No description available.

This research presents a pulse-by-pulse analysis of Oscillometric blood pressure waveform at systolic, diastolic and mean arterial pressure points. Using a mathematical optimization technique, pulses are characterized into component harmonic by minimizing the least square error. The results at the important pressure points are analyzed and compared for different subject using different waveform extraction techniques. Blood pressure is estimated using the harmonic parameters. The approach studies changes in the parameters as oscillometric blood pressure recording is done. 8 harmonic parameters are obtained from the pulse characterization and are used to estimate Systolic arterial Blood Pressure, Mean arterial Blood Pressure, and Diastolic arterial Blood Pressure. The estimates are compared with our reference value to determine which has the best agreement. The proposed method is further compared with Maximum Amplitude Algorithm and Pulse Morphology Algorithm. The effect of oscillometric waveform extraction methods on the proposed method is observed. The experiment established the fact that the extraction technique can alter the shape of oscillometric pulses. The methods were compared and it was observed that the used extraction methods did not make any significant difference on the accuracy, using this technique.

Faire le lien entre la morphologie d'un signal sonore et certains de ses attributs perceptifs est une étape capitale dans l'élaboration d'un synthétiseur proposant un contrôle intuitif. Certains aspects de cette morphologie peuvent être caractérisés au moyen de "descripteurs acoustiques". Lorsqu'ils sont choisis judicieusement, ces descripteurs permettent de classer des signaux dans des catégories ayant un sens perceptif ; ceci permet d'établir un lien entre morphologie et perception. Dans le travail présenté ici, on s'intéresse en particulier à la catégorisation perceptive de sons d'impact.La plupart des descripteurs considérés ici se construisent à partir d'une modélisation paramétrique du signal. Dans notre cas, la modélisation la plus appropriée semble être la décomposition en somme de sinusoïdes amorties. Une estimation stable et rigoureuse des paramètres du modèle étant essentielle au calcul des descripteurs, on se penche sur la comparaison de plusieurs méthodes de décomposition. Il ressort que la méthode à haute résolution ESPRIT semble la plus indiquée, mais qu'elle ne peut pas être utilisée sous sa forme classique. On propose donc différentes adaptations. En particulier, on s'intéresse à l'application d'ESPRIT dans des repères de Gabor. En outre, on propose des méthodes pour maximiser le caractère parcimonieux de la décomposition.On étudie finalement un cas d'application concret : à partir d'une banque de sons enregistrés en chambre anéchoïque résultant d'impacts sur divers objets du quotidien, on évalue la pertinence d'un ensemble de descripteurs pour la catégorisation en fonction du matériau perçu. / Linking an audio signal morphology with some of its perceptual attributes is a key step when elaborating a intuitively controlled synthesizer. Some of these morphology aspects can be characterized using "acoustical descriptors". When chosen wisely, descriptors can allow a classification of audio signals in categories which are perceptually relevant ; in such cases, this approach establishes a link between morphology and perception. The present work focuses on the perceptual categorization of impact sounds.Most of the descriptors proposed here are computed using a parametrized description of the signal. Here, the most appropriate parametrization seems to be a decomposition in exponentially damped sinusoids. A robust and stable estimation of the model parameters being essential to the computation of relevant descriptors, different parametrization methods are described and compared. From these comparisons, it appears that the high-resolution method ESPRIT is the most appropriate, but that it cannot be applied in its classical form. Several adaptations are therefore investigated. In particular, the application of ESPRIT in Gabor frames is considered. Besides, a method is proposed in order to minimize the number of components necessary for a satisfactory decomposition.Finally, a concrete application is addressed : from an impact sounds bank recorded in an anechoic chamber, elaborated with a wide range of everyday-life objects, the relevance of several acoustical descriptors for the perceptual categorization of the perceived material is investigated.

Several emerging wireless communication systems require direct transmission between mobile terminals to support efficient data transfer and user mobility. Such mobile-to-mobile communication systems differ from the conventional cellular systems where only the user unit is mobile. In addition, there might be a relay, also called a repeater, between the original transmitter and the final receiver to improve the network range and coverage. Potential applications for mobile-to-mobile systems include Intelligent Highways for coordinated traffic control and ad-hoc networks meant for military and disaster management. Relays may be deployed in cellular networks and IEEE 802.16 mesh networks for wireless broadband access. Extensive research in cellular radio channels has led to the successful deployment of cellular networks. However, our knowledge of the radio channels encountered in mobile-to-mobile and relay-based systems is still inadequate. This forms the primary motivation behind our research in addressing wireless channel modeling, simulation, and estimation issues for these systems. Specifically, we investigate frequency-flat mobile-to-mobile channels and develop simulation models by using the sum-of-sinusoids method, which is widely used for cellular channels. In addition, we present the properties of amplify and forward relay channels via theoretical analysis. This analysis, to the best of our knowledge, is the first of its kind. Further, we address the unique challenges, which arise because of the different underlying channel model, for channel estimation in amplify and forward relay systems. Our work would provide other researchers the necessary tools for the design and testing of these emerging communication systems.

