Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Monte Carlo and analytical dose calculations for ocular proton therapy

Uveal melanoma is a rare but life-threatening form of ocular cancer. Contemporary treatment techniques include proton therapy, which enables conservation of the eye and its useful vision. Dose to the proximal structures is widely believed to play a role in treatment side effects, therefore, reliable dose estimates are required for properly evaluating the therapeutic value and complication risk of treatment plans. Unfortunately, current simplistic dose calculation algorithms can result in errors of up to 30% in the proximal region. In addition, they lack predictive methods for absolute dose per monitor unit (D/MU) values. ^ To facilitate more accurate dose predictions, a Monte Carlo model of an ocular proton nozzle was created and benchmarked against measured dose profiles to within ±3% or ±0.5 mm and D/MU values to within ±3%. The benchmarked Monte Carlo model was used to develop and validate a new broad beam dose algorithm that included the influence of edgescattered protons on the cross-field intensity profile, the effect of energy straggling in the distal portion of poly-energetic beams, and the proton fluence loss as a function of residual range. Generally, the analytical algorithm predicted relative dose distributions that were within ±3% or ±0.5 mm and absolute D/MU values that were within ±3% of Monte Carlo calculations. Slightly larger dose differences were observed at depths less than 7 mm, an effect attributed to the dose contributions of edge-scattered protons. Additional comparisons of Monte Carlo and broad beam dose predictions were made in a detailed eye model developed in this work, with generally similar findings. ^ Monte Carlo was shown to be an excellent predictor of the measured dose profiles and D/MU values and a valuable tool for developing and validating a broad beam dose algorithm for ocular proton therapy. The more detailed physics modeling by the Monte Carlo and broad beam dose algorithms represent an improvement in the accuracy of relative dose predictions over current techniques, and they provide absolute dose predictions. It is anticipated these improvements can be used to develop treatment strategies that reduce the incidence or severity of treatment complications by sparing normal tissue. ^

Design and Optimization of Four-dimensional Cone-beam Computed Tomography in Image-guided Radiation Therapy

The influence of respiratory motion on patient anatomy poses a challenge to accurate radiation therapy, especially in lung cancer treatment. Modern radiation therapy planning uses models of tumor respiratory motion to account for target motion in targeting. The tumor motion model can be verified on a per-treatment session basis with four-dimensional cone-beam computed tomography (4D-CBCT), which acquires an image set of the dynamic target throughout the respiratory cycle during the therapy session. 4D-CBCT is undersampled if the scan time is too short. However, short scan time is desirable in clinical practice to reduce patient setup time. This dissertation presents the design and optimization of 4D-CBCT to reduce the impact of undersampling artifacts with short scan times. This work measures the impact of undersampling artifacts on the accuracy of target motion measurement under different sampling conditions and for various object sizes and motions. The results provide a minimum scan time such that the target tracking error is less than a specified tolerance. This work also presents new image reconstruction algorithms for reducing undersampling artifacts in undersampled datasets by taking advantage of the assumption that the relevant motion of interest is contained within a volume-of-interest (VOI). It is shown that the VOI-based reconstruction provides more accurate image intensity than standard reconstruction. The VOI-based reconstruction produced 43% fewer least-squares error inside the VOI and 84% fewer error throughout the image in a study designed to simulate target motion. The VOI-based reconstruction approach can reduce acquisition time and improve image quality in 4D-CBCT.

Offshore stereo measurements of gravity waves: classical epipolar and novel variational techniques

Stereo video techniques are effective for estimating the space-time wave dynamics over an area of the ocean. Indeed, a stereo camera view allows retrieval of both spatial and temporal data whose statistical content is richer than that of time series data retrieved from point wave probes. Classical epipolar techniques and modern variational methods are reviewed to reconstruct the sea surface from the stereo pairs sequentially in time. Current improvements of the variational methods are presented.

