Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Made available in DSpace on 2014-12-17T14:55:49Z (GMT). No. of bitstreams: 1 VitorLS_DISSERT.pdf: 1833534 bytes, checksum: 72ebc7d9d8be6ba8ae53eaad106afa8d (MD5) Previous issue date: 2011-03-03 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering / Following the new tendency of interdisciplinarity of modern science, a new field called neuroengineering has come to light in the last decades. After 2000, scientific journals and conferences all around the world have been created on this theme. The present work comprises three different subareas related to neuroengineering and electrical engineering: neural stimulation; theoretical and computational neuroscience; and neuronal signal processing; as well as biomedical engineering. The research can be divided in three parts: (i) A new method of neuronal photostimulation was developed based on the use of caged compounds. Using the inhibitory neurotransmitter GABA caged by a ruthenium complex it was possible to block neuronal population activity using a laser pulse. The obtained results were evaluated by Wavelet analysis and tested by non-parametric statistics. (ii) A mathematical method was created to identify neuronal assemblies. Neuronal assemblies were proposed as the basis of learning by Donald Hebb remain the most accepted theory for neuronal representation of external stimuli. Using the Marcenko-Pastur law of eigenvalue distribution it was possible to detect neuronal assemblies and to compute their activity with high temporal resolution. The application of the method in real electrophysiological data revealed that neurons from the neocortex and hippocampus can be part of the same assembly, and that neurons can participate in multiple assemblies. (iii) A new method of automatic classification of heart beats was developed, which does not rely on a data base for training and is not specialized in specific pathologies. The method is based on Wavelet decomposition and normality measures of random variables. Throughout, the results presented in the three fields of knowledge represent qualification in neural and biomedical engineering

No description available.

Cette thèse est dédiée à l'analyse de signaux électriques quantiques dans les canaux de bords de l'effet Hall quantique. En particulier, j'ai utilisé l'analogue électronique de l'interféromètre de Hong, Ou et Mandel pour réaliser des expériences d'interférométrie à deux particules. En entrée de l'interféromètre sont placées des sources d'électrons uniques qui permettent l'injection contrôlée d'excitation ne contenant qu'une seule particule. Les canaux de bords guident ces excitations jusqu'à l'interféromètre. Il s'agit d'un contact ponctuel quantique qui agit comme une lame semi-réfléchissante pour les électrons. On mesure en sortie les fluctuations basse fréquence du courant. Cela nous permet de mesurer le recouvrement entre les fonctions d'onde à un électron émises à chaque entrée. Grâce à cette mesure de recouvrement, j'ai pu caractériser à des échelles de temps sub-nanoseconde, le rôle des interactions Coulombienne sur la propagation de l'électron unique. J'ai pu montrer que ces interactions étaient la source principale de la décohérence du paquet d'onde mono-électronique et qu'elles décomposent l'électron sur des modes collectifs. C'est une manifestation de la fractionalisation de l'électron qui apparaît dans les systèmes uni-dimensionnel en interactions. Grâce à cet interféromètre, j'ai pu aussi implémenter un protocole de tomographie qui permet de reconstruire toute les informations à une particule de n'importe quel signal émis dans le canal de bord. / This thesis is dedicated to processing of quantum electronic signals in the edge channels of the integer quantum Hall effect. In particular, I used the electronic analogue of the Hong, Ou and Mandel interferometer to realize two particle interference measurements. The interferometer consists of a quantum point contact (QPC) that acts as an electronic beam-splitter. The inputs are fed by single electron sources whose single particle excitations are guided toward the QPC by quantum Hall edge channels. We measure low frequency current noise in one of the output to measure overlaps of first order coherence functions. With this interferometer I could characterize on short time scales the role of Coulomb interactions on single electron propagation. I could show that interactions are the main source of decoherence of the single particle wave packet and that the electron decomposes into collective modes. This is due to fractionalisation which is a hallmark of interacting unidimensional systems. Thanks to this interferometer I could also implement a universal tomography protocol to dissect all single particle information of any arbitrary current. This enables the study of non-classical propagating state.