Spectroscopic techniques allow for studies of materials and organisms on the atomic and molecular level. Examples of such techniques are nuclear magnetic resonance (NMR) spectroscopy—one of the principal techniques to obtain physical, chemical, electronic and structural information about molecules—and magnetic resonance imaging (MRI)—an important medical imaging technique for, e.g., visualization of the internal structure of the human body. The less well-known spectroscopic technique of nuclear quadrupole resonance (NQR) is related to NMR and MRI but with the difference that no external magnetic field is needed. NQR has found applications in, e.g., detection of explosives and narcotics. The first part of this thesis is focused on detection and identification of solid and liquid explosives using both NQR and NMR data. Methods allowing for uncertainties in the assumed signal amplitudes are proposed, as well as methods for estimation of model parameters that allow for non-uniform sampling of the data. The second part treats two medical applications. Firstly, new, fast methods for parameter estimation in MRI data are presented. MRI can be used for, e.g., the diagnosis of anomalies in the skin or in the brain. The presented methods allow for a significant decrease in computational complexity without loss in performance. Secondly, the estimation of blood flow velo-city using medical ultrasound scanners is addressed. Information about anomalies in the blood flow dynamics is an important tool for the diagnosis of, for example, stenosis and atherosclerosis. The presented methods make no assumption on the sampling schemes, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions.

De nombreux traitements appliqués aux signaux audio travaillent sur une représentation Temps-Fréquence (TF) des données. Lorsque le résultat de ces algorithmes est un champ spectral d’amplitude, la question se pose, pour reconstituer un signal temporel, d’estimer le champ de phase correspondant. C’est par exemple le cas dans les applications de séparation de sources, qui estiment les spectrogrammes des sources individuelles à partir du mélange ; la méthode dite de filtrage de Wiener, largement utilisée en pratique, fournit des résultats satisfaisants mais est mise en défaut lorsque les sources se recouvrent dans le plan TF. Cette thèse aborde le problème de la reconstruction de phase de signaux dans le domaine TF appliquée à la séparation de sources audio. Une étude préliminaire révèle la nécessité de mettre au point de nouvelles techniques de reconstruction de phase pour améliorer la qualité de la séparation de sources. Nous proposons de baser celles-ci sur des modèles de signaux. Notre approche consiste à exploiter des informations issues de modèles sous-jacents aux données comme les mélanges de sinusoïdes. La prise en compte de ces informations permet de préserver certaines propriétés intéressantes, comme la continuité temporelle ou la précision des attaques. Nous intégrons ces contraintes dans des modèles de mélanges pour la séparation de sources, où la phase du mélange est exploitée. Les amplitudes des sources pourront être supposées connues, ou bien estimées conjointement dans un modèle inspiré de la factorisation en matrices non-négatives complexe. Enfin, un modèle probabiliste de sources à phase non-uniforme est mis au point. Il permet d’exploiter les à priori provenant de la modélisation de signaux et de tenir compte d’une incertitude sur ceux-ci. Ces méthodes sont testées sur de nombreuses bases de données de signaux de musique réalistes. Leurs performances, en termes de qualité des signaux estimés et de temps de calcul, sont supérieures à celles des méthodes traditionnelles. En particulier, nous observons une diminution des interférences entre sources estimées, et une réduction des artéfacts dans les basses fréquences, ce qui confirme l’intérêt des modèles de signaux pour la reconstruction de phase. / A variety of audio signal processing techniques act on a Time-Frequency (TF) representation of the data. When the result of those algorithms is a magnitude spectrum, it is necessary to reconstruct the corresponding phase field in order to resynthesize time-domain signals. For instance, in the source separation framework the spectrograms of the individual sources are estimated from the mixture ; the widely used Wiener filtering technique then provides satisfactory results, but its performance decreases when the sources overlap in the TF domain. This thesis addresses the problem of phase reconstruction in the TF domain for audio source separation. From a preliminary study we highlight the need for novel phase recovery methods. We therefore introduce new phase reconstruction techniques that are based on music signal modeling : our approach consists inexploiting phase information that originates from signal models such as mixtures of sinusoids. Taking those constraints into account enables us to preserve desirable properties such as temporal continuity or transient precision. We integrate these into several mixture models where the mixture phase is exploited ; the magnitudes of the sources are either assumed to be known, or jointly estimated in a complex nonnegative matrix factorization framework. Finally we design a phase-dependent probabilistic mixture model that accounts for model-based phase priors. Those methods are tested on a variety of realistic music signals. They compare favorably or outperform traditional source separation techniques in terms of signal reconstruction quality and computational cost. In particular, we observe a decrease in interferences between the estimated sources and a reduction of artifacts in the low-frequency components, which confirms the benefit of signal model-based phase reconstruction methods.

