Measurements, Models, Systems and Design

531 s.

Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation

The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.This work has been partly financed by the Spanish government through the CONSOLIDER-INGENIO 2010 program (COMONSENS project, ref. CSD2008-00010), as well as projects COSIMA (TEC2010-19545-C04-03), ALCIT (TEC2012 38800- C03-01), COMPREHENSION (TEC2012-38883-C02-01) and DISSECT (TEC2012-38058-C03-01)

Light curves and multidimensional reconstructions of photon observations

Diese Dissertation konzentriert sich auf die Entwicklung und Anwendung von bayesianischen Inferenzmethoden, um physikalisch relevante Informationen aus verrauschten Photonenbeobachtungen zu extrahieren. Des Weiteren wird eine Methode entwickelt, um Beobachtungen von komplexen Systemen, welche sich stochastisch mit der Zeit entwickeln, anhand weniger Trainingsbeispiele in verschiedene Klassen einzuordnen. Zu letztem Zweck entwickeln wir den Dynamic System Classifier (DSC). Dieser basiert auf der grundlegenden Annahme, dass viele komplexe Systeme in einem vereinfachten Rahmen durch stochastische Differentialgleichungen (SDE) mit zeitabhängigen Koeffizienten beschrieben werden können. Diese werden verwendet, um Informationen aus einer Klasse ähnlicher, aber nicht identischer simulierter Systeme zu abstrahieren. Der DSC ist in zwei Phasen unterteilt. In der ersten Lernphase werden die Koeffizienten der SDE aus einem kleinen Trainingsdatensatz gelernt. Sobald diese gelernt wurden, dienen sie für einen kostengünstigen Vergleich von Daten und abstrahierter Information. Wir entwickeln, implementieren und testen beide Schritte in dem Rahmen bayesianischer Logik für kontinuierliche Größen, nämlich der Informationsfeldtheorie. Der zweite Teil dieser Arbeit beschäftigt sich mit astronomischer Bildgebung basierend auf Zählraten von Photonen. Die Notwendigkeit hierfür ergibt sich unter anderem aus der Verfügbarkeit von zahlreichen Satelliten, welche die Röntgen- und γ−Strahlen im Weltraum beobachten. In diesem Zusammenhang entwickeln wir den existierenden D3PO-Algorithmus weiter, hin zu D4PO, um multidimensionale Photonenbeobachtungen zu entrauschen, zu dekonvolvieren und in morphologisch unterschiedliche Komponenten aufzuteilen. Die Zerlegung wird durch ein hierarchisches bayesianisches Parametermodell gesteuert. Dieses erlaubt es, Felder zu rekonstruieren, die über den Produktraum von mehreren Mannigfaltigkeiten definiert sind. D4PO zerlegt den beobachteten Fluss an Photonen in eine diffuse, eine punktförmige und eine Hintergrundkomponente, während er gleichzeitig die Korrelationsstruktur für jede einzelne Komponente in jeder ihrer Mannigfaltigkeiten lernt. Die Funktionsweise von D4PO wird anhand eines simulierten Datensatzes hochenergetischer Photonen demonstriert. Schließlich wenden wir D4PO auf Daten der Magnetar-Flares von SGR 1806-20 und SGR 1900+14 an, um nach deren charakteristischen Eigenschwingungen zu suchen. Der Algorithmus rekonstruierte erfolgreich den logarithmischen Photonenfluss sowie dessen spektrale Leistungsdichte. Im Gegensatz zu früheren Arbeiten anderer Autoren können wir quasi- periodische Oszillationen (QPO) in den abklingenden Enden dieser Ereignisse bei Frequenzen ν > 17 Hz nicht bestätigen. Deren Echtheit ist fraglich, da diese in das von Rauschen dominierende Regime fallen. Dennoch finden wir neue Kandidaten für Oszillationen bei ν ≈ 9.2 Hz (SGR 1806-20) und ν ≈ 7.7 Hz (SGR 1900+14). Für den Fall, dass diese Oszillationen real sind, bevorzugen moderne theoretische Modelle von Magnetaren relativ schwache Magnetfelder im Bereich von B ≈ 6 × 1013 − 3 × 1014 G.This thesis focuses on the development and application of Bayesian inference methods to extract physical relevant information from noise contaminated photon observations and to classify the observations of complex stochastically evolving systems into different classes based on a few training samples of each class. To this latter end we develop the dynamic system classifier (DSC). This is based on the fundamental assumption that many complex systems may be described in a simplified framework by stochastic differential equations (SDE) with time dependent coefficients. These are used to abstract information from a class of similar but not identical simulated systems. The DSC is split into two phases. In the first learning phase the coefficients of the SDE are learned from a small training data set. Once these are obtained, they serve for an inexpensive data - class comparison. We develop, implement, and test both steps in a Bayesian inference framework for continuous quantities, namely information field theory. Astronomical imaging based on photon count data is a challenging task but absolutely necessary due to todays availability of space based X-ray and γ- ray telescopes. In this context we advance the existing D3PO algorithm into D4PO to denoise, denconvolve, and decompose multidimensional photon observations into morphologically different components. The decomposition is driven by a probabilistic hierarchical Bayesian parameter model, allowing us to reconstruct fields, that are defined over the product space of multiple manifolds. Thereby D4PO decomposes the photon count data into a diffuse, point-like, and background component, while it simultaneously learns the correlation structure over each of their manifolds individually. The capabilities of the algorithm are demonstrated by applying it to a simulated high energy photon count data set. Finally we apply D4PO to analyse the giant magnetar flare data of SGR 1806-20 and SGR 1900+14. The algorithm successfully reconstructs the logarithmic photon flux as well as its power spectrum. In contrast to previous findings we cannot confirm quasi periodic oscillations (QPO) in the decaying tails of these events at frequencies ν > 17 Hz. They might not be real as these fall into the noise dominated regime of the spectrum. Nevertheless we find new candidates for oscillations at ν ≈ 9.2 Hz (SGR 1806-20) and ν ≈ 7.7 Hz (SGR 1900+14). In case these oscillations are real, state of the art theoretical models of magnetars favour relatively weak magnetic fields in the range of B ≈ 6×1013−3×1014 G