The radar transmitter identification problem involves the identification of a specific radar transmitter based on a received pulse. The radar transmitters are of identical make and model. This makes the problem challenging since the differences between radars of identical make and model will be solely due to component tolerances and variation. Radar pulses also vary in time and frequency which means that the problem is non-stationary. Because of this fact, time-frequency representations such as shift-invariant quadratic time-frequency representations (Cohen’s class) and wavelets were used. A model for a radar transmitter was developed. This consisted of an analytical solution to a pulse-forming network and a linear model of an oscillator. Three signal classification algorithms were developed. A signal classifier was developed that used a radially Gaussian Cohen’s class transform. This time-frequency representation was refined to increase the classification accuracy. The classification was performed with a support vector machine classifier. The second signal classifier used a wavelet packet transform to calculate the feature values. The classification was performed using a support vector machine. The third signal classifier also used the wavelet packet transform to calculate the feature values but used a Universum type classifier for classification. This classifier uses signals from the same domain to increase the classification accuracy. The classifiers were compared against each other on a cubic and exponential chirp test problem and the radar transmitter model. The classifier based on the Cohen’s class transform achieved the best classification accuracy. The classifier based on the wavelet packet transform achieved excellent results on an Electroencephalography (EEG) test dataset. The complexity of the wavelet packet classifier is significantly lower than the Cohen’s class classifier. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted

La presente tesis cuenta con dos objetivos. Por una parte introducir y validar un método de análisis de corrientes estatóricas para el diagnóstico de máquinas de inducción conectadas a la red que operan en estado transitorio, basado en el uso de filtros de rechazo de frecuencia en combinación con la distribución de Wigner-Ville, con especial interés en su aplicación para la detección incipiente de defectos. El segundo objetivo consiste en replicar de la manera más fidedigna posible el proceso de rotura de una barra en el rótor de un motor de inducción. Para ello se ha diseñado un ensayo encaminado a provocar dicha avería sometiendo un motor a fatiga. Con este objetivo se ha construido un banco de pruebas y emplazado los sensores necesarios, así como un sistema de recogida de datos de manera automatizada. Adicionalmente, se ha diseñado los programas de procesamiento de los mismos, también para ser llevado a cabo de la manera lo más desatendida posible. / Climente Alarcón, V. (2012). Aportación al mantenimiento predictivo de motores de inducción mediante modernas técnicas de análisis de la señal [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15915