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates. For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples is low, and that it is also efficient in the sense that the estimates have Cram\'er-Rao lower Bound (CRB) equivalent variance. In the direction of arrival (DOA) scenario, there are different types of prior information; first, we study the case when the location of some of the emitters in the scene is known. We then turn to cases with additional prior information, i.e.~when it is known that some (or all) of the source signals are uncorrelated. As it turns out, knowledge of some DOA combined with this latter form of prior knowledge is especially beneficial, giving estimators that are dramatically more accurate than the state-of-art. We also derive the corresponding CRBs, and show that under quite mild assumptions, the estimators are efficient. Finally, we also investigate the frequency estimation scenario, where the data is a one-dimensional temporal sequence which we model as a spatial multi-sensor response. The line-frequency estimation problem is studied when some of the frequencies are known; through experimental data we show that our approach can be beneficial. The second frequency estimation paper explores the analysis of pulse spin-locking data sequences, which are encountered in nuclear resonance experiments. By introducing a novel modeling technique for such data, we develop a method for estimating the interesting parameters of the model. The technique is significantly faster than previously available methods, and provides accurate estimation results. / Denna doktorsavhandling behandlar parameterestimeringsproblem inom flerkanals-signalbehandling. Den gemensamma förutsättningen för dessa problem är att det finns information om de sökta parametrarna redan innan data analyseras; tanken är att på ett så finurligt sätt som möjligt använda denna kunskap för att förbättra skattningarna av de okända parametrarna. I en uppsats studeras kovariansmatrisskattning när det är känt att den sanna kovariansmatrisen har Kronecker- och Toeplitz-struktur. Baserat på denna kunskap utvecklar vi en metod som säkerställer att även skattningarna har denna struktur, och vi kan visa att den föreslagna skattaren har bättre prestanda än existerande metoder. Vi kan också visa att skattarens varians når Cram\'er-Rao-gränsen (CRB). Vi studerar vidare olika sorters förhandskunskap i riktningsbestämningsscenariot: först i det fall då riktningarna till ett antal av sändarna är kända. Sedan undersöker vi fallet då vi även vet något om kovariansen mellan de mottagna signalerna, nämligen att vissa (eller alla) signaler är okorrelerade. Det visar sig att just kombinationen av förkunskap om både korrelation och riktning är speciellt betydelsefull, och genom att utnyttja denna kunskap på rätt sätt kan vi skapa skattare som är mycket noggrannare än tidigare möjligt. Vi härleder även CRB för fall med denna förhandskunskap, och vi kan visa att de föreslagna skattarna är effektiva. Slutligen behandlar vi även frekvensskattning. I detta problem är data en en-dimensionell temporal sekvens som vi modellerar som en spatiell fler-kanalssignal. Fördelen med denna modelleringsstrategi är att vi kan använda liknande metoder i estimatorerna som vid sensor-signalbehandlingsproblemen. Vi utnyttjar återigen förhandskunskap om källsignalerna: i ett av bidragen är antagandet att vissa frekvenser är kända, och vi modifierar en existerande metod för att ta hänsyn till denna kunskap. Genom att tillämpa den föreslagna metoden på experimentell data visar vi metodens användbarhet. Det andra bidraget inom detta område studerar data som erhålls från exempelvis experiment inom kärnmagnetisk resonans. Vi introducerar en ny modelleringsmetod för sådan data och utvecklar en algoritm för att skatta de önskade parametrarna i denna modell. Vår algoritm är betydligt snabbare än existerande metoder, och skattningarna är tillräckligt noggranna för typiska tillämpningar. / <p>QC 20131115</p>