required at the receiver for classifying multiple signals. The use of multiple antennas at the transmitter and receiver is known as a Multi Input Multi Output (MIMO) communication system. By using multiple antennas at the receiver, apart from classifying signals from multiple users, the CR can harness the advantages offered by classical MIMO communication techniques like higher data rate, reliability, and an extended coverage area. While MIMO CR will provide numerous benefits, there are some significant challenges in applying conventional MIMO theory to CR. In this dissertation, open problems in applying classical MIMO techniques to a CR scenario are addressed. A blind equalizer is another important signal processing component that a CR must possess since there are no training or pilot signals available in many applications. In a typical wireless communication environment the transmitted signals are subjected to noise and multipath fading. Multipath fading not only affects the performance of symbol detection by causing inter symbol interference (ISI) but also affects the performance of the AMC. The equalizer is a signal processing component that removes ISI from the received signal, thus improving the symbol detection performance. In a conventional wireless communication system, training or pilot sequences are usually available for designing the equalizer. When a training sequence is available, equalizer parameters are adapted by minimizing the well known cost function called mean square error (MSE). When a training sequence is not available, blind equalization algorithms adapt the parameters of the blind equalizer by minimizing cost functions that exploit the higher order statistics of the received signal. These cost functions are non convex and hence the blind equalizer has the potential to converge to a local minimum. Convergence to a local minimum not only affects symbol detection performance but also affects the performance of the AMC. Robust blind equalizers can be designed if the performance of the AMC is also considered while adapting equalizer parameters. In this dissertation we also develop Single Input Single Output (SISO) and MIMO blind equalizers where the performance of the AMC is also considered while adapting the equalizer parameters. / Ph. D.

Made available in DSpace on 2014-06-11T19:22:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-12-07Bitstream added on 2014-06-13T19:28:02Z : No. of bitstreams: 1 tiago_mm_me_ilha.pdf: 962669 bytes, checksum: 4988399c15f758626b264c1adb577b2f (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Um dos setores de grande importância na indústria frigorífica é o responsável pelo esquarte- jamento de aves, no qual peças inteiras são separadas em partes menores para comercialização. O processo de esquartejamento pode ser feito de forma automática, através de máquinas de corte, ou por trabalhadores, que cortam as aves utilizando uma serra circular. Por ser um tra- balho manual e envolver uma lâmina de corte, a periculosidade desse tipo de trabalho é alta, de maneira que mesmo com o uso de uma luva de aço inox como equipamento de proteção, costumam ocorrer acidentes que podem variar desde pequenos cortes até amputação de parte da mão do trabalhador atingido. Neste trabalho, é apresentado um método de análise de sinais para evitar que esse tipo de acidente ocorra. Esse sistema baseia-se na análise dos sinais acústicos envolvidos gerados durante esse processo e são utilizados para desligar o motor que impulsiona a serra e acionar um sistema de frenagem em casos quando houver a ocorrência de acidentes. O problema é abordado utilizando inicialmente um filtro digital e, posteriormente, com as técni- cas de análise multirresolução apresentadas pelas wavelets. Além disso, empregou-se também a decomposição de modo empírico, que também realiza uma análise multirresolução dos sinais decompondo os mesmos em funções de modo intrínseco. Visando detectar o maior número possível de toques suaves de luva na serra sem que cortes de ossos de frango fossem confundi- dos com toques de luva, o sistema apresentou um índice de acertos de aproximadamente 70%, havendo a ocorrência de apenas 2% de falsos positivos. Além desse problema, abordou-se o caso de detecção de trinca em ovos, no qual o objetivo era separar ovos trincados de ovos in- teiros utilizando um sistema barato e eficiente... / One of the most important sectors in the meatpacking industry is chicken quartering, where whole pieces are cut into smaller ones. The quartering process can be done by automatic ma- chines or by manually cutting the chickens using a circular saw. The manual technique imposes physical risks for the workers, which wear protective stainless steel gloves. Small injuries or, in the worst case, amputation of part of the hand can occur in the event of an accident. In this work, we propose a methodology to prevent this type of accident, which is based on the anal- ysis of the acoustic signals generated during this process. In the event of an accident, the saw touches the metal glove, the acoustic signals are processed and used to turn off the engine that drives the saw and trigger a braking system. The problem is firstly analyzed using a digital filter and then with multiresolution techniques by wavelet analysis. In addition, the empirical mode decomposition technique is also employed, which also performs multiresolution analysis of sig- nals. These three techniques are implemented and compared. The method presented a 70% of successful detection of light touches of saw/glove and 2% of false positives, when a normal cut operation is detected as a saw/glove touch, in general occurring when cutting specific parts of bone. Besides this problem, the case of eggshell crack detection is studied, where the goal was to separate cracked eggs from intact eggs using an inexpensive and efficient system. A solenoid was used as a source of mechanical excitation and the resulting acoustic signals were acquired and processed. The same signal processing techniques were employed and compared, with small changes in parameters. As a result, it was possible to detect 80% of cracked eggs and 100% of intact eggs. The multiresolution technique... (Complete abstract click electronic access below)