Contribution to the quality improvement of the antenna measurement using post-processing techniques = Contribución a la mejora de la calidad de medida de antena usando técnicas de post-procesado

One important task in the design of an antenna is to carry out an analysis to find out the characteristics of the antenna that best fulfills the specifications fixed by the application. After that, a prototype is manufactured and the next stage in design process is to check if the radiation pattern differs from the designed one. Besides the radiation pattern, other radiation parameters like directivity, gain, impedance, beamwidth, efficiency, polarization, etc. must be also evaluated. For this purpose, accurate antenna measurement techniques are needed in order to know exactly the actual electromagnetic behavior of the antenna under test. Due to this fact, most of the measurements are performed in anechoic chambers, which are closed areas, normally shielded, covered by electromagnetic absorbing material, that simulate free space propagation conditions, due to the absorption of the radiation absorbing material. Moreover, these facilities can be employed independently of the weather conditions and allow measurements free from interferences. Despite all the advantages of the anechoic chambers, the results obtained both from far-field measurements and near-field measurements are inevitably affected by errors. Thus, the main objective of this Thesis is to propose algorithms to improve the quality of the results obtained in antenna measurements by using post-processing techniques and without requiring additional measurements. First, a deep revision work of the state of the art has been made in order to give a general vision of the possibilities to characterize or to reduce the effects of errors in antenna measurements. Later, new methods to reduce the unwanted effects of four of the most commons errors in antenna measurements are described and theoretical and numerically validated. The basis of all them is the same, to perform a transformation from the measurement surface to another domain where there is enough information to easily remove the contribution of the errors. The four errors analyzed are noise, reflections, truncation errors and leakage and the tools used to suppress them are mainly source reconstruction techniques, spatial and modal filtering and iterative algorithms to extrapolate functions. Therefore, the main idea of all the methods is to modify the classical near-field-to-far-field transformations by including additional steps with which errors can be greatly suppressed. Moreover, the proposed methods are not computationally complex and, because they are applied in post-processing, additional measurements are not required. The noise is the most widely studied error in this Thesis, proposing a total of three alternatives to filter out an important noise contribution before obtaining the far-field pattern. The first one is based on a modal filtering. The second alternative uses a source reconstruction technique to obtain the extreme near-field where it is possible to apply a spatial filtering. The last one is to back-propagate the measured field to a surface with the same geometry than the measurement surface but closer to the AUT and then to apply also a spatial filtering. All the alternatives are analyzed in the three most common near-field systems, including comprehensive noise statistical analyses in order to deduce the signal-to-noise ratio improvement achieved in each case. The method to suppress reflections in antenna measurements is also based on a source reconstruction technique and the main idea is to reconstruct the field over a surface larger than the antenna aperture in order to be able to identify and later suppress the virtual sources related to the reflective waves. The truncation error presents in the results obtained from planar, cylindrical and partial spherical near-field measurements is the third error analyzed in this Thesis. The method to reduce this error is based on an iterative algorithm to extrapolate the reliable region of the far-field pattern from the knowledge of the field distribution on the AUT plane. The proper termination point of this iterative algorithm as well as other critical aspects of the method are also studied. The last part of this work is dedicated to the detection and suppression of the two most common leakage sources in antenna measurements. A first method tries to estimate the leakage bias constant added by the receiver’s quadrature detector to every near-field data and then suppress its effect on the far-field pattern. The second method can be divided into two parts; the first one to find the position of the faulty component that radiates or receives unwanted radiation, making easier its identification within the measurement environment and its later substitution; and the second part of this method is able to computationally remove the leakage effect without requiring the substitution of the faulty component. Resumen Una tarea importante en el diseño de una antena es llevar a cabo un análisis para averiguar las