A new broadband beamformer composed of nested arrays (NAs), multi-dimensional (MD) filters, and multirate techniques is proposed for both linear and planar arrays. It is shown that this combination results in frequency-invariant response. For a given number of sensors, the advantage of using NAs is that the effective aperture for low temporal frequencies is larger than in the case of using uniform arrays. This leads to high spatial selectivity for low frequencies. For a given aperture size, the proposed beamformer can be implemented with significantly fewer sensors and less computation than uniform arrays with a slight deterioration in performance. Taking advantage of the Noble identity and polyphase structures, the proposed method can be efficiently implemented. Simulation results demonstrate the good performance of the proposed beamformer in terms of frequency-invariant response and computational requirements. The broadband beamformer requires a filter bank with a non-compatible set of sampling rates which is challenging to be designed. To address this issue, a filter bank design approach is presented. The approach is based on formulating the design problem as an optimization problem with a performance index which consists of a term depending on perfect reconstruction (PR) and a term depending on the magnitude specifications of the analysis filters. The design objectives are to achieve almost perfect reconstruction (PR) and have the analysis filters satisfying some prescribed frequency specifications. Several design examples are considered to show the satisfactory performance of the proposed method. A new blind multi-stage space-time equalizer (STE) is proposed which can separate narrowband sources from a mixed signal. Neither the direction of arrival (DOA) nor a training sequence is assumed to be available for the receiver. The beamformer and equalizer are jointly updated to combat both co-channel interference (CCI) and inter-symbol interference (ISI) effectively. Using subarray beamformers, the DOA, possibly time-varying, of the captured signal is estimated and tracked. The estimated DOA is used by the beamformer to provide strong CCI cancellation. In order to alleviate inter-stage error propagation significantly, a mean-square-error sorting algorithm is used which assigns detected sources to different stages according to the reconstruction error at different stages. Further, to speed up the convergence, a simple-yet-efficient DOA estimation algorithm is proposed which can provide good initial DOAs for the multi-stage STE. Simulation results illustrate the good performance of the proposed STE and show that it can effectively deal with changing DOAs and time variant channels. / Graduate / 0544 / imanmoaz@uvic.ca

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.

L’objectif de notre recherche est de comprendre les bases cérébrales de l’interaction sociale dans un contexte de performance musicale grâce à des outils issus des neurosciences (électroencéphalographie : EEG) et du traitement du signal. Ce manuscrit présente tout d’abord un état de l’art des études récentes dans le domaine de l’hyperscanning. Nous offrons une réflexion sur les prérequis et la méthodologie à adopter pour concevoir une expérience prédisposant à l’émergence d’une synchronisation neuronale. Nous explorons ensuite les processus cérébraux mis en jeu lors de la pratique de la musique au travers d’études réalisées en neurosciences. Par la suite nous présentons plusieurs méthodes permettant de calculer des indices de couplage cérébral sur les données récoltées lors d’expériences en hyperscanning. Nous y décrivons en particulier les méthodes de séparation de source conjointe (jBSS) dont l’avantage est de se rapprocher d’une réalité anatomique et physiologique, ainsi que de prendre en compte l’information inter-sujets lors de l’estimation des sources. Enfin, nous détaillons notre contribution au champ des neurosciences sociales sous la forme d’une expérience longitudinale en hyperscanning-EEG. Elle étudie l’interaction sociale de pianistes à quatre mains lors de l’apprentissage d’un morceau de musique sur une période de deux mois. Nous mettons en évidence qu’il existe une corrélation entre l’augmentation de la performance musicale au cours du temps, la synchronisation cérébrale et la qualité de la relation entre les musiciens. / The aim of our research is to understand the neural bases of social interaction in a musical performance context with tools from neuroscience (electroencephalography: EEG) and signal processing. This manuscript first presents a state of the art of recent studies in the field of hyperscanning. We introduce our recommendations on the prerequisites and methodology to design experiments facilitating the emergence of neuronal synchronization. We then explore the cerebral processes involved in the practice of music through studies in neuroscience of music. Subsequently we present several methods to calculate brain coupling on data collected during experiments in hyperscanning. We describe in particular the methods of joint blind source separation (jBSS) whose advantages are to approach anatomical and physiological reality, as well as taking into account inter-subject information when estimating sources. Finally, we detail our contribution to the field of social neuroscience with a longitudinal experience in hyperscanning-EEG. We studied social interaction from musicians playing four hands piano over a two-month period. We highlight a correlation between increased musical performance over time, cerebral synchronization and quality of the relationship between the pianists.