In this thesis, an estimation method adapted from MUSIC algorithm is presented for estimation of signal arrival time for impulse radio UWB systems. An accurate estimate of signal arrival time is considered essential in time-based wireless and indoor location systems. Since most wireless communications systems used for indoor position location may suffer from dense multipath situation, the accuracy of determining signal arrival time become an important issue for the time-based location systems. The fine resolution of UWB signals provides potentially accurate ranging for indoor location applications. However, the ambiguity caused by the unresolved first arrival path may still yield an error in determining the true signal arrival time. The presented method uses improved MUSIC techniques in time domains to estimate the shortest and the real signal arrival time for UWB radio link. For a two-multipath case, analysis and simulation results of multipath resolvability and the variance of estimation errors of signal arrival time are discussed.

Orientador: Ricardo Tokio Higuti / Banca: Francisco Villarreal Alvarado / Banca: Washington Luiz de Barros Melo / Resumo: Um dos setores de grande importância na indústria frigorífica é o responsável pelo esquarte- jamento de aves, no qual peças inteiras são separadas em partes menores para comercialização. O processo de esquartejamento pode ser feito de forma automática, através de máquinas de corte, ou por trabalhadores, que cortam as aves utilizando uma serra circular. Por ser um tra- balho manual e envolver uma lâmina de corte, a periculosidade desse tipo de trabalho é alta, de maneira que mesmo com o uso de uma luva de aço inox como equipamento de proteção, costumam ocorrer acidentes que podem variar desde pequenos cortes até amputação de parte da mão do trabalhador atingido. Neste trabalho, é apresentado um método de análise de sinais para evitar que esse tipo de acidente ocorra. Esse sistema baseia-se na análise dos sinais acústicos envolvidos gerados durante esse processo e são utilizados para desligar o motor que impulsiona a serra e acionar um sistema de frenagem em casos quando houver a ocorrência de acidentes. O problema é abordado utilizando inicialmente um filtro digital e, posteriormente, com as técni- cas de análise multirresolução apresentadas pelas wavelets. Além disso, empregou-se também a decomposição de modo empírico, que também realiza uma análise multirresolução dos sinais decompondo os mesmos em funções de modo intrínseco. Visando detectar o maior número possível de toques suaves de luva na serra sem que cortes de ossos de frango fossem confundi- dos com toques de luva, o sistema apresentou um índice de acertos de aproximadamente 70%, havendo a ocorrência de apenas 2% de falsos positivos. Além desse problema, abordou-se o caso de detecção de trinca em ovos, no qual o objetivo era separar ovos trincados de ovos in- teiros utilizando um sistema barato e eficiente... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: One of the most important sectors in the meatpacking industry is chicken quartering, where whole pieces are cut into smaller ones. The quartering process can be done by automatic ma- chines or by manually cutting the chickens using a circular saw. The manual technique imposes physical risks for the workers, which wear protective stainless steel gloves. Small injuries or, in the worst case, amputation of part of the hand can occur in the event of an accident. In this work, we propose a methodology to prevent this type of accident, which is based on the anal- ysis of the acoustic signals generated during this process. In the event of an accident, the saw touches the metal glove, the acoustic signals are processed and used to turn off the engine that drives the saw and trigger a braking system. The problem is firstly analyzed using a digital filter and then with multiresolution techniques by wavelet analysis. In addition, the empirical mode decomposition technique is also employed, which also performs multiresolution analysis of sig- nals. These three techniques are implemented and compared. The method presented a 70% of successful detection of light touches of saw/glove and 2% of false positives, when a normal cut operation is detected as a saw/glove touch, in general occurring when cutting specific parts of bone. Besides this problem, the case of eggshell crack detection is studied, where the goal was to separate cracked eggs from intact eggs using an inexpensive and efficient system. A solenoid was used as a source of mechanical excitation and the resulting acoustic signals were acquired and processed. The same signal processing techniques were employed and compared, with small changes in parameters. As a result, it was possible to detect 80% of cracked eggs and 100% of intact eggs. The multiresolution technique... (Complete abstract click electronic access below) / Mestre