características de la antena que mejor cumple las especificaciones fijadas por la aplicación. Después de esto, se fabrica un prototipo de la antena y el siguiente paso en el proceso de diseño es comprobar si el patrón de radiación difiere del diseñado. Además del patrón de radiación, otros parámetros de radiación como la directividad, la ganancia, impedancia, ancho de haz, eficiencia, polarización, etc. deben ser también evaluados. Para lograr este propósito, se necesitan técnicas de medida de antenas muy precisas con el fin de saber exactamente el comportamiento electromagnético real de la antena bajo prueba. Debido a esto, la mayoría de las medidas se realizan en cámaras anecoicas, que son áreas cerradas, normalmente revestidas, cubiertas con material absorbente electromagnético. Además, estas instalaciones se pueden emplear independientemente de las condiciones climatológicas y permiten realizar medidas libres de interferencias. A pesar de todas las ventajas de las cámaras anecoicas, los resultados obtenidos tanto en medidas en campo lejano como en medidas en campo próximo están inevitablemente afectados por errores. Así, el principal objetivo de esta Tesis es proponer algoritmos para mejorar la calidad de los resultados obtenidos en medida de antenas mediante el uso de técnicas de post-procesado. Primeramente, se ha realizado un profundo trabajo de revisión del estado del arte con el fin de dar una visión general de las posibilidades para caracterizar o reducir los efectos de errores en medida de antenas. Después, se han descrito y validado tanto teórica como numéricamente nuevos métodos para reducir el efecto indeseado de cuatro de los errores más comunes en medida de antenas. La base de todos ellos es la misma, realizar una transformación de la superficie de medida a otro dominio donde hay suficiente información para eliminar fácilmente la contribución de los errores. Los cuatro errores analizados son ruido, reflexiones, errores de truncamiento y leakage y las herramientas usadas para suprimirlos son principalmente técnicas de reconstrucción de fuentes, filtrado espacial y modal y algoritmos iterativos para extrapolar funciones. Por lo tanto, la principal idea de todos los métodos es modificar las transformaciones clásicas de campo cercano a campo lejano incluyendo pasos adicionales con los que los errores pueden ser enormemente suprimidos. Además, los métodos propuestos no son computacionalmente complejos y dado que se aplican en post-procesado, no se necesitan medidas adicionales. El ruido es el error más ampliamente estudiado en esta Tesis, proponiéndose un total de tres alternativas para filtrar una importante contribución de ruido antes de obtener el patrón de campo lejano. La primera está basada en un filtrado modal. La segunda alternativa usa una técnica de reconstrucción de fuentes para obtener el campo sobre el plano de la antena donde es posible aplicar un filtrado espacial. La última es propagar el campo medido a una superficie con la misma geometría que la superficie de medida pero más próxima a la antena y luego aplicar también un filtrado espacial. Todas las alternativas han sido analizadas en los sistemas de campo próximos más comunes, incluyendo detallados análisis estadísticos del ruido con el fin de deducir la mejora de la relación señal a ruido lograda en cada caso. El método para suprimir reflexiones en medida de antenas está también basado en una técnica de reconstrucción de fuentes y la principal idea es reconstruir el campo sobre una superficie mayor que la apertura de la antena con el fin de ser capaces de identificar y después suprimir fuentes virtuales relacionadas con las ondas reflejadas. El error de truncamiento que aparece en los resultados obtenidos a partir de medidas en un plano, cilindro o en la porción de una esfera es el tercer error analizado en esta Tesis. El método para reducir este error está basado en un algoritmo iterativo para extrapolar la región fiable del patrón de campo lejano a partir de información de la distribución del campo sobre el plano de la antena. Además, se ha estudiado el punto apropiado de terminación de este algoritmo iterativo así como otros aspectos críticos del método. La última parte de este trabajo está dedicado a la detección y supresión de dos de las fuentes de leakage más comunes en medida de antenas. El primer método intenta realizar una estimación de la constante de fuga del leakage añadido por el detector en cuadratura del receptor a todos los datos en campo próximo y después suprimir su efecto en el patrón de campo lejano. El segundo método se puede dividir en dos partes; la primera de ellas para encontrar la posición de elementos defectuosos que radian o reciben radiación indeseada, haciendo más fácil su identificación dentro del entorno de medida y su posterior substitución. La segunda parte del método es capaz de eliminar computacionalmente el efector del leakage sin necesidad de la substitución del elemento defectuoso.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Observation and Detection of Underground Cellars in the Duero Basin By GNSS, Lidar and Ground Penetrating Radar Techniques