Novel Pitch Detection Algorithm With Application to Speech Coding

This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions

Maximum Likelihood Pitch Estimation Using Sinusoidal Modeling

The aim of the work presented in this thesis is to automatically extract the fundamental frequency of a periodic signal from noisy observations, a task commonly referred to as pitch estimation. An algorithm for optimal pitch estimation using a maximum likelihood formulation is presented. The speech waveform is modeled using sinusoidal basis functions that are harmonically tied together to explicitly capture the periodic structure of voiced speech. The problem of pitch estimation is casted as a model selection problem and the Akaike Information Criterion is used to estimate the pitch. The algorithm is compared with several existing pitch detection algorithms (PDAs) on a reference pitch database. The results indicate the superior performance of the algorithm in comparison with most of the PDAs. The application of parametric modeling in single channel speech segregation and the use of mel-frequency cepstral coefficients for sequential grouping are analyzed in the speech separation challenge database

Analog dithering techniques for highly linear and efficient transmitters

The current thesis is about investigation of new methods and techniques to be able to utilize the switched mode amplifiers, for linear and efficient applications. Switched mode amplifiers benefit from low overlap between the current and voltage wave forms in their output terminals, but they seriously suffer from nonlinearity. This makes it impossible to use them to amplify non-constant envelope message signals, where very high linearity is expected. In order to do that, dithering techniques are studied and a full linearity analysis approach is developed, by which the linearity performance of the dithered amplifier can be analyzed, based on the dithering level and frequency. The approach was based on orthogonalization of the equivalent nonlinearity and is capable of prediction of both co-channel and adjacent channel nonlinearity metrics, for a Gaussian complex or real input random signal. Behavioral switched mode amplifier models are studied and new models are developed, which can be utilized to predict the nonlinear performance of the dithered power amplifier, including the nonlinear capacitors effects. For HFD application, self-oscillating and asynchronous sigma delta techniques are currently used, as pulse with modulators (PWM), to encode a generic RF message signal, on the duty cycle of an output pulse train. The proposed models and analysis techniques were applied to this architecture in the first phase, and the method was validated with measurement on a prototype sample, realized in 65 nm TSMC CMOS technology. Afterwards, based on the same dithering phenomenon, a new linearization technique was proposed, which linearizes the switched mode class D amplifier, and at the same time can reduce the reactive power loss of the amplifier. This method is based on the dithering of the switched mode amplifier with frequencies lower than the band-pass message signal and is called low frequency dithering (LFD). To test this new technique, two test circuits were realized and the idea was applied to them. Both of the circuits were of the hard nonlinear type (class D) and are integrated CMOS and discrete LDMOS technologies respectively. The idea was successfully tested on both test circuits and all of the linearity metric predictions for a digitally modulated RF signal and a random signal were compared to the measurements. Moreover a search method to find the optimum dither frequency was proposed and validated. Finally, inspired by averaging interpretation of the dithering phenomenon, three new topologies were proposed, which are namely DLM, RF-ADC and area modulation power combining, which are all nonlinear systems linearized with dithering techniques. A new averaging method was developed and used for analysis of a Gilbert cell mixer topology, which resulted in a closed form relationship for the conversion gain, for long channel devices

A shared frequency set between the historical mid-latitude aurora records and the global surface temperature

Herein we show that the historical records of mid-latitude auroras from 1700 to 1966 present oscillations with periods of about 9, 10-11, 20-21, 30 and 60 years. The same frequencies are found in proxy and instrumental global surface temperature records since 1650 and 1850, respectively and in several planetary and solar records. Thus, the aurora records reveal a physical link between climate change and astronomical oscillations. Likely, there exists a modulation of the cosmic ray flux reaching the Earth and/or of the electric properties of the ionosphere. The latter, in turn, have the potentiality of modulating the global cloud cover that ultimately drives the climate oscillations through albedo oscillations. In particular, a quasi 60-year large cycle is quite evident since 1650 in all climate and astronomical records herein studied, which also include an historical record of meteorite fall in China from 619 to 1943. These findings support the thesis that climate oscillations have an astronomical origin. We show that a harmonic constituent model based on the major astronomical frequencies revealed in the aurora records is able to forecast with a reasonable accuracy the decadal and multidecadal temperature oscillations from 1950 to 2010 using the temperature data before 1950, and vice versa. The existence of a natural 60-year modulation of the global surface temperature induced by astronomical mechanisms, by alone, would imply that at least 60-70% of the warming observed since 1970 has been naturally induced. Moreover, the climate may stay approximately stable during the next decades because the 60-year cycle has entered in its cooling phase.Comment: 18 pages, 11 figure

Waveguide-Based Photonic Sensors: From Devices to Robust Systems

Integrated photonic sensor systems are miniaturized, mass-producible devices that leverage the mature semiconductor fabrication technology and a well-established ecosystem for photonic circuits. This book aims at a holistic treatment of waveguide-based photonic sensor systems by analyzing photonic waveguide design, photonic circuit design and readout design. Across all levels, a special emphasis is given to system-level performance optimization under realistic environmental conditions