La séparation aveugle de sources aveugle (BSS) est une technique d’estimation des différents signaux observés au travers de leurs mélanges à l’aide de plusieurs capteurs, lorsque le mélange et les signaux sont inconnus. Bien qu’il ait été démontré mathématiquement que pour des mélanges linéaires, sous des conditions faibles, des sources mutuellement indépendantes peuvent être estimées, il n’existe dans de résultats théoriques généraux dans le cas de mélanges non-linéaires. La littérature sur ce sujet est limitée à des résultats concernant des mélanges non linéaires spécifiques.Dans la présente étude, le problème est abordé en utilisant une nouvelle approche utilisant l’information temporelle des signaux. L’idée originale conduisant à ce résultat, est d’étudier le problème de mélanges linéaires, mais variant dans le temps, déduit du problème non linéaire initial par dérivation. Il est démontré que les contre-exemples déjà présentés, démontrant l’inefficacité de l’analyse par composants indépendants (ACI) pour les mélanges non-linéaires, perdent leur validité, considérant l’indépendance au sens des processus stochastiques, au lieu de l’indépendance au sens des variables aléatoires. Sur la base de cette approche, de bons résultats théoriques et des développements algorithmiques sont fournis. Bien que ces réalisations ne soient pas considérées comme une preuve mathématique de la séparabilité des mélanges non-linéaires, il est démontré que, compte tenu de quelques hypothèses satisfaites dans la plupart des applications pratiques, elles sont séparables.De plus, les BSS non-linéaires pour deux ensembles utiles de signaux sources sont également traités, lorsque les sources sont (1) spatialement parcimonieuses, ou (2) des processus Gaussiens. Des méthodes BSS particulières sont proposées pour ces deux cas, dont chacun a été largement étudié dans la littérature qui correspond à des propriétés réalistes pour de nombreuses applications pratiques.Dans le cas de processus Gaussiens, il est démontré que toutes les applications non-linéaires ne peuvent pas préserver la gaussianité de l’entrée, cependant, si on restreint l’étude aux fonctions polynomiales, la seule fonction préservant le caractère gaussiens des processus (signaux) est la fonction linéaire. Cette idée est utilisée pour proposer un algorithme de linéarisation qui, en cascade par une méthode BSS linéaire classique, sépare les mélanges polynomiaux de processus Gaussiens.En ce qui concerne les sources parcimonieuses, on montre qu’elles constituent des variétés distinctes dans l’espaces des observations et peuvent être séparées une fois que les variétés sont apprises. À cette fin, plusieurs problèmes d’apprentissage multiple ont été généralement étudiés, dont les résultats ne se limitent pas au cadre proposé du SRS et peuvent être utilisés dans d’autres domaines nécessitant un problème similaire. / Blind Source Separation (BSS) is a technique for estimating individual source components from their mixtures at multiple sensors, where the mixing model is unknown. Although it has been mathematically shown that for linear mixtures, under mild conditions, mutually independent sources can be reconstructed up to accepted ambiguities, there is not such theoretical basis for general nonlinear models. This is why there are relatively few results in the literature in this regard in the recent decades, which are focused on specific structured nonlinearities.In the present study, the problem is tackled using a novel approach utilizing temporal information of the signals. The original idea followed in this purpose is to study a linear time-varying source separation problem deduced from the initial nonlinear problem by derivations. It is shown that already-proposed counter-examples showing inefficiency of Independent Component Analysis (ICA) for nonlinear mixtures, loose their validity, considering independence in the sense of stochastic processes instead of simple random variables. Based on this approach, both nice theoretical results and algorithmic developments are provided. Even though these achievements are not claimed to be a mathematical proof for the separability of nonlinear mixtures, it is shown that given a few assumptions, which are satisfied in most practical applications, they are separable.Moreover, nonlinear BSS for two useful sets of source signals is also addressed: (1) spatially sparse sources and (2) Gaussian processes. Distinct BSS methods are proposed for these two cases, each of which has been widely studied in the literature and has been shown to be quite beneficial in modeling many practical applications.Concerning Gaussian processes, it is demonstrated that not all nonlinear mappings can preserve Gaussianity of the input. For example being restricted to polynomial functions, the only Gaussianity-preserving function is linear. This idea is utilized for proposing a linearizing algorithm which, cascaded by a conventional linear BSS method, separates polynomial mixturesof Gaussian processes.Concerning spatially sparse sources, it is shown that spatially sparsesources make manifolds in the observations space, and can be separated once the manifolds are clustered and learned. For this purpose, multiple manifold learning problem has been generally studied, whose results are not limited to the proposed BSS framework and can be employed in other topics requiring a similar issue.