The underground cellars that appear in different parts of Spain are part of an agricultural landscape dispersed, sometimes damaged, others at risk of disappearing. This paper studies the measurement and display of a group of wineries located in Atauta (Soria), in the Duero River corridor. It is a unique architectural complex, facing rising, built on a smooth hillock as shown in Fig. 1. These constructions are excavated in the ground. The access to the cave or underground cellar has a shape of a narrow tube or down gallery. Immediately after, this space gets wider. There, wine is produced and stored [1]. Observation and detection of the underground cellar, both on the outside and underground, it is essential to make an inventory of the rural patrimony [2]. The geodetection is a noninvasive technique, adequate to accurately locate buried structures in the ground. Works undertaken include topographic work with the LIDAR techniques and integration with data obtained by GNSS and GPR.

Smart aeronautical structures: development and experimental validation of a structural health monitoring system for damage detection

En muchas áreas de la ingeniería, la integridad y confiabilidad de las estructuras son aspectos de extrema importancia. Estos son controlados mediante el adecuado conocimiento de danos existentes. Típicamente, alcanzar el nivel de conocimiento necesario que permita caracterizar la integridad estructural implica el uso de técnicas de ensayos no destructivos. Estas técnicas son a menudo costosas y consumen mucho tiempo. En la actualidad, muchas industrias buscan incrementar la confiabilidad de las estructuras que emplean. Mediante el uso de técnicas de última tecnología es posible monitorizar las estructuras y en algunos casos, es factible detectar daños incipientes que pueden desencadenar en fallos catastróficos. Desafortunadamente, a medida que la complejidad de las estructuras, los componentes y sistemas incrementa, el riesgo de la aparición de daños y fallas también incrementa. Al mismo tiempo, la detección de dichas fallas y defectos se torna más compleja. En años recientes, la industria aeroespacial ha realizado grandes esfuerzos para integrar los sensores dentro de las estructuras, además de desarrollar algoritmos que permitan determinar la integridad estructural en tiempo real. Esta filosofía ha sido llamada “Structural Health Monitoring” (o “Monitorización de Salud Estructural” en español) y este tipo de estructuras han recibido el nombre de “Smart Structures” (o “Estructuras Inteligentes” en español). Este nuevo tipo de estructuras integran materiales, sensores, actuadores y algoritmos para detectar, cuantificar y localizar daños dentro de ellas mismas. Una novedosa metodología para detección de daños en estructuras se propone en este trabajo. La metodología está basada en mediciones de deformación y consiste en desarrollar técnicas de reconocimiento de patrones en el campo de deformaciones. Estas últimas, basadas en PCA (Análisis de Componentes Principales) y otras técnicas de reducción dimensional. Se propone el uso de Redes de difracción de Bragg y medidas distribuidas como sensores de deformación. La metodología se validó mediante pruebas a escala de laboratorio y pruebas a escala real con estructuras complejas. Los efectos de las condiciones de carga variables fueron estudiados y diversos experimentos fueron realizados para condiciones de carga estáticas y dinámicas, demostrando que la metodología es robusta ante condiciones de carga desconocidas. ABSTRACT In many engineering fields, the integrity and reliability of the structures are extremely important aspects. They are controlled by the adequate knowledge of existing damages. Typically, achieving the level of knowledge necessary to characterize the structural integrity involves the usage of nondestructive testing techniques. These are often expensive and time consuming. Nowadays, many industries look to increase the reliability of the structures used. By using leading edge techniques it is possible to monitoring these structures and in some cases, detect incipient damage that could trigger catastrophic failures. Unfortunately, as the complexity of the structures, components and systems increases, the risk of damages and failures also increases. At the same time, the detection of such failures and defects becomes more difficult. In recent years, the aerospace industry has done great efforts to integrate the sensors within the structures and, to develop algorithms for determining the structural integrity in real time. The ‘philosophy’ has being called “Structural Health Monitoring” and these structures have been called “smart structures”. These new types of structures integrate materials, sensors, actuators and algorithms to detect, quantify and locate damage within itself. A novel methodology for damage detection in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (Principal Component Analysis) and other dimensional reduction techniques. The use of fiber Bragg gratings and distributed sensing as strain sensors is proposed. The methodology have been validated by using laboratory scale tests and real scale tests with complex structures. The effects of the variable load conditions were studied and several experiments were performed for static and dynamic load conditions, demonstrating that the methodology is robust under unknown load conditions.

