A high resolution speedy delivery time domain reflectometer (SD/TDR) has been developed in the Electrical Engineering department at The University of Texas at Austin. The SD/TDR uses a novel non-sinusoidal signal that does not undergo dispersion during transmission in a lossy media. SD/TDR is used to estimate the length and detect the location of faults in the transmission lines. Time of flight (TOF) is one of the critical parameters of SD/TDR and a function of several temperature dependent factors. Given the TOF and length of a transmission line, signal delay can be computed. This research presents an empirical study of the effect of temperature on the TOF in a plenum grade coaxial cable for temperatures ranging from -3 °C to 60 °C. We also study the effect of temperature on characteristic impedance of the coaxial cable. Finally, a SD double exponential waveform is used to estimate TOF for calibrated short and open terminations. / text

The Speedy Delivery (SD) waveform was introduced in patent US 6,441,695 B1 issued August 27, 2002 to the inventor Dr. Robert Flake. In the most basic form, the SD boundary condition is an exponential, D⋅e [superscript α⋅t] . The propagating waveform is described by an analytic, closed form solution of the wave equation in lossy media and has several very special properties. The most surprising property is that the leading edge of the waveform propagates with attenuation but without distortion. The lack of distortion occurs even in lossy transmission media with frequency dependent parameters. This is unlike any other known pulse shape. Additionally, varying the waveforms parameter, α, can vary the propagation velocity and the attenuation of the waveform. Because the exponential waveform is unbounded it cannot continue indefinitely and must be truncated and closed by a non-SD closing edge. This dissertation discusses the transmission behavior and two applications of truncated SD waveforms. A brief analysis of SD propagation in lossy transmission lines is presented and some practical considerations associated with truncating the SD waveforms are addressed. The parameters needed to describe the propagation of the SD waveform are defined and techniques for determining their values are presented. Finally, examples applying these truncated SD waveforms to time domain reflectometry and Communication Technology are presented. / text

Three information measures are discussed and used as objective functions for optimization of wireless sensor networks (WSNs) and radar systems. In addition, a long-term system performance measure is developed for evaluating the performance of slow-fading WSNs. Three system applications are considered: a distributed detection system, a distributed multiple hypothesis system, and a radar target recognition system.First, we consider sensor power optimization for distributed binary detection systems. The system communicates over slow-fading orthogonal multiple access channels. In earlier work, it was demonstrated that system performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system. We define outage probability for slow-fading system as a long-term performance measure, and analytically develop the detection outage with the given system model.Based on the analytical result of the outage probability, diversity gain is derived and shown to be proportional to the number of the sensor nodes. Then, we extend the optimized power control strategy to a distributed multiple hypothesis system, and enhance the power optimization by exploiting a priori probabilities and local sensor statistics. We also extend outage probability to the distributed multiple-hypotheses problem. The third application is radar waveform design with a new performance measure: Task-Specific Information (TSI). TSI is an information-theoretic measure formulated for one or more specific sensor tasks by encoding the task(s) directly into the signal model via source variables. For example, we consider the problem of correctly classifying a linear system from a set of known alternatives, and the source variable takes the form of an indicator vector that selects the transfer function of the true hypothesis. We then compare the performance of TSI with conventional waveforms and other information-theoretic waveform designs via simulation. We apply radar-specific constraints and signal models to the waveform optimization.