Notre recherche vise à explorer les bases cérébrales de l'interaction sociale, par le biais notamment de l'électro-encéphalographie synchrone de plusieurs individus (hyperscanning-EEG). Cette thèse s'articule autour de trois volets théoriques, méthodologiques et expérimentaux complémentaires dans leur fonction. En premier lieu, nous proposons deux cadres conceptuels éclairant l'analyse des synchronies interindividuelles (couplage) chez l'humain. Le premier cadre s'intéresse aux conditions d'apparition du couplage, que l'on présente en considérant les principes fondamentaux qui semblent prédisposer à son émergence. Le second cadre théorique a pour but d'appréhender les rôles fonctionnels possibles du couplage. On y propose une taxonomie des processus de cognition sociale explorés au cours d'expériences sur le couplage entre sujets en interaction. En second lieu, des travaux en traitement du signal visent à développer des méthodes d'analyse adéquates pour les données produites au cours d'expérimentations en hyperscanning. On s'intéresse en particulier aux approches de séparation de source conjointe (JBSS) permettant de prendre en compte l'information inter-sujet dans la séparation. L'avantage de ces développements récents sur les méthodes classiques est démontré par une étude comparative de leurs performances sur données réelles acquises en hyperscanning-EEG. En dernier lieu, on contribue au champ des neurosciences sociales à travers une étude hyperscanning-EEG qui porte sur les effets du toucher affectif sur le couplage des activités cérébrales et physiologiques entre individus en interaction. Nous montrons que, chez des partenaires en couple, le toucher peut accroitre la dépendance des activités physiologiques, un résultat qui appuie son rôle particulier pour la communication et le support affectif au sein des relations intimes. / This research aims at exploring the neural bases of social interaction with use ofelectroencephalography acquired simultaneously from multiple individuals (hyperscanning-EEG). Wedo so by contributing on three complementary aspects at a theoretical, methodological andexperimental level. First, we provide two theoretical frameworks that can guide and inform theanalysis of interpersonal synchronies. Our first frame focuses on the necessary conditions for brain-tobraincoupling to occur. Our second frame provides a taxonomy for social cognition and situatesexisting studies in the neuroscience of social interaction. Second, we advance on the methodologicalaspects by extending the analysis framework, thereby considering and benefiting from hyperscanning-EEG data. Specifically, we focus on Joint Blind Source Separation (JBSS), a novel signal processing toolthat can take into account brain-to-brain coupling in the estimation of cortical activity. We provide acomparative study of such algorithms on real-world hyperscanning-EEG data and we demonstratetheir appropriateness for analysis of joint neural data. Finally, we contribute experimentally with ahyperscanning-EEG study in which we investigate the effect of affective touch on interpersonalcoupling during a natural interaction. We show that touch can increase coupling of physiologicalactivities between romantic partners. This results supports an instrumental role of interactive touchfor affective communication and support in close relationships.