This thesis presents the development of MUSIC algorithm based novel approaches for the estimation of Direction of Arrival (DOA) of electromagnetic sources. For the 2D-DOA estimation, this thesis proposes orthogonally polarized linear array configuration rather than the conventionally invoked two dimensional array. An elegant one dimensional search technique to compute 2D-DOA estimation for a single source scenario has been proposed. To facilitate one dimensional search for 2D-DOA estimation, a closed form relationship between the azimuth and elevation angles of the 2D-DOA is derived using the analytical expressions of radiation patterns of Rectangular Waveguide (RWG) and Circular Waveguide (CWG). The computation time for the proposed one dimensional search technique is reduced by a factor of 50 and 150 for 1 and 0:5 search interval respectively. To improve the accuracy and the resolution of 2D-DOA estimation in case of closely spaced sources, this thesis proposes novel array configurations such as orthogonally polarized planar array, orthogonally mounted linear array and orthogonally polarized linear array. Through numerous simulation studies, a relative performance comparison of 2D-DOA estimation realized through various proposed novel array configurations has been carried out to highlight the accuracy and resolution under wide range of SNR conditions. The thesis presents a discussion on the analysis of effect of spatial de correlation in lieu of the employed orthogonally polarized elements in the array configuration on the improved accuracy and resolution of the 2D-DOA estimation. This thesis also deals with the utility of the proposed orthogonally polarized array configurations for tracking of 2D-DOA angles of non-stationary signal sources. The weighting factor and forgetting factor approaches for smoothing the time-varying covariance matrix of the non-stationary sources are studied. The simulation studies on 2D-DOA tracking by invoking proposed array configurations along with the proposed smoothing techniques prove that orthogonal polarized array configuration track the DOA source angle with minimum estimation errors. The thesis proposes the replacement of computationally intensive numerical schemes in Multiple Signal Classification (MUSIC) algorithm such as eigen decomposition and singular value decomposition with the subspace tracking techniques such as Bi-Iterative Singular Value Decomposition (Bi-SVD) algorithm. Invoking the concept of sub-band processing, the thesis addresses the validity of the extension of the presented 2D-DOA estimation analysis to wide band signal. A two subband filter approach is proposed for the estimation 2D-DOA of single and two wideband sources. The simulation study of the two subband filter approach along with the orthogonal polarized array configurations confirms the better estimation accuracy as well as the lesser computation time.

This thesis first discusses the ground plane effects of a test site on the antenna factors (AFs) of hybrid antenna (biconical log-periodic dipole array). Meanwhile, the effects of mutual coupling between antenna and its image, and the variation of active phase center are also discussed. From these analyses, a hybrid method, based on the modified SSM (Standard Site Method) and the PCPM (Phase Center and Pattern Matching) applied to the hybrid antenna for NSA (Normalized Site Attenuation) measurement is proposed. By this method, the low geometry- dependent AFs of hybrid antenna can be obtained to produce more reasonable NSA values for a test site. Secondly, this thesis proposes a simple, fast, and accurate method to calibrate the free-space AFs of broadband EMC (Electromagnetic Compatibility) antennas. This method adopts a fixed-height configuration and a MUSIC (MUltiple SIgnal Classification) algorithm. This configuration significantly shortens measurement time and removes height-dependent calibration errors. Meanwhile, the MUSIC algorithm can remove unexpected reflections from the ground plane or any other reflecting objects, by which the free-space AFs can be calculated. In addition, this method can also automatically compensate for the phase center shift, which makes measurement easier and more convenient. To verify this method, the calibrated results are compared with other published standard methods: the mean differences can be as low as 0.25 dB for the LPDA (log-periodic dipole array), 0.42 dB for the hybrid antennas, and 0.36 dB for the horn antennas. Finally, this thesis provides a method of using two equivalent negative inductances from two terminals of three coupled inductors to reduce the parasitic inductances of a typical three-capacitor EMI (Electromagnetic Interference) filter. Theoretical analysis and formula deduction for the design of two equivalent negative inductances are demonstrated. The experimental results show that the insertion losses of a three-capacitor EMI filter at 50 MHz can be reduced by 16.8 dB for the DM (differential-mode) and by 19.2 dB for the CM (common-mode). In Appendix A of this thesis, an extended study of the effect of ground plane on antenna¡¦s radiation is described. A simple V-shape edge-groove design for a finite ground plane can effectively reduce the pattern ripples of a monopole. The optimal design of proposed structure can reduce the peak-to-peak pattern ripples from 26 to 4.5 dB.