Mon travail de thèse est consacré à l’étude de quelques aspects de la physique quantique des systèmes quantiques à N corps en interaction. Il est orienté vers l’étude des chaînes de spins quantiques. Je me suis intéressé à plusieurs questions relatives aux chaînes de spins quantiques, du point de vue numérique et analytique à la fois. J'aborde en particulier les questions relatives à la structure des fonctions d'onde, la forme de la densité d'états et les propriétés spectrales des Hamiltoniens de chaînes de spins. Dans un premier temps, je présenterais très rapidement les techniques numériques de base pour le calcul des vecteurs et valeurs propres des Hamiltonien de chaînes de spins. Les densités d’états des modèles quantiques constituent des quantités importantes et très simples qui permettent de caractériser les propriétés spectrales des systèmes avec un grand nombre de degrés de liberté. Alors que dans la limite thermodynamique, les densités d'états de la plupart des modèles intégrables sont bien décrites par une loi gaussienne, dans certaines limites de couplage de la chaîne de spins au champ magnétique et pour un nombre de spins N fini sur la chaîne, on observe l’apparition de pics dans la densité d’états. Je montrerais que la connaissance des deux premiers moments du Hamiltonien dans le sous-espace dégénéré associé à chaque pics donne une bonne approximation de la densité d’états. Dans un deuxième temps je m'intéresserais aux propriétés spectrales des Hamiltoniens de chaînes de spins quantiques. L’un des principal résultats sur la statistique spectrale des systèmes quantiques concerne le comportement universel des fluctuations des mesures telles que l’espacement entre valeurs propres consécutives. Ces fluctuations sont bien décrites par la théorie des matrices aléatoires mais la comparaison avec les prédictions de cette théorie nécessite généralement une opération sur le spectre du Hamiltonien appelée unfolding. Dans les problèmes quantiques de N corps, la taille de l’espace de Hilbert croît généralement exponentiellement avec le nombre de particules, entraînant un manque de données pour pouvoir faire une statistique. Ces limitations ont amené l’introduction d’une nouvelle mesure se passant de la procédure d’unfolding basée sur le rapport d’espacements successifs plutôt que les espacements. En suivant l’idée du “surmise” de Wigner pour le calcul de la distribution de l’espacement, je montre comment calculer une approximation de la distribution du rapport d’espacements dans les trois ensembles gaussiens invariants en faisant le calcul pour des matrices 3x3. Les résultats obtenus pour les différents ensembles de matrices aléatoires se sont révélés être en excellent accord avec les résultats numériques. Enfin je m’intéresserais à la structure des fonctions d’ondes fondamentales des modèles de chaînes de spins quantiques. Les fonctions d’onde constituent, avec le spectre en énergie, les objets fondamentaux des systèmes quantiques : leur structure est assez compliquée et n’est pas très bien comprise pour la plupart des systèmes à N corps. En raison de la croissance exponentielle de la taille de l’espace de Hilbert avec le nombre de particules, l’étude des vecteurs propres est une tâche très difficile, non seulement du point de vue analytique mais aussi du point de vue numérique. Je démontrerais en particulier que l’état fondamental de tous les modèles que nous avons étudiés est multifractal avec en général une dimension fractale non triviale. / My thesis is devoted to the study of some aspects of many body quantum interacting systems. In particular we focus on quantum spin chains. I have studied several aspects of quantum spin chains, from both numerical and analytical perspectives. I addressed especially questions related to the structure of eigenfunctions, the level densities and the spectral properties of spin chain Hamiltonians. In this thesis, I first present the basic numerical techniques used for the computation of eigenvalues and eigenvectors of spin chain Hamiltonians. Level densities of quantum models are important and simple quantities that allow to characterize spectral properties of systems with large number of degrees of freedom. It is well known that the level densities of most integrable models tend to the Gaussian in the thermodynamic limit. However, it appears that in certain limits of coupling of the spin chain to the magnetic field and for finite number of spins on the chain, one observes peaks in the level density. I will show that the knowledge of the first two moments of the Hamiltonian in the degenerate subspace associated with each peak give a good approximation to the level density. Next, I study the statistical properties of the eigenvalues of spin chain Hamiltonians. One of the main achievements in the study of the spectral statistics of quantum complex systems concerns the universal behaviour of the fluctuation of measure such as the distribution of spacing between two consecutive eigenvalues. These fluctuations are very well described by the theory of random matrices but the comparison with the theoretical prediction generally requires a transformation of the spectrum of the Hamiltonian called the unfolding procedure. For many-body quantum systems, the size of the Hilbert space generally grows exponentially with the number of particles leading to a lack of data to make a proper statistical study. These constraints have led to the introduction of a new measure free of the unfolding procedure and based on the ratio of consecutive level spacings rather than the spacings themselves. This measure is independant of the local level density. By following the Wigner surmise for the computation of the level spacing distribution, I obtained approximation for the distribution of the ratio of consecutive level spacings by analyzing random 3x3 matrices for the three canonical ensembles. The prediction are compared with numerical results showing excellent agreement. Finally, I investigate eigenfunction statistics of some canonical spin-chain Hamiltonians. Eigenfunctions together with the energy spectrum are the fundamental objects of quantum systems: their structure is quite complicated and not well understood. Due to the exponential growth of the size of the Hilbert space, the study of eigenfunctions is a very difficult task from both analytical and numerical points of view. I demonstrate that the groundstate eigenfunctions of all canonical models of spin chain are multifractal, by computing numerically the Rényi entropy and extrapolating it to obtain the multifractal dimensions.

Die Arbeit befaßt sich mit Methoden der Identifikation von Schwingungsursachen anhand gemessener Schwingungssignale an Maschinen mit ungleichförmig übersetzenden Getrieben. Den technischen Hintergrund bilden die Verarbeitungsmaschinen, die als ein Haupteinsatzgebiet der auch als Mechanismen bezeichneten Getriebe gelten. Es werden eine ganze Reihe mechanismentypischer Schwingungsursachen untersucht, die im wesentlichen auf Elastizitäten, veränderliche Parameter, Spiel, Reibung und Unstetigkeiten in den Lagefunktionen zurückzuführen sind. Neben den traditionellen Methoden der Signalanalyse gilt das Hauptaugenmerk den neuen Methoden der Zeit-Frequenz-Analyse, die dem zumeist instationären Charakter der durch die Mechanismen verursachten Schwingungen gerecht werden. Dazu zählen vor allem lineare Transformationen wie die Kurzzeit-Fourier- oder die Wavelet-Transformation, quadratische Verteilungen aus Cohen's Klasse wie die Wigner-Ville und die Choi-Williams Verteilung oder auch höhere Transformationen wie die Adaptive Optimal Kernel oder die Reassignment Methode. Die Untersuchungen münden in einer tabellarischen Zusammenstellung der gefundenen Merkmale. Diese soll der Unterstützung der praktischen Signalanalyse bei Mechanismenschwingungen dienen. Die Anwendung der zur Verfügung stehenden Mittel und Methoden wird an zahlreichen Beispielen aus dem Bereich der Schwingungsdiagnose an Verarbeitungsmaschinen demonstriert. / This thesis deals with methods to identify causes of vibrations by investigation of messured signals at machines with mechanisms (with varying velocity ratio). The technical background is the field of manufacturing machines, the main application of such mechanisms. Inspected are the most typical causes of vibrations at mechanisms that comes with elasticity, variable parameters, clearance, friction or nonsmooth transferfunctions. There are used both, traditional methods of signal analysis and time-frequency-analysis methods that are especially advantageous for the often instationary vibrations at this kind of machines. Linear transformations like the Short-Time-Fourier- or the Wavelet-Transformation, quadratic distributions from Cohe's Class like the Wigner-Ville- or the Choi-Williams-Distribution and higher order transformations like the Adaptive Optimal Kernel or the Reassignment Method are used in this Paper. The investigation leads into a tabular form of characteristics wich should be used for identification of vibrational causes during vibrational analysis. The use of available methods is demonstrated by solving various examples on real manufactoring machines.