Acquises dans plusieurs centaines de bandes spectrales contiguës, les images hyperspectrales permettent d'analyser finement la composition d'une scène observée. En raison de la résolution spatiale limitée des capteurs utilisés, le spectre d'un pixel d'une image hyperspectrale résulte de la composition de plusieurs signatures associées à des matériaux distincts. À ce titre, le démélange d'images hyperspectrales vise à estimer les signatures des différents matériaux observés ainsi que leur proportion dans chacun des pixels de l'image. Pour cette analyse, il est d'usage de considérer qu'une signature spectrale unique permet de décrire un matériau donné, ce qui est généralement intrinsèque au modèle de mélange choisi. Toutefois, la signature d'un matériau présente en pratique une variabilité spectrale qui peut être significative d'une image à une autre, voire au sein d'une même image. De nombreux paramètres peuvent en être cause, tels que les conditions d'acquisitions (e.g., conditions d'illumination locales), la déclivité de la scène observée ou des interactions complexes entre la lumière incidente et les éléments observés. À défaut d'être prises en compte, ces sources de variabilité perturbent fortement les signatures extraites, tant en termes d'amplitude que de forme. De ce fait, des erreurs d'estimation peuvent apparaître, qui sont d'autant plus importantes dans le cas de procédures de démélange non-supervisées. Le but de cette thèse consiste ainsi à proposer de nouvelles méthodes de démélange pour prendre en compte efficacement ce phénomène. Nous introduisons dans un premier temps un modèle de démélange original visant à prendre explicitement en compte la variabilité spatiale des spectres purs. Les paramètres de ce modèle sont estimés à l'aide d'un algorithme d'optimisation sous contraintes. Toutefois, ce modèle s'avère sensible à la présence de variations spectrales abruptes, telles que causées par la présence de données aberrantes ou l'apparition d'un nouveau matériau lors de l'analyse d'images hyperspectrales multi-temporelles. Pour pallier ce problème, nous introduisons une procédure de démélange robuste adaptée à l'analyse d'images multi-temporelles de taille modérée. Compte tenu de la dimension importante des données étudiées, notamment dans le cas d'images multi-temporelles, nous avons par ailleurs étudié une stratégie d'estimation en ligne des différents paramètres du modèle de mélange proposé. Enfin, ce travail se conclut par l'étude d'une procédure d'estimation distribuée asynchrone, adaptée au démélange d'un grand nombre d'images hyperspectrales acquises sur une même scène à différents instants. / Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data -- referred to as endmembers -- and their relative proportion in each pixel according to a predefined mixture model. In this context, a given material is commonly assumed to be represented by a single spectral signature. This assumption shows a first limitation, since endmembers may vary locally within a single image, or from an image to another due to varying acquisition conditions, such as declivity and possibly complex interactions between the incident light and the observed materials. Unless properly accounted for, spectral variability can have a significant impact on the shape and the amplitude of the acquired signatures, thus inducing possibly significant estimation errors during the unmixing process. A second limitation results from the significant size of HS data, which may preclude the use of batch estimation procedures commonly used in the literature, i.e., techniques exploiting all the available data at once. Such computational considerations notably become prominent to characterize endmember variability in multi-temporal HS (MTHS) images, i.e., sequences of HS images acquired over the same area at different time instants. The main objective of this thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability. Endmember variability is addressed by considering an explicit variability model reminiscent of the total least squares problem, and later extended to account for time-varying signatures. The variability is first estimated using an unsupervised deterministic optimization procedure based on the Alternating Direction Method of Multipliers (ADMM). Given the sensitivity of this approach to abrupt spectral variations, a robust model formulated within a Bayesian framework is introduced. This formulation enables smooth spectral variations to be described in terms of spectral variability, and abrupt changes in terms of outliers. Finally, the computational restrictions induced by the size of the data is tackled by an online estimation algorithm. This work further investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models.

En acoustique sous marine, les ´etudes sur les zones en eau peu profondes sontredevenues strat´egiques. Cette th`ese porte sur l’ ´etude de la s´eparation et la d´etectionde trajet dans le cadre des eaux peu profondes tomographie acoustique oc´eanique. Dansune premi´ere ´etape de notre travail, nous avons donn´e un bref aperc¸u sur les techniquesexistantes de traitement acoustique sous-marine afin de trouver la difficult´e toujoursconfront´es `a ce type de m´ethodes. Par cons´equent, nous avons fait une conclusion qu’ilest encore n´e cessaire d’am´eliorer la r´esolution de s´eparation afin de fournir des informationsplus utiles pour l’ ´etape inverse de la tomographie acoustique oc´eanique.Ainsi, une enquˆete sur les mthodes haute r´esolution est effecut´ee. Enfin, nous avonspropos´e une m´ethode `a haute r´esolution appel´ee lissage MUSICAL (MUSIC Active largeband), qui combine le lissage de fr´equence spatiale avec l’algorithme MUSICAL, pourune s´eparation efficace de trajet coh´erentes ou totalement corr´el´es. Cependant, cettem´ethode est bas´ee sur la connaissance a priori du nombre de trajet. Ainsi, nous introduisonsun test (exponential fitting test) (EFT) `a l’aide de courte longueur des ´echantillonspour d´eterminer le nombre de trajets. Ces deux m´ethodes sont appliqu´ees `a la fois desdonn´ees synth´etiques et les donn´ees r´eelles acquises dans un r´eservoir `a petite ´echelle.Leurs performances sont compar´ees avec les m´ethodes conventionnelles pertinentes. / As the studies on shallow-water acoustics became an active field again, this dissertationfocuses on studying the separation and detection of raypaths in the context of shallowwaterocean acoustic tomography. As a first step of our work, we have given a briefreview on the existing array processing techniques in underwater acoustics so as to findthe difficulties still faced by these methods. Consequently, we made a conclusion thatit is still necessary to improve the separation resolution in order to provide more usefulinformation for the inverse step of ocean acoustic tomography. Thus, a survey on highresolutionmethod is provided to discover the technique which can be extended to separatethe raypaths in our application background. Finally, we proposed a high-resolutionmethod called smoothing-MUSICAL (MUSIC Actif Large band), which combines thespatial-frequency smoothing with MUSICAL algorithm, for efficient separation of coherentor fully correlated raypaths. However, this method is based on the prior knowledgeof the number of raypaths. Thus, we introduce an exponential fitting test (EFT)using short-length samples to determine the number of raypaths. These two methodsare both applied to synthetic data and real data acquired in a tank at small scale. Theirperformances are compared with the relevant conventional methods respectively.