Deep learning is increasingly becoming a viable way of classifying all types of data. Modern deep learning algorithms, such as one dimensional convolutional neural networks, have demonstrated excellent performance in classifying time series data because of the ability to identify time invariant features. A primary challenge of deep learning for time series classification is the large amount of data required for training and many application domains, such as in medicine, have challenges obtaining sufficient data. Transfer learning is a deep learning method used to apply feature knowledge from one deep learning model to another; this is a powerful tool when both training datasets are similar and offers smaller datasets the power of more robust larger datasets. This makes it vital that the best source dataset is selected when performing transfer learning and presently there is no metric for this purpose. In this thesis a metric of predicting the performance of transfer learning is proposed. To develop this metric this research will focus on classification and transfer learning for human-activity-recognition time series data. For general time series data, finding temporal relations between signals is computationally intensive using non-deep learning techniques. Rather than time-series signal processing, a neural network autoencoder was used to first transform the source and target datasets into a time independent feature space. To compare and quantify the suitability of transfer learning datasets, two metrics were examined: i) average embedded signal from each dataset was used to calculate the distance between each datasets centroid, and ii) a Generative Adversarial Network (GAN) model was trained and the discriminator portion of the GAN is then used to assess the dissimilarity between source and target. This thesis measures a correlation between the distance between two dataset and their similarity, as well as the ability for a GAN to discriminate between two datasets and their similarity. The discriminator metric, however, does suffer from an upper limit of dissimilarity. These metrics were then used to predict the success of transfer learning from one dataset to another for the purpose of general time series classification. / Thesis / Master of Applied Science (MASc) / Over the past decade, advances in computational power and increases in data quantity have made deep learning a useful method of complex pattern recognition and classification in data. There is a growing desire to be able to use these complex algorithms on smaller quantities of data. To achieve this, a deep learning model is first trained on a larger dataset and then retrained on the smaller dataset; this is called transfer learning. For transfer learning to be effective, there needs to be a level of similarity between the two datasets so that properties from larger dataset can be learned and then refined using the smaller dataset. Therefore, it is of great interest to understand what level of similarity exists between the two datasets. The goal of this research is to provide a similarity metric between two time series classification datasets so that potential performance gains from transfer learning can be better understood. The measure of similarity between two time series datasets presents a unique challenge due to the nature of this data. To address this challenge an encoder approach was implemented to transform the time series data into a form where each signal example can be compared against one another. In this thesis, different similarity metrics were evaluated and correlated to the performance of a deep learning model allowing the prediction of how effective transfer learning may be when applied.

