Realisierung der IceCube MonteCarlo-Produktion im WLCG und Untersuchung von Auswirkungen meteorologischer Parameter auf die Erzeugung atmosphärischer Myonen

Das am Südpol gelegene Neutrinoteleskop IceCube detektiert hochenergetische Neutrinos über die schwache Wechselwirkung geladener und neutraler Ströme. Die Analyse basiert auf einem Vergleich mit Monte-Carlo-Simulationen, deren Produktion global koordiniert wird. In Mainz ist es erstmalig gelungen, Simulationen innerhalb der Architektur des Worldwide LHC Computing Grid (WLCG) zu realisieren, was die Möglichkeit eröffnet, Monte-Carlo-Berechnungen auch auf andere deutsche Rechnerfarmen (CEs) mit IceCube-Berechtigung zu verteilen. Atmosphärische Myonen werden mit einer Rate von über 1000 Ereignissen pro Sekunde aufgezeichnet. Eine korrekte Interpretation dieses dominanten Signals, welches um einen Faktor von 10^6 reduziert werden muss um das eigentliche Neutrinosignal zu extrahieren, ist deswegen von großer Bedeutung. Eigene Simulationen mit der Software-Umgebung CORSIKA wurden durchgeführt um die von Energie und Einfallswinkel abhängige Entstehungshöhe atmosphärischer Myonen zu bestimmen. IceCube Myonraten wurden mit Wetterdaten des European Centre for Medium-Range Weather Forcasts (ECMWF) verglichen und Korrelationen zwischen jahreszeitlichen sowie kurzzeitigen Schwankungen der Atmosphärentemperatur und Myonraten konnten nachgewiesen werden. Zudem wurde eine Suche nach periodischen Effekten in der Atmosphäre, verursacht durch z.B. meteorologische Schwerewellen, mit Hilfe einer Fourieranalyse anhand der IceCube-Daten durchgeführt. Bislang konnte kein signifikanter Nachweis zur Existenz von Schwerewellen am Südpol erbracht werden.

Development and characterization of micromachined devices for separation techniques

Nowadays microfluidic is becoming an important technology in many chemical and biological processes and analysis applications. The potential to replace large-scale conventional laboratory instrumentation with miniaturized and self-contained systems, (called lab-on-a-chip (LOC) or point-of-care-testing (POCT)), offers a variety of advantages such as low reagent consumption, faster analysis speeds, and the capability of operating in a massively parallel scale in order to achieve high-throughput. Micro-electro-mechanical-systems (MEMS) technologies enable both the fabrication of miniaturized system and the possibility of developing compact and portable systems. The work described in this dissertation is towards the development of micromachined separation devices for both high-speed gas chromatography (HSGC) and gravitational field-flow fractionation (GrFFF) using MEMS technologies. Concerning the HSGC, a complete platform of three MEMS-based GC core components (injector, separation column and detector) is designed, fabricated and characterized. The microinjector consists of a set of pneumatically driven microvalves, based on a polymeric actuating membrane. Experimental results demonstrate that the microinjector is able to guarantee low dead volumes, fast actuation time, a wide operating temperature range and high chemical inertness. The microcolumn consists of an all-silicon microcolumn having a nearly circular cross-section channel. The extensive characterization has produced separation performances very close to the theoretical ideal expectations. A thermal conductivity detector (TCD) is chosen as most proper detector to be miniaturized since the volume reduction of the detector chamber results in increased mass and reduced dead volumes. The microTDC shows a good sensitivity and a very wide dynamic range. Finally a feasibility study for miniaturizing a channel suited for GrFFF is performed. The proposed GrFFF microchannel is at early stage of development, but represents a first step for the realization of a highly portable and potentially low-cost POCT device for biomedical applications.