The existence of Gravitational Waves from binary black holes is one of the most interesting predictions of General Relativity. These ripples in space-time should be visible to ground-based gravitational wave detectors worldwide in the next few years. One such detector, the Laser Interferometer Gravitational-wave Observatory (LIGO) is in the process of being upgraded to its Advanced sensitivity which should make gravitational wave detections routine. Even so, the signals that LIGO will detect will be faint compared to the detector noise, and so accurate waveform templates are crucial. In this thesis, we present a detailed analysis of the accuracy of hybrid gravitational waveforms. Hybrids are created by stitching a long post-Newtonian inspiral to the late inspiral, merger, and ringdown produced by numerical relativity simulations. We begin our investigation with a study of the systematic errors in the numerical waveform, and errors due to hybridization and choice of detector noise. For current NR waveforms, the largest source of error comes from the unknown high-order terms in the post-Newtonian waveform, which we first explore for equal-mass, non-spinning binaries, and also for unequal-mass, non-spinning binaries. We then consider the potential reduction in hybrid errors if these higher-order terms were known. Finally, we investigate the possibility of using hybrid waveforms as a detection template bank and integrating NR+PN hybrids into the LIGO detection pipeline.

The existence of Gravitational Waves from binary black holes is one of the most interesting predictions of General Relativity. These ripples in space-time should be visible to ground-based gravitational wave detectors worldwide in the next few years. One such detector, the Laser Interferometer Gravitational-wave Observatory (LIGO) is in the process of being upgraded to its Advanced sensitivity which should make gravitational wave detections routine. Even so, the signals that LIGO will detect will be faint compared to the detector noise, and so accurate waveform templates are crucial. In this thesis, we present a detailed analysis of the accuracy of hybrid gravitational waveforms. Hybrids are created by stitching a long post-Newtonian inspiral to the late inspiral, merger, and ringdown produced by numerical relativity simulations. We begin our investigation with a study of the systematic errors in the numerical waveform, and errors due to hybridization and choice of detector noise. For current NR waveforms, the largest source of error comes from the unknown high-order terms in the post-Newtonian waveform, which we first explore for equal-mass, non-spinning binaries, and also for unequal-mass, non-spinning binaries. We then consider the potential reduction in hybrid errors if these higher-order terms were known. Finally, we investigate the possibility of using hybrid waveforms as a detection template bank and integrating NR+PN hybrids into the LIGO detection pipeline.

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Vocal fold vibration results from an alternating balance between subglottal air pressure that drives the vocal folds apart and muscular, elastic, and restoring forces that draw them together. The aim of the present thesis is to present quantitative data of normal vocal function using a noninvasive method. Measurements are made on the inverse filtered airflow waveform, of estimated average trans glottal pressure and glottal airflow, and of sound pressure for productions of syllable sequences. Statistical results are used to infer mechanisms that underlie differences across ( 1 ) normal, loud, and soft voice, (2) normal, high, and low pitch, and (3) between female and male voices. Interspeaker variation in group data and intra speaker variation across repeated recordings is also investigated. The results showed no significant female-male differences in pressure, suggesting that differences in other measures were not primarily due to differences in the respiratory systems . Most glottal waveforms showed a DC flow offset, suggesting an air leakage through a posterior glottal opening. Results suggested (indirectly) that the males in comparison with the females had significantly higher vocal fold closing velocities (maximum flow declination rate), larger vocal fold oscillations (AC flow), and relatively longer closed portions of the cycle (open quotient) in normal and loud voice. In soft voice, female and male waveforms were more alike. In comparison with normal voice, both females and males produced loud voice with significantly higher values of pressure, vocal fold closing velocity, and AC flow. Soft voice was produced with significantly lower values of these measures and increased DC flow. Correlation analyses indicated that several of the airflow measures were more directly related to vocal intensity than to pitch. Interspeaker variation was large, emphasizing the importance of large subject groups to capture normal variation. Intraspeaker variation across recording sessions was less than 2 standard deviations of the group means. The results should contribute to the understanding of normal voice function, and should be useful as norms in studies of voices disorders as well. / Härtill 5 uppsatser.För att köpa boken skicka en beställning till exp@ling.su.se/ To order the book send an e-mail to exp@ling.su.se