Made available in DSpace on 2014-12-17T14:55:16Z (GMT). No. of bitstreams: 1 FranciscoJTV_TESE.pdf: 16694617 bytes, checksum: 98c04bab1f2a3180ba8bd87b03174888 (MD5) Previous issue date: 2013-05-24 / The exponential growth in the applications of radio frequency (RF) is accompanied by great challenges as more efficient use of spectrum as in the design of new architectures for multi-standard receivers or software defined radio (SDR) . The key challenge in designing architecture of the software defined radio is the implementation of a wide-band receiver, reconfigurable, low cost, low power consumption, higher level of integration and flexibility. As a new solution of SDR design, a direct demodulator architecture, based on fiveport technology, or multi-port demodulator, has been proposed. However, the use of the five-port as a direct-conversion receiver requires an I/Q calibration (or regeneration) procedure in order to generate the in-phase (I) and quadrature (Q) components of the transmitted baseband signal. In this work, we propose to evaluate the performance of a blind calibration technique without additional knowledge about training or pilot sequences of the transmitted signal based on independent component analysis for the regeneration of I/Q five-port downconversion, by exploiting the information on the statistical properties of the three output signals / Estudos recentes apontam que o aumento nas aplica??es de r?dio frequ?ncia (RF) vem acompanhado por grandes desafios tanto no uso eficiente do espectro eletromagn?tico quanto no projeto de novas arquiteturas para receptores multi-padr?o, ou r?dio definidos por software (RDS). O principal desafio da arquitetura f?sica de um RDS ? a implementa??o de um receptor banda-larga com caracter?sticas de baixo custo, baixo consumo, maior grau de integra??o e flexibilidade. A arquitetura homodina, baseada na tecnologia cinco-portas, surge como uma alternativa para aplica??es em r?dio definidos por software. No entanto, a regenera??o das componentes em fase e quadratura, no receptor cinco-portas, comumente denominada de calibra??o, constitui um dos maiores desafios na aplica??o dessa tecnologia. Os m?todos de calibra??o, propostos na literatura, normalmente baseiam-se no conhecimento do modelo matem?tico do circuito, em que o mesmo ? calibrado previamente (off-line), para um tipo de sinal com caracter?sticas espec?ficas ou em tempo real, com base no conhecimento da sequ?ncia de aprendizagem e do tipo de modula??o. Nesse trabalho, ? apresentado uma proposta de regenera??o cega dessas componentes, para um receptor homodino cinco-portas, utilizando a abordagem denominada Separa??o Cega de Fontes (an?lise de componentes independentes - ICA), que explora as caracter?sticas estat?sticas dos tr?s sinais de sa?da do receptor cinco-portas. A valida??o dessa abordagem ? realizada por meio de simula??o e de resultados experimentais obtidos para o receptor cinco portas implementado em tecnologia de microfita

Cognitive radio technology has been an important area of research in telecommunications for solving the problem of spectrum scarcity. That\'s because in addition to allowing dynamic allocation of the electromagnetic spectrum, cognitive radios must be able to identify the non cognitive user\'s transmission on the channel. This operation is only possible through the continuous sensing of the electromagnetic spectrum. In this context, this paper presents a detailed study on spectrum sensing, an important stage in cognitive radio technology. For the presentation of this work, a detailed study on software dened radio (SDR) was carried out, without which it would be impossible to work with cognitive radios, once they are implemented by means of SDR technology. It was also presented the tools GNU Radio and USRP, which together form a solution of SDR, through implementation of AM receivers. The theoretical foundations of spectrum sensing and blind source separation (BSS) are presented and then is made a detailed study of the use of BSS for spectral sensing. From the study of BSS, it is possible to use new metrics for decision making about the presence or the absence of a primary user in the channel. Throughout the study, simulations