Métodos diferenciales e interferométricos para la evaluación de deformaciones de la corteza terrestre mediante técnicas GNSS e InSAR = Differential and interferometric methods for evaluation of crustal deformation using GNSS and InSAR techniques

La actividad volcánica interviene en multitud de facetas de la propia actividad humana, no siempre negativas. Sin embargo, son más los motivos de peligrosidad y riesgo que incitan al estudio de la actividad volcánica. Existen razones de seguridad que inciden en el mantenimiento del seguimiento y monitorización de la actividad volcánica para garantizar la vida y la seguridad de los asentamientos antrópicos en las proximidades de los edificios volcánicos. En esta tesis se define e implementa un sistema de monitorización de movimientos de la corteza en las islas de Tenerife y La Palma, donde el impacto social que representa un aumento o variación de la actividad volcánica en las islas es muy severo. Aparte de la alta densidad demográfica del Archipiélago, esta población aumenta significativamente, en diferentes periodos a lo largo del año, debido a la actividad turística que representa la mayor fuente de ingresos de las islas. La población y los centros turísticos se diseminan predominantemente a lo largo de las costas y también a lo largo de los flancos de los edificios volcánicos. Quizá el mantenimiento de estas estructuras sociales y socio-económicas son los motivos más importantes que justifican una monitorización de la actividad volcánica en las Islas Canarias. Recientemente se ha venido trabajando cada vez más en el intento de predecir la actividad volcánica utilizando los nuevos sistemas de monitorización geodésica, puesto que la actividad volcánica se manifiesta anteriormente por deformación de la corteza terrestre y cambios en la fuerza de la gravedad en la zona donde más tarde se registran eventos volcánicos. Los nuevos dispositivos y sensores que se han desarrollado en los últimos años en materias como la geodesia, la observación de la Tierra desde el espacio y el posicionamiento por satélite, han permitido observar y medir tanto la deformación producida en el terreno como los cambios de la fuerza de la gravedad antes, durante y posteriormente a los eventos volcánicos que se producen. Estos nuevos dispositivos y sensores han cambiado las técnicas o metodologías geodésicas que se venían utilizando hasta la aparición de los mismos, renovando métodos clásicos y desarrollando otros nuevos que ya se están afianzando como metodologías probadas y reconocidas para ser usadas en la monitorización volcánica. Desde finales de la década de los noventa del siglo pasado se han venido desarrollando en las Islas Canarias varios proyectos que han tenido como objetivos principales el desarrollo de nuevas técnicas de observación y monitorización por un lado y el diseño de una metodología de monitorización volcánica adecuada, por otro. Se presenta aquí el estudio y desarrollo de técnicas GNSS para la monitorización de deformaciones corticales y su campo de velocidades para las islas de Tenerife y La Palma. En su implementación, se ha tenido en cuenta el uso de la infraestructura geodésica y de monitorización existente en el archipiélago a fin de optimizar costes, además de complementarla con nuevas estaciones para dar una cobertura total a las dos islas. Los resultados obtenidos en los proyectos, que se describen en esta memoria, han dado nuevas perspectivas en la monitorización geodésica de la actividad volcánica y nuevas zonas de interés que anteriormente no se conocían en el entorno de las Islas Canarias. Se ha tenido especial cuidado en el tratamiento y propagación de los errores durante todo el proceso de observación, medida y proceso de los datos registrados, todo ello en aras de cuantificar el grado de fiabilidad de los resultados obtenidos. También en este sentido, los resultados obtenidos han sido verificados con otros procedentes de sistemas de observación radar de satélite, incorporando además a este estudio las implicaciones que el uso conjunto de tecnologías radar y GNSS tendrán en un futuro en la monitorización de deformaciones de la corteza terrestre. ABSTRACT Volcanic activity occurs in many aspects of human activity, and not always in a negative manner. Nonetheless, research into volcanic activity is more likely to be motivated by its danger and risk. There are security reasons that influence the monitoring of volcanic activity in order to guarantee the life and safety of human settlements near volcanic edifices. This thesis defines and implements a monitoring system of movements in the Earth’s crust in the islands of Tenerife and La Palma, where the social impact of an increase (or variation) of volcanic activity is very severe. Aside from the high demographic density of the archipelago, the population increases significantly in different periods throughout the year due to tourism, which represents a major source of revenue for the islands. The population and the tourist centres are mainly spread along the coasts and also along the flanks of the volcanic edifices. Perhaps the preservation of these social and socio-economic structures is the most important reason that justifies monitoring volcanic activity in the Canary Islands. Recently more and more work has been done with the intention of predicting volcanic activity, using new geodesic monitoring systems, since volcanic activity is evident prior to eruption because of a deformation of the Earth’s crust and changes in the force of gravity in the zone where volcanic events will later be recorded. The new devices and sensors that have been developed in recent years in areas such as geodesy, the observation of the Earth from space, and satellite positioning have allowed us to observe and measure the deformation produced in the Earth as well as the changes in the force of gravity before, during, and after the volcanic events occur. The new devices and sensors have changed the geodetic techniques and methodologies that were used previously. The classic methods have been renovated and other newer ones developed that are now vouched for as proven recognised methodologies to be used for volcanic monitoring. Since the end of the 1990s, in the Canary Islands various projects have been developed whose principal aim has been the development of new observation and monitoring techniques on the one hand, and the design of an appropriate volcanic monitoring methodology on the other. The study and development of GNSS techniques for the monitoring of crustal deformations and their velocity field is presented here. To carry out the study, the use of geodetic infrastructure and existing monitoring in the archipelago have been taken into account in order to optimise costs, besides complementing it with new stations for total coverage on both islands. The results obtained in the projects, which are described below, have produced new perspectives in the geodetic monitoring of volcanic activity and new zones of interest which previously were unknown in the environment of the Canary Islands. Special care has been taken with the treatment and propagation of errors during the entire process of observing, measuring, and processing the recorded data. All of this was done in order to quantify the degree of trustworthiness of the results obtained. Also in this sense, the results obtained have been verified with others from satellite radar observation systems, incorporating as well in this study the implications that the joint use of radar technologies and GNSS will have for the future of monitoring deformations in the Earth’s crust.