L'inversion de forme d'onde complète (FWI) est un processus non-linéaire et mal posé d’ajustement de données, dans notre cas, issues d’acquisitions simiques. Cette technique cherche à reconstruire, à partir d’un modèle initial obtenu à faible nombre d’onde (faible résolution), des paramètres constitutifs contrôlant la propagation des ondes à grands nombres d’ondes (forte résolution). Durant ce processus itératif, certains artéfacts peuvent altérer la qualité du modèle reconstruit. Afin de diminuer ces artéfacts et d’assurer une reconstruction des paramètres qui soit cohérente d’un point de vue géologique, différentes techniques de pré-conditionnement ou de régularisation peuvent être proposées.Cette thèse se focalise sur le potentiel de nouveaux filtres multi-dimensionnels construits dans l’espace des nombres d’ondes et orientés suivant les structures géologiques. Une stratégie de pré-conditionnement a été mise au point à l’aide de ces filtres et a été appliquée avec succès à la problématique FWI. La formulation analytique 1D de l’opérateur inverse de covariance laplacienne (Tarantola, 2005) constitue la base de la formulation d’opérateurs de dimension supérieure qui sont validés ici en les comparants avec l’opérateur analytique de covariance laplacienne 1D. Nous avons utilisé cette fonction analytique inverse 1D comme la base de filtrage de dimension supérieure, via l’addition de multiples fonctions inverses orientées orthogonalement. Ces fonctions laplaciennes inverses additionnelles (AIL) sont obtenues pour des configurations 2D et 3D après discrétisation par des techniques de différences finies. Nous montrons que l’on peut calculer un filtre en nombre d’onde de manière rapide et robuste en résolvant le système linéaire associé à ces opérateurs inverses. Lorsque des pentes sont inclues à l’étape de discrétisation par différences finies, il est alors possible d’utiliser ces opérateurs comme des filtres en nombre d’ondes orientés vers les structures géologiques, ceci avec une grande efficacité.Ce filtre (AIL) montre des propriétés rapides de convergence et des performances indépendantes du vecteur à filtrer. Nous montrons notamment comment ce filtre peut être utilisé comme un opérateur utile pour le gradient associé à la FWI. Le pré-conditionnement du gradient peut atténuer les effets du problème mal-posé qui vont s’étendre dans l’espace des modèles. Deux exemples synthétiques (Valhall et Marmousi) calculés dans l’espace des fréquences sont proposés dans cette thèse. Le pré-conditionnement AIL s’avère efficace pour atténuer d’une part la signature mal-posée provenant de la présence de bruit ambient dans les données observées et d’autre part d’artéfacts liés aux effets de repliement spatial liés aux conditions d’imagerie par FWI. La possibilité d’inclure des pentes permet de filtrer de manière préférentielle en considérant des pendages géologiques. Cette stratégie de filtrage permet l’atténuation d’artéfacts, tout en préservant le contenu en nombre d’ondes de la stratigraphie orthogonale au pendage.Un cas réel d’inversion 2D FWI est finalement abordé permettant tout d’abord d’illustrer la sensibilité des résultats d’inversion au modèle initial. Celui-ci est d’importance majeure, particulièrement dans les régions profondes dépassant la pénétration maximale des ondes transmises. L’application de la technique FWI à cette acquisition sismique a permis d’améliorer de manière significative la cohérence sur une image migrée par renversement du temps (RTM). Nous montrons également que le pré-conditionneur AIL permet une décroissance significative du nombre de tirs requis à modéliser dans la boucle d’inversion, sans pour autant dégrader le contenu en nombre d’onde des structures géologiques principales dans les résultats finaux obtenus après inversion. / Full waveform inversion (FWI) is a non-linear, ill-posed, local data fitting technique. FWI looks to moves from an initial, low-wavenumber representation of the earth parameters to a broadband representation. During this iterative process a number of undesirable artifacts can map into our model parameter reconstruction. To mitigate these artifacts and to ensure a geologically consistent model parameter reconstruction, various preconditioning and/or regularization strategies have been proposed.This thesis details the construction of new, efficient, multi-dimensional, structurally-orientated wavenumber filters. A preconditioning strategy has been devised using these filters that we have successfully applied to FWI. The 1D analytical inverse Laplacian covariance operator (Tarantola, 2005) forms the basis of higher dimensional operators and is initially validated by comparing to the 1D analytical Laplacian covariance operator. We use this analytical 1D inverse function as the basis for higher dimensional filtering via the addition of multiple, orthogonally orientated inverse functions. These additive inverse laplacian functions (AIL) are shown in 2D and 3D configurations and are discretized using finite-difference techniques. We show that one can calculate, a rapid and robust wavenumber filter, by solving the linear system associated with these inverse operators. When dip is included at the finite difference discretization stage, it is possible to use these operators as highly efficient, structurally orientated wavenumber filters.The AIL filter is shown to be rapid to converge and its performance is independent of the vector to be filtered. We show, that the filter can be a useful preconditioning operator for the FWI gradient. Preconditioning the gradient can mitigate against ill-posed effects mapping into the model-space. Two synthetic (Valhall and Marmousi) frequency domain FWI example are shown in this thesis. The AIL preconditioner has success at mitigating the ill-posed imprint coming from ambient noise in the observed data and also artifacts from spatial aliasing effects in the FWI imaging condition. The ability to include dip, allows one to preferentially filter along geological dip. This filtering strategy allows the mitigation of artifacts, while simultaneously preserving the stratigraphic based wavenumber content that is orthogonal to dip.A 2D, real data FWI case-study is also shown and we highlight the sensitivity of the inversion result to the initial model. The initial model is of key importance, this especially true in the areas deeper than the maximum penetration of transmitted waves. The application of FWI on this line is able to significantly improve gather alignment on a RTM, migrated image. We also see that the AIL preconditioner can allows us to significantly decrease the number of shot records we are required to model in our inversion workflow without degrading the key geological wavenumber content in the final inversion result.