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Le Contrôle Non Destructif (CND) de matériaux composites multicouches pour des problèmes de qualité, viabilité, sécurité et disponibilité des systèmes qui impliquent des pièces fabriquées dans les industries aéronautiques et de l’automobile est devenu une tâche essentielle aujourd’hui. L'objectif visé par cette thèse est l’imagerie électromagnétique de structures complexes multicouches anisotropes, de plus en plus utilisées dans des applications, et encore source de sérieux défis à l'étape de leur modélisation et encore plus à l'étape souvent en enfance de leur imagerie. En utilisant une vaste gamme de fréquences, qui va des courants de Foucault jusqu’aux micro-ondes, il y a un fort besoin de rendre disponibles des procédures de modélisation et d'imagerie qui sont robustes, rapides, précises et utiles à la décision des utilisateurs finaux sur des défauts potentiels, tant donc en basse fréquence (BF) (matériaux conducteurs, type fibre de carbone) qu’en haute fréquence (HF) (matériaux diélectriques, type fibre de verre). De plus, il est important d'obtenir des résultats en des temps brefs. Cependant, cela nécessite la connaissance d’une réponse précise à des sources externes aux multicouches, en considérant les couches des composites comme non endommagées ou endommagées : on parle donc de solution du problème direct, avec le cas particulier de sources élémentaires conduisant aux dyades de Green (DGF). La modélisation et la simulation numérique du problème direct sont gérés principalement via une solution au premier ordre de la formulation intégrale de contraste de source impliquant le tenseur de dépolarisation des défauts, quand ceux-ci sont assez petits vis-à-vis de l’épaisseur de peau locale (cas BF) ou de la longueur d'onde locale (cas HF). La précision des DGF doit nécessairement être assurée alors, même si les sources se situent loin de l'origine, ce qui donne un spectre de dyades qui oscille très rapidement. La technique d'interpolation-intégration dite de Padua-Domínguez est ainsi introduite dans le but d'évaluer de façon efficace des intégrales fortement oscillantes.Néanmoins, les matériaux composites peuvent souffrir de divers défauts, lors du processus de fabrication ou pendant leurs utilisations. Vides d’air, cavités remplies de liquide, fissures, etc., peuvent affecter le fonctionnement correct des structures composites. Il est donc indispensable de pouvoir détecter la présence des défauts. Ici, l’insistance est sur la méthode bien connue d’imagerie dite MUltiple SIgnal Classification (MUSIC), qui est basée sur la décomposition en valeurs singulières (SVD) des DGF ; celle-ci est développée afin de localiser les positions de multiples petits défauts volumiques en interaction faible enfouis dans des milieux anisotropes uniaxiaux. Le principal inconvénient de la méthode MUSIC est cependant sa sensibilité par rapport au bruit. Par conséquent, des méthodes MUSIC avec une résolution améliorée et la Recursively Applied and Projected (RAP) MUSIC sont introduites afin de surmonter un tel inconvénient de l'algorithme standard et de fournir des résultats de qualité avec une meilleure résolution. De nombreuses simulations numériques illustrent ces investigations. / Non-Destructive Testing/Evaluation (NdT/E) of multi-layered composite materials for problems of quality, viability, safety and availability of systems involving manufactured parts (in aeronautics and in automotive industry, as a good example) has become an interesting and challenging task nowadays. The focus of the PhD thesis is on the electromagnetic (EM) imaging of complex anisotropic multi-slab composite panels as increasingly encountered in applications, yet source of strong challenges at modeling stage and even more at often-in-infancy imaging stage. From eddy-currents to microwaves, there is a strong need to make available modeling and imaging procedures that are robust, fast, accurate and useful to potential end-users’ decision about potential defects both at low-frequency (LF) (conductive materials, carbon-fiber like) and high-frequency (HF) (dielectric materials, glass-fiber like). Moreover, it is important to get the results in close-to-real-time. However, this requires an accurate response to external sources of the multilayers, considering the layers which these composite structures are made of as undamaged or damaged. The modeling at forward stage is managed via a first-order solution involving the dyadic Green’s functions (DGF) of the layers along with the depolarization tensor of the assumed defects when they are small enough vis-à-vis the skin depth (LF case) or the wavelength (HF case). The accuracy of the DGF has to be ensured even if the sources lie far away from the origin, which yields a fast-oscillating spectrum of the dyads. The Padua-Domínguez interpolation-integration technique is introduced herein in order to evaluate in an effective fashion fast-oscillating integrals.Damages or disorders, which these composite structures may suffer from, are of many kinds. One could mention voids, fluid-filled cavities or uniaxial defects with obvious impacts on the electromagnetic and geometric parameters of the multilayers. That is, the task to make available to end-users imaging algorithms tailored to detect the presence of defects. The well-known standard MUltiple SIgnal Classification (MUSIC) algorithm, which is based on the Singular Value Decomposition (SVD) of such DGF, is here applied to localize the positions of small multiple defects with weak interaction embedded in anisotropic uniaxial media. The main drawback of MUSIC is its sensitivity with respect to the noise. Therefore, MUSIC with enhanced resolution and Recursively Applied and Projected (RAP) MUSIC are introduced to overcome such a drawback of the standard algorithm and to provide quality results with better resolution.