Odor space and olfactory processing: Collective algorithms and neural implementation

Several basic olfactory tasks must be solved by highly olfactory animals, including background suppression, multiple object separation, mixture separation, and source identification. The large number N of classes of olfactory receptor cells—hundreds or thousands—permits the use of computational strategies and algorithms that would not be effective in a stimulus space of low dimension. A model of the patterns of olfactory receptor responses, based on the broad distribution of olfactory thresholds, is constructed. Representing one odor from the viewpoint of another then allows a common description of the most important basic problems and shows how to solve them when N is large. One possible biological implementation of these algorithms uses action potential timing and adaptation as the “hardware” features that are responsible for effective neural computation.

Measuring the stellar wind parameters in IGR J17544-2619 and Vela X-1 constrains the accretion physics in supergiant fast X-ray transient and classical supergiant X-ray binaries

Context. Classical supergiant X-ray binaries (SGXBs) and supergiant fast X-ray transients (SFXTs) are two types of high-mass X-ray binaries (HMXBs) that present similar donors but, at the same time, show very different behavior in the X-rays. The reason for this dichotomy of wind-fed HMXBs is still a matter of debate. Among the several explanations that have been proposed, some of them invoke specific stellar wind properties of the donor stars. Only dedicated empiric analysis of the donors’ stellar wind can provide the required information to accomplish an adequate test of these theories. However, such analyses are scarce. Aims. To close this gap, we perform a comparative analysis of the optical companion in two important systems: IGR J17544-2619 (SFXT) and Vela X-1 (SGXB). We analyze the spectra of each star in detail and derive their stellar and wind properties. As a next step, we compare the wind parameters, giving us an excellent chance of recognizing key differences between donor winds in SFXTs and SGXBs. Methods. We use archival infrared, optical and ultraviolet observations, and analyze them with the non-local thermodynamic equilibrium (NLTE) Potsdam Wolf-Rayet model atmosphere code. We derive the physical properties of the stars and their stellar winds, accounting for the influence of X-rays on the stellar winds. Results. We find that the stellar parameters derived from the analysis generally agree well with the spectral types of the two donors: O9I (IGR J17544-2619) and B0.5Iae (Vela X-1). The distance to the sources have been revised and also agree well with the estimations already available in the literature. In IGR J17544-2619 we are able to narrow the uncertainty to d = 3.0 ± 0.2 kpc. From the stellar radius of the donor and its X-ray behavior, the eccentricity of IGR J17544-2619 is constrained to e< 0.25. The derived chemical abundances point to certain mixing during the lifetime of the donors. An important difference between the stellar winds of the two stars is their terminal velocities (ν∞ = 1500 km s-1 in IGR J17544-2619 and ν∞ = 700 km s-1 in Vela X-1), which have important consequences on the X-ray luminosity of these sources. Conclusions. The donors of IGR J17544-2619 and Vela X-1 have similar spectral types as well as similar parameters that physically characterize them and their spectra. In addition, the orbital parameters of the systems are similar too, with a nearly circular orbit and short orbital period. However, they show moderate differences in their stellar wind velocity and the spin period of their neutron star which has a strong impact on the X-ray luminosity of the sources. This specific combination of wind speed and pulsar spin favors an accretion regime with a persistently high luminosity in Vela X-1, while it favors an inhibiting accretion mechanism in IGR J17544-2619. Our study demonstrates that the relative wind velocity is critical in class determination for the HMXBs hosting a supergiant donor, given that it may shift the accretion mechanism from direct accretion to propeller regimes when combined with other parameters.