This thesis deals with time-frequency analysis of electrograms measured on isolated guinea pig hearts perfused according to Langendorff. Time-frequency analysis is based on algorithms Matching Pursuit and Wigner-Ville Distribution. The theoretical part describes the basics of electrocardiography, measurement on isolated hearts, the theory of approximation method Matching Pursuit and its combination with the Wigner-Ville distribution spectrum showing the energy density of the signal. Also other common approaches of time-frequency analysis are presented including the theory of continuous wavelet transform. The presented algorithms were tested on a set of electrograms, on which were induced ischemia within measurement followed by reperfusion. The proposed method allows for the fast detection of ischemia without any a priori knowledge of the signal, and also serves as a tool for measurement of EG important points and intervals. In the conclusion efficacy of the method was presented and its possible uses has been discussed.

Die Arbeit befaßt sich mit Methoden der Identifikation von Schwingungsursachen anhand gemessener Schwingungssignale an Maschinen mit ungleichförmig übersetzenden Getrieben. Den technischen Hintergrund bilden die Verarbeitungsmaschinen, die als ein Haupteinsatzgebiet der auch als Mechanismen bezeichneten Getriebe gelten. Es werden eine ganze Reihe mechanismentypischer Schwingungsursachen untersucht, die im wesentlichen auf Elastizitäten, veränderliche Parameter, Spiel, Reibung und Unstetigkeiten in den Lagefunktionen zurückzuführen sind. Neben den traditionellen Methoden der Signalanalyse gilt das Hauptaugenmerk den neuen Methoden der Zeit-Frequenz-Analyse, die dem zumeist instationären Charakter der durch die Mechanismen verursachten Schwingungen gerecht werden. Dazu zählen vor allem lineare Transformationen wie die Kurzzeit-Fourier- oder die Wavelet-Transformation, quadratische Verteilungen aus Cohen's Klasse wie die Wigner-Ville und die Choi-Williams Verteilung oder auch höhere Transformationen wie die Adaptive Optimal Kernel oder die Reassignment Methode. Die Untersuchungen münden in einer tabellarischen Zusammenstellung der gefundenen Merkmale. Diese soll der Unterstützung der praktischen Signalanalyse bei Mechanismenschwingungen dienen. Die Anwendung der zur Verfügung stehenden Mittel und Methoden wird an zahlreichen Beispielen aus dem Bereich der Schwingungsdiagnose an Verarbeitungsmaschinen demonstriert. / This thesis deals with methods to identify causes of vibrations by investigation of messured signals at machines with mechanisms (with varying velocity ratio). The technical background is the field of manufacturing machines, the main application of such mechanisms. Inspected are the most typical causes of vibrations at mechanisms that comes with elasticity, variable parameters, clearance, friction or nonsmooth transferfunctions. There are used both, traditional methods of signal analysis and time-frequency-analysis methods that are especially advantageous for the often instationary vibrations at this kind of machines. Linear transformations like the Short-Time-Fourier- or the Wavelet-Transformation, quadratic distributions from Cohe's Class like the Wigner-Ville- or the Choi-Williams-Distribution and higher order transformations like the Adaptive Optimal Kernel or the Reassignment Method are used in this Paper. The investigation leads into a tabular form of characteristics wich should be used for identification of vibrational causes during vibrational analysis. The use of available methods is demonstrated by solving various examples on real manufactoring machines.