Charge separation and recombination in novel polymeric absorber materials for organic solar cells : a photophysical study

In dieser Dissertation wird die Ladungsträgergeneration und -rekombination in neuen polymeren Absorbermaterialien für organische Solarzellen untersucht. Das Verständnis dieser Prozesse ist wesentlich für die Entwicklung neuer photoaktiver Materialsysteme, die hohe Effizienzen erzielen und organische Solarzellen konkurrenzfähig im Bereich der erneuerbaren Energien machen. Experimentell verwendet diese Arbeit hauptsächlich die Methode der transienten Absorptionsspektroskopie, die sich für die Untersuchung photophysikalischer Prozesse auf einer Zeitskala von 100 fs bis 1 ms als sehr leistungsfähig erweist. Des Weiteren wird eine soft-modeling Methode vorgestellt, die es ermöglicht, photophysikalische Prozesse aus einer gemessenen transienten Absorptions-Datenmatrix zu bestimmen, wenn wenig a priori Kenntnisse der Reaktionskinetiken vorhanden sind. Drei unterschiedliche Donor:Akzeptor-Systeme werden untersucht; jedes dieser Systeme stellt eine andere Herangehensweise zur Optimierung der Materialien dar in Bezug auf Lichtabsorption über einen breiten Wellenlängenbereich, effiziente Ladungstrennung und schnellen Ladungstransport. Zuerst wird ein Terpolymer untersucht, das aus unterschiedlichen Einheiten für die Lichtabsorption und den Ladungstransport besteht. Es wird gezeigt, dass es möglich ist, den Fluss angeregter Zustände vom Chromophor auf die Transporteinheit zu leiten. Im zweiten Teil wird der Einfluss von Kristallinität auf die freie Ladungsträgergeneration mit einer Folge von ternären Mischungen, die unterschiedliche Anteile an amorphem und semi-kristallinem Polymer enthalten, untersucht. Dabei zeigt es sich, dass mit steigendem amorphen Polymeranteil sowohl der Anteil der geminalen Ladungsträgerrekombination erhöht als auch die nicht-geminale Rekombination schneller ist. Schlussendlich wird ein System untersucht, in dem sowohl Donor als auch Akzeptor Polymere sind, was zu verbesserten Absorptionseigenschaften führt. Die Rekombination von Ladungstransferzuständen auf der unter 100 ps Zeitskala stellt hier den hauptsächliche Verlustkanal dar, da freie Ladungsträger nur an Grenzflächen erzeugt werden können, an denen Donor und Akzeptor face-to-face zueinander orientiert sind. Darüber hinaus wird festgestellt, dass weitere 40-50% der Ladungsträger durch die Rekombination von Grenzflächenzuständen verloren gehen, die aus mobilen Ladungsträgern geminal gebildet werden.

Thermoelectric performance of p-type TiCoSb half-Heusler compounds - Intrinsic phase separation and charge carrier concentration optimization as key to high efficiency

The world's rising demand of energy turns the development of sustainable and more efficient technologies for energy production and storage into an inevitable task. Thermoelectric generators, composed of pairs of n-type and p-type semiconducting materials, di¬rectly transform waste heat into useful electricity. The efficiency of a thermoelectric mate¬rial depends on its electronic and lattice properties, summarized in its figure of merit ZT. Desirable are high electrical conductivity and Seebeck coefficients, and low thermal con¬ductivity. Half-Heusler materials are very promising candidates for thermoelectric applications in the medium¬ temperature range such as in industrial and automotive waste heat recovery. The advantage of Heusler compounds are excellent electronic properties and high thermal and mechanical stability, as well as their low toxicity and elemental abundance. Thus, the main obstacle to further enhance their thermoelectric performance is their relatively high thermal conductivity.rn rnIn this work, the thermoelectric properties of the p-type material (Ti/Zr/Hf)CoSb1-xSnx were optimized in a multistep process. The concept of an intrinsic phase separation has recently become a focus of research in the compatible n-type (Ti/Zr/Hf)NiSn system to achieve low thermal conductivities and boost the TE performance. This concept is successfully transferred to the TiCoSb system. The phase separation approach can form a significant alternative to the previous nanostructuring approach via ball milling and hot pressing, saving pro¬cessing time, energy consumption and increasing the thermoelectric efficiency. A fundamental concept to tune the performance of thermoelectric materials is charge carrier concentration optimization. The optimum carrier concentration is reached with a substitution level for Sn of x = 0.15, enhancing the ZT about 40% compared to previous state-of-the-art samples with x = 0.2. The TE performance can be enhanced further by a fine-tuning of the Ti-to-Hf ratio. A correlation of the microstructure and the thermoelectric properties is observed and a record figure of merit ZT = 1.2 at 710°C was reached with the composition Ti0.25Hf0.75CoSb0.85Sn0.15.rnTowards application, the long term stability of the material under actual conditions of operation are an important issue. The impact of such a heat treatment on the structural and thermoelectric properties is investigated. Particularly, the best and most reliable performance is achieved in Ti0.5Hf0.5CoSb0.85Sn0.15, which reached a maximum ZT of 1.1 at 700°C. The intrinsic phase separation and resulting microstructure is stable even after 500 heating and cooling cycles.