Title: The source process of Greek earthquakes Author: Dana K ížová Department: Department of Geophysics Supervisor of the doctoral thesis: Prof. RNDr. Ji í Zahradník DrSc., Department of Geophysics Abstract: Investigations of moment tensor (MT) and its uncertainty are topical. This thesis is focused on isotropic component of three shallow earthquakes: Event A in Cretan Sea (Mw 5.3) and two events near Santorini island, B (Mw 4.9) and C (Mw 4.7). MT is inverted from full waveforms in an assumed 1D velocity model. The inverse problem is non-linear in centroid depth and time, and linear in six MT parameters, one is the MT-trace. Uncertainty of isotropic component is studied by a new approach (K ížová et al., 2013). The trace is systematically varied, and remaining parameters are optimized. The method reveals tradeoffs between the isotropic component, depth, time, and focal mechanism. From two existing velocity models, we prefer the one with lower condition number, in which a (positive) isotropic component is indicated for event B. To rapidly assess a likely existence of isotropic component, an empirical method is proposed (K ížová et al., 2016). It is based on comparison between depth- dependences of waveform correlation in full and deviatoric modes. Based on extensive synthetic tests, the method confirms a...

abstract: In this thesis, an integrated waveform-agile multi-modal tracking-beforedetect sensing system is investigated and the performance is evaluated using an experimental platform. The sensing system of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors allows for integration of complementary information from different sensors. However, there are many challenges to overcome, including tracking low signal-to-noise ratio (SNR) targets, waveform configurations that can optimize tracking performance and statistically dependent measurements. Address some of these challenges, a particle filter (PF) based recursive waveformagile track-before-detect (TBD) algorithm is developed to avoid information loss caused by conventional detection under low SNR environments. Furthermore, a waveform-agile selection technique is integrated into the PF-TBD to allow for adaptive waveform configurations. The embedded exponential family (EEF) approach is used to approximate distributions of parameters of dependent RF and EO measurements and to further improve target detection rate and tracking performance. The performance of the integrated algorithm is evaluated using real data from three experimental scenarios. / Dissertation/Thesis / M.S. Electrical Engineering 2012