and implementations were conducted on MATLAB in order to perform various situations, and, nally, it is presented outcomes and conclusions reached during the work. / A tecnologia de rádio cognitivo tem sido uma importante área de pesquisa em telecomunicações para a solução do problema da escassez espectral. Isto porque, além de permitirem a alocação dinâmica do espectro eletromagnético, os rádios cognitivos devem ser capazes de identificar as transmissões de usuários não cognitivos no canal. Esta operação só é possível por meio do sensoriamento contínuo do espectro eletromagnético. Neste contexto, este trabalho apresenta um estudo detalhado sobre o sensoriamento de espectro, uma importante etapa da tecnologia de rádios cognitivos. Para a apresentação deste trabalho foi realizado um estudo detalhado a respeito de rádio definido por software (SDR), sem o qual não seria possível o trabalho com rádios cognitivos, uma vez que este é implementado por meio da tecnologia de SDR. Também foram apresentadas as ferramentas GNU Radio e USRP, que, juntas, formam uma solução de SDR, por meio de implementações de receptores AM. Os fundamentos teóricos de sensoriamento de espectro e separação cega de fontes (BSS) são apresentados e, em seguida, é realizado um estudo aprofundado do uso de BSS para o sensoriamento espectral. A partir do estudo de BSS, é possível utilizar novas métricas de decisão a respeito da presença ou não de um usuário primário no canal. Durante todo este trabalho foram realizadas implementações e simulações no MATLAB com a finalidade de executar diversas situações e, finalmente, são apresentados resultados verificados e conclusões obtidas neste trabalho. / Mestre em Ciências

Este trabalho estuda o modelo de análise em componentes independentes (ICA) para misturas instantâneas, aplicado na separação de sinais de áudio. Três algoritmos de separação de misturas instantâneas são avaliados: FastICA, PP (Projection Pursuit) e PearsonICA; possuindo dois princípios básicos em comum: as fontes devem ser independentes estatisticamente e não-Gaussianas. Para analisar a capacidade de separação dos algoritmos foram realizados dois grupos de experimentos. No primeiro grupo foram geradas misturas instantâneas, sinteticamente, a partir de sinais de áudio pré-definidos. Além disso, foram geradas misturas instantâneas a partir de sinais com características específicas, também geradas sinteticamente, para avaliar o comportamento dos algoritmos em situações específicas. Para o segundo grupo foram geradas misturas convolutivas no laboratório de acústica do LPS. Foi proposto o algoritmo PP, baseado no método de Busca de Projeções comumente usado em sistemas de exploração e classificação, para separação de múltiplas fontes como alternativa ao modelo ICA. Embora o método PP proposto possa ser utilizado para separação de fontes, ele não pode ser considerado um método ICA e não é garantida a extração das fontes. Finalmente, os experimentos validam os algoritmos estudados. / This work studies Independent Component Analysis (ICA) for instantaneous mixtures, applied to audio signal (source) separation. Three instantaneous mixture separation algorithms are considered: FastICA, PP (Projection Pursuit) and PearsonICA, presenting two common basic principles: sources must be statistically independent and non-Gaussian. In order to analyze each algorithm separation capability, two groups of experiments were carried out. In the first group, instantaneous mixtures were generated synthetically from predefined audio signals. Moreover, instantaneous mixtures were generated from specific signal generated with special features, synthetically, enabling the behavior analysis of the algorithms. In the second group, convolutive mixtures were probed in the acoustics laboratory of LPS at EPUSP. The PP algorithm is proposed, based on the Projection Pursuit technique usually applied in exploratory and clustering environments, for separation of multiple sources as an alternative to conventional ICA. Although the PP algorithm proposed could be applied to separate sources, it couldnt be considered an ICA method, and source extraction is not guaranteed. Finally, experiments validate the studied algorithms.