Development of a morphometric magnetic resonance image parameter suitable for distinguishing between normal dogs and dogs with cerebellar atrophy

Neurodegenerative diseases affect the cerebellum of numerous dog breeds. Although subjective, magnetic resonance (MR) imaging has been used to detect cerebellar atrophy in these diseases, but there are few data available on the normal size range of the cerebellum relative to other brain regions. The purpose of this study was to determine whether the size of the cerebellum maintains a consistent ratio with other brain regions in different ages and breeds of normal dogs and to define a measurement that can be used to identify cerebellar atrophy on MR images. Images from 52 normal and 13 dogs with cerebellar degenerative diseases were obtained. Volume and mid-sagittal cross-sectional area of the forebrain, brainstem, and cerebellum were calculated for each normal dog and compared between different breeds and ages as absolute and relative values. The ratio of the cerebellum to total brain and of the brainstem to cerebellum mid-sagittal cross-sectional area was compared between normal and affected dogs and the sensitivity and specificity of these ratios at distinguishing normal from affected dogs was calculated. The percentage of the brain occupied by the cerebellum in diverse dog breeds between 1 and 5 years of age was not significantly different, and cerebellar size did not change with increasing age. Using a cut off of 89%, the ratio between the brainstem and cerebellum mid-sagittal cross-sectional area could be used successfully to differentiate affected from unaffected dogs with a sensitivity and specificity of 100%, making this ratio an effective tool for identifying cerebellar atrophy on MR images.

Parameter estimation for transformer modeling

Large Power transformers, an aging and vulnerable part of our energy infrastructure, are at choke points in the grid and are key to reliability and security. Damage or destruction due to vandalism, misoperation, or other unexpected events is of great concern, given replacement costs upward of $2M and lead time of 12 months. Transient overvoltages can cause great damage and there is much interest in improving computer simulation models to correctly predict and avoid the consequences. EMTP (the Electromagnetic Transients Program) has been developed for computer simulation of power system transients. Component models for most equipment have been developed and benchmarked. Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Thus, transformer modeling is not a mature field and newer improved models must be made available. In this work, improved topologically-correct duality-based models are developed for three-phase autotransformers having five-legged, three-legged, and shell-form cores. The main problem in the implementation of detailed models is the lack of complete and reliable data, as no international standard suggests how to measure and calculate parameters. Therefore, parameter estimation methods are developed here to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, λ-i saturation characteristic, capacitive effects, and frequency dependency of winding resistance and core loss. Steady-state excitation, and de-energization and re-energization transients are simulated and compared with an earlier-developed BCTRAN-based model. Black start energization cases are also simulated as a means of model evaluation and compared with actual event records. The simulated results using the model developed here are reasonable and more correct than those of the BCTRAN-based model. Simulation accuracy is dependent on the accuracy of the equipment model and its parameters. This work is significant in that it advances existing parameter estimation methods in cases where the available data and measurements are incomplete. The accuracy of EMTP simulation for power systems including three-phase autotransformers is thus enhanced. Theoretical results obtained from this work provide a sound foundation for development of transformer parameter estimation methods using engineering optimization. In addition, it should be possible to refine which information and measurement data are necessary for complete duality-based transformer models. To further refine and develop the models and transformer parameter estimation methods developed here, iterative full-scale laboratory tests using high-voltage and high-power three-phase transformer would be helpful.