Methods of visualization and analysis of cardiac depolarization in the three dimensional space

The master thesis presents methods for intellectual analysis and visualization 3D EKG in order to increase the efficiency of ECG analysis by extracting additional data. Visualization is presented as part of the signal analysis tasks considered imaging techniques and their mathematical description. Have been developed algorithms for calculating and visualizing the signal attributes are described using mathematical methods and tools for mining signal. The model of patterns searching for comparison purposes of accuracy of methods was constructed, problems of a clustering and classification of data are solved, the program of visualization of data is also developed. This approach gives the largest accuracy in a task of the intellectual analysis that is confirmed in this work. Considered visualization and analysis techniques are also applicable to the multi-dimensional signals of a different kind.

The correspondence framework for 3D surface matching algorithms

Beyond the inherent technical challenges, current research into the three dimensional surface correspondence problem is hampered by a lack of uniform terminology, an abundance of application specific algorithms, and the absence of a consistent model for comparing existing approaches and developing new ones. This paper addresses these challenges by presenting a framework for analysing, comparing, developing, and implementing surface correspondence algorithms. The framework uses five distinct stages to establish correspondence between surfaces. It is general, encompassing a wide variety of existing techniques, and flexible, facilitating the synthesis of new correspondence algorithms. This paper presents a review of existing surface correspondence algorithms, and shows how they fit into the correspondence framework. It also shows how the framework can be used to analyse and compare existing algorithms and develop new algorithms using the framework's modular structure. Six algorithms, four existing and two new, are implemented using the framework. Each implemented algorithm is used to match a number of surface pairs. Results demonstrate that the correspondence framework implementations are faithful implementations of existing algorithms, and that powerful new surface correspondence algorithms can be created. (C) 2004 Elsevier Inc. All rights reserved.

The past, present and future of observational research in marketing

Purpose - To introduce the contents of the special issue, and provide an integrative overview of the development of observational methodologies in marketing research, as well as some directions for the future. Design/methodology/approach - A historical review of the development of observational methods, beginning with philosophical foundations, is provided. Key philosophical debates are summarized, and trends in observational methods are described and analyzed, with particular reference to the impact of technology. Following this, the contributions to the special issue are summarized and brought together. Findings - Observational research in marketing is more than the well-known method of "participant-observation." In fact, technology has the potential to revolutionize observational research, and move it beyond a solely "qualitative" method. The internet, video, scanner-tracking, and neuroimaging methods are all likely to have a big impact on the development of traditional and innovative observation methods in the future. The articles in the special issue provide a good overview of these developments. Research limitations/implications - The views of the authors may differ from those of others. Practical implications - Observation is a far more wide-ranging strategy than many perceive. There is a need for more expertise in all types of observational methodologies within marketing research schools and departments, in order to take account of the vast opportunities which are currently emerging. Originality/value - Provides an original perspective on observational methods, and serves as a useful overview of trends and developments in the field.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.