High precision range estimation techniques for multi-path wireless environments

Range estimation is the core of many positioning systems such as radar, and Wireless Local Positioning Systems (WLPS). The estimation of range is achieved by estimating Time-of-Arrival (TOA). TOA represents the signal propagation delay between a transmitter and a receiver. Thus, error in TOA estimation causes degradation in range estimation performance. In wireless environments, noise, multipath, and limited bandwidth reduce TOA estimation performance. TOA estimation algorithms that are designed for wireless environments aim to improve the TOA estimation performance by mitigating the effect of closely spaced paths in practical (positive) signal-to-noise ratio (SNR) regions. Limited bandwidth avoids the discrimination of closely spaced paths. This reduces TOA estimation performance. TOA estimation methods are evaluated as a function of SNR, bandwidth, and the number of reflections in multipath wireless environments, as well as their complexity. In this research, a TOA estimation technique based on Blind signal Separation (BSS) is proposed. This frequency domain method estimates TOA in wireless multipath environments for a given signal bandwidth. The structure of the proposed technique is presented and its complexity and performance are theoretically evaluated. It is depicted that the proposed method is not sensitive to SNR, number of reflections, and bandwidth. In general, as bandwidth increases, TOA estimation performance improves. However, spectrum is the most valuable resource in wireless systems and usually a large portion of spectrum to support high performance TOA estimation is not available. In addition, the radio frequency (RF) components of wideband systems suffer from high cost and complexity. Thus, a novel, multiband positioning structure is proposed. The proposed technique uses the available (non-contiguous) bands to support high performance TOA estimation. This system incorporates the capabilities of cognitive radio (CR) systems to sense the available spectrum (also called white spaces) and to incorporate white spaces for high-performance localization. First, contiguous bands that are divided into several non-equal, narrow sub-bands that possess the same SNR are concatenated to attain an accuracy corresponding to the equivalent full band. Two radio architectures are proposed and investigated: the signal is transmitted over available spectrum either simultaneously (parallel concatenation) or sequentially (serial concatenation). Low complexity radio designs that handle the concatenation process sequentially and in parallel are introduced. Different TOA estimation algorithms that are applicable to multiband scenarios are studied and their performance is theoretically evaluated and compared to simulations. Next, the results are extended to non-contiguous, non-equal sub-bands with the same SNR. These are more realistic assumptions in practical systems. The performance and complexity of the proposed technique is investigated as well. This study’s results show that selecting bandwidth, center frequency, and SNR levels for each sub-band can adapt positioning accuracy.

End-tidal CO2 pressure: an important parameter for a correct interpretation of changes in cerebral hemodynamics and oxygenation measured with functional near infrared spectrophotometry (fNIRS)

The aim of the present study was to investigate the effects of different speech tasks (recitation of prose (PR), alliteration (AR) and hexameter (HR) verses) and a control task (mental arithmetic (MA) with voicing of the result) on endtidal CO2 (ET-CO2), cerebral hemodynamics; i.e. total hemoglobin (tHb) and tissue oxygen saturation (StO2). tHb and StO2 were measured with a frequency domain near infrared spectrophotometer (ISS Inc., USA) and ET-CO2 with a gas analyzer (Nellcor N1000). Measurements were performed in 24 adult volunteers (11 female, 13 male; age range 22 to 64 years) during task performance in a randomized order on 4 different days to avoid potential carry over effects. Statistical analysis was applied to test differences between baseline, 2 recitation and 5 recovery periods. The two brain hemispheres and 4 tasks were tested separately. Data analysis revealed that during the recitation tasks (PR, AR and HR) StO2 decreased statistically significant (p < 0.05) during PR and AR in the right prefrontal cortex (PFC) and during AR and HR in the left PFC. tHb showed a significant decrease during HR in the right PFC and during PR, AR and HR in the left PFC. During the MA task, StO2 increased significantly. A significant decrease in ET-CO2 was found during all 4 tasks with the smallest decrease during the MA task. In conclusion, we hypothesize that the observed changes in tHb and StO2 are mainly caused by an altered breathing during the tasks that led a lowering of the CO2 content in the blood provoked a cerebral CO2 reaction, i.e. a vasoconstriction of blood vessels due to decreased CO2 pressure and thereby decrease in cerebral blood volume. Therefore, breathing changes should be monitored during brain studies involving speech when using functional near infrared spectroscopy (fNIRS) to ensure a correct interpretation of changes in hemodynamics and oxygenation.

Optimal Test for Parameter Instability When the Unstable Process and the Error Distribution are Unknown

This paper proposes asymptotically optimal tests for unstable parameter process under the feasible circumstance that the researcher has little information about the unstable parameter process and the error distribution, and suggests conditions under which the knowledge of those processes does not provide asymptotic power gains. I first derive a test under known error distribution, which is asymptotically equivalent to LR tests for correctly identified unstable parameter processes under suitable conditions. The conditions are weak enough to cover a wide range of unstable processes such as various types of structural breaks and time varying parameter processes. The test is then extended to semiparametric models in which the underlying distribution in unknown but treated as unknown infinite dimensional nuisance parameter. The semiparametric test is adaptive in the sense that its asymptotic power function is equivalent to the power envelope under known error distribution.

Geological investigation of dykes in Shackleton Range, Antarctica

During the Geological Expedition to the Shackleton Range, Antarctica (GEISHA) in 1987/88, samples were taken from twenty-one basaltic dykes for palaeomagnetic investigations. The directions of characteristic remanent magnetization (ChRM) of the dykes were determined by thermal and alternating-field demagnetization of 268 cores drilled from the specimens collected. Moreover, on account of the hydrothermal and sometimes low-grade metamorphism of the dyke rock and the resulting partial modification of the primary magnetization, not only were comprehensive magnetic studies carried out, but also ore-microscopic examination. Only thus was it possible to achieve a reasonable assessment and interpretation of the remanent magnetization. Jurassic and Silurian-Devonian ages were confirmed for the dykes of the northern and northwestern Shackleton Range by comparison of the paleopole positions calculated on the basis of the ChRM of the dykes with the known pole positions for the eastern Antarctic, as well as with polar-wandering curves for Gondwana. Radiometric ages were also determined far some of the dykes. Middle and Late Proterozoic ages are postulated far the dykes in the Read Mountains. Conclusions on the geotectonic relations of the Shackleton Range can also be drawn from the palaeomagnetic data. It has been postulated that the main strike direction, which differs distinctly from that of the Ross orogen, is due to rotation or displacement of the Shackleton Range crustal block; however, this was not corroborated. The pole positions for the Shackleton Range agree with those of rocks of the same age from other areas of East Antarctica and its positions in the Palaeozoic-Mesozoic polar-wandering path for Gondwana are evidence against the idea of rotation and rather suggest that the position of the Shakleton Range crustal block is autochthonous.

Detección de patrones y anomalías espectrales del terreno mediante espectrometría de imagen de alta resolución : reconocimiento, optimización y evaluación multiescenario

El objeto de esta Tesis doctoral es el desarrollo de una metodologia para la deteccion automatica de anomalias a partir de datos hiperespectrales o espectrometria de imagen, y su cartografiado bajo diferentes condiciones tipologicas de superficie y terreno. La tecnologia hiperespectral o espectrometria de imagen ofrece la posibilidad potencial de caracterizar con precision el estado de los materiales que conforman las diversas superficies en base a su respuesta espectral. Este estado suele ser variable, mientras que las observaciones se producen en un numero limitado y para determinadas condiciones de iluminacion. Al aumentar el numero de bandas espectrales aumenta tambien el numero de muestras necesarias para definir espectralmente las clases en lo que se conoce como Maldicion de la Dimensionalidad o Efecto Hughes (Bellman, 1957), muestras habitualmente no disponibles y costosas de obtener, no hay mas que pensar en lo que ello implica en la Exploracion Planetaria. Bajo la definicion de anomalia en su sentido espectral como la respuesta significativamente diferente de un pixel de imagen respecto de su entorno, el objeto central abordado en la Tesis estriba primero en como reducir la dimensionalidad de la informacion en los datos hiperespectrales, discriminando la mas significativa para la deteccion de respuestas anomalas, y segundo, en establecer la relacion entre anomalias espectrales detectadas y lo que hemos denominado anomalias informacionales, es decir, anomalias que aportan algun tipo de informacion real de las superficies o materiales que las producen. En la deteccion de respuestas anomalas se asume un no conocimiento previo de los objetivos, de tal manera que los pixeles se separan automaticamente en funcion de su informacion espectral significativamente diferenciada respecto de un fondo que se estima, bien de manera global para toda la escena, bien localmente por segmentacion de la imagen. La metodologia desarrollada se ha centrado en la implicacion de la definicion estadistica del fondo espectral, proponiendo un nuevo enfoque que permite discriminar anomalias respecto fondos segmentados en diferentes grupos de longitudes de onda del espectro, explotando la potencialidad de separacion entre el espectro electromagnetico reflectivo y emisivo. Se ha estudiado la eficiencia de los principales algoritmos de deteccion de anomalias, contrastando los resultados del algoritmo RX (Reed and Xiaoli, 1990) adoptado como estandar por la comunidad cientifica, con el metodo UTD (Uniform Targets Detector), su variante RXD-UTD, metodos basados en subespacios SSRX (Subspace RX) y metodo basados en proyecciones de subespacios de imagen, como OSPRX (Orthogonal Subspace Projection RX) y PP (Projection Pursuit). Se ha desarrollado un nuevo metodo, evaluado y contrastado por los anteriores, que supone una variacion de PP y describe el fondo espectral mediante el analisis discriminante de bandas del espectro electromagnetico, separando las anomalias con el algortimo denominado Detector de Anomalias de Fondo Termico o DAFT aplicable a sensores que registran datos en el espectro emisivo. Se han evaluado los diferentes metodos de deteccion de anomalias en rangos del espectro electromagnetico del visible e infrarrojo cercano (Visible and Near Infrared-VNIR), infrarrojo de onda corta (Short Wavelenght Infrared-SWIR), infrarrojo medio (Meadle Infrared-MIR) e infrarrojo termico (Thermal Infrared-TIR). La respuesta de las superficies en las distintas longitudes de onda del espectro electromagnetico junto con su entorno, influyen en el tipo y frecuencia de las anomalias espectrales que puedan provocar. Es por ello que se han utilizado en la investigacion cubos de datos hiperepectrales procedentes de los sensores aeroportados cuya estrategia y diseno en la construccion espectrometrica de la imagen difiere. Se han evaluado conjuntos de datos de test de los sensores AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) y MASTER (MODIS/ASTER Simulator). Se han disenado experimentos sobre ambitos naturales, urbanos y semiurbanos de diferente complejidad. Se ha evaluado el comportamiento de los diferentes detectores de anomalias a traves de 23 tests correspondientes a 15 areas de estudio agrupados en 6 espacios o escenarios: Urbano - E1, Semiurbano/Industrial/Periferia Urbana - E2, Forestal - E3, Agricola - E4, Geologico/Volcanico - E5 y Otros Espacios Agua, Nubes y Sombras - E6. El tipo de sensores evaluados se caracteriza por registrar imagenes en un amplio rango de bandas, estrechas y contiguas, del espectro electromagnetico. La Tesis se ha centrado en el desarrollo de tecnicas que permiten separar y extraer automaticamente pixeles o grupos de pixeles cuya firma espectral difiere de manera discriminante de las que tiene alrededor, adoptando para ello como espacio muestral parte o el conjunto de las bandas espectrales en las que ha registrado radiancia el sensor hiperespectral. Un factor a tener en cuenta en la investigacion ha sido el propio instrumento de medida, es decir, la caracterizacion de los distintos subsistemas, sensores imagen y auxiliares, que intervienen en el proceso. Para poder emplear cuantitativamente los datos medidos ha sido necesario definir las relaciones espaciales y espectrales del sensor con la superficie observada y las potenciales anomalias y patrones objetivos de deteccion. Se ha analizado la repercusion que en la deteccion de anomalias tiene el tipo de sensor, tanto en su configuracion espectral como en las estrategias de diseno a la hora de registrar la radiacion prodecente de las superficies, siendo los dos tipos principales de sensores estudiados los barredores o escaneres de espejo giratorio (whiskbroom) y los barredores o escaneres de empuje (pushbroom). Se han definido distintos escenarios en la investigacion, lo que ha permitido abarcar una amplia variabilidad de entornos geomorfologicos y de tipos de coberturas, en ambientes mediterraneos, de latitudes medias y tropicales. En resumen, esta Tesis presenta una tecnica de deteccion de anomalias para datos hiperespectrales denominada DAFT en su variante de PP, basada en una reduccion de la dimensionalidad proyectando el fondo en un rango de longitudes de onda del espectro termico distinto de la proyeccion de las anomalias u objetivos sin firma espectral conocida. La metodologia propuesta ha sido probada con imagenes hiperespectrales reales de diferentes sensores y en diferentes escenarios o espacios, por lo tanto de diferente fondo espectral tambien, donde los resultados muestran los beneficios de la aproximacion en la deteccion de una gran variedad de objetos cuyas firmas espectrales tienen suficiente desviacion respecto del fondo. La tecnica resulta ser automatica en el sentido de que no hay necesidad de ajuste de parametros, dando resultados significativos en todos los casos. Incluso los objetos de tamano subpixel, que no pueden distinguirse a simple vista por el ojo humano en la imagen original, pueden ser detectados como anomalias. Ademas, se realiza una comparacion entre el enfoque propuesto, la popular tecnica RX y otros detectores tanto en su modalidad global como local. El metodo propuesto supera a los demas en determinados escenarios, demostrando su capacidad para reducir la proporcion de falsas alarmas. Los resultados del algoritmo automatico DAFT desarrollado, han demostrado la mejora en la definicion cualitativa de las anomalias espectrales que identifican a entidades diferentes en o bajo superficie, reemplazando para ello el modelo clasico de distribucion normal con un metodo robusto que contempla distintas alternativas desde el momento mismo de la adquisicion del dato hiperespectral. Para su consecucion ha sido necesario analizar la relacion entre parametros biofisicos, como la reflectancia y la emisividad de los materiales, y la distribucion espacial de entidades detectadas respecto de su entorno. Por ultimo, el algoritmo DAFT ha sido elegido como el mas adecuado para sensores que adquieren datos en el TIR, ya que presenta el mejor acuerdo con los datos de referencia, demostrando una gran eficacia computacional que facilita su implementacion en un sistema de cartografia que proyecte de forma automatica en un marco geografico de referencia las anomalias detectadas, lo que confirma un significativo avance hacia un sistema en lo que se denomina cartografia en tiempo real. The aim of this Thesis is to develop a specific methodology in order to be applied in automatic detection anomalies processes using hyperspectral data also called hyperspectral scenes, and to improve the classification processes. Several scenarios, areas and their relationship with surfaces and objects have been tested. The spectral characteristics of reflectance parameter and emissivity in the pattern recognition of urban materials in several hyperspectral scenes have also been tested. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS (Airborne Hyperspectral System), HyMAP Imaging Spectrometer, CASI (Compact Airborne Spectrographic Imager), AVIRIS (Airborne Visible Infrared Imaging Spectrometer), HYDICE (Hyperspectral Digital Imagery Collection Experiment) and MASTER (MODIS/ASTER Simulator) have been used in this research. It is assumed that there is not prior knowledge of the targets in anomaly detection. Thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by the image segmentation. Several experiments on different scenarios have been designed, analyzing the behavior of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. Results and their consequences in unsupervised classification processes are discussed. Detection of spectral anomalies aims at extracting automatically pixels that show significant responses in relation of their surroundings. This Thesis deals with the unsupervised technique of target detection, also called anomaly detection. Since this technique assumes no prior knowledge about the target or the statistical characteristics of the data, the only available option is to look for objects that are differentiated from the background. Several methods have been developed in the last decades, allowing a better understanding of the relationships between the image dimensionality and the optimization of search procedures as well as the subpixel differentiation of the spectral mixture and its implications in anomalous responses. In other sense, image spectrometry has proven to be efficient in the characterization of materials, based on statistical methods using a specific reflection and absorption bands. Spectral configurations in the VNIR, SWIR and TIR have been successfully used for mapping materials in different urban scenarios. There has been an increasing interest in the use of high resolution data (both spatial and spectral) to detect small objects and to discriminate surfaces in areas with urban complexity. This has come to be known as target detection which can be either supervised or unsupervised. In supervised target detection, algorithms lean on prior knowledge, such as the spectral signature. The detection process for matching signatures is not straightforward due to the complications of converting data airborne sensor with material spectra in the ground. This could be further complicated by the large number of possible objects of interest, as well as uncertainty as to the reflectance or emissivity of these objects and surfaces. An important objective in this research is to establish relationships that allow linking spectral anomalies with what can be called informational anomalies and, therefore, identify information related to anomalous responses in some places rather than simply spotting differences from the background. The development in recent years of new hyperspectral sensors and techniques, widen the possibilities for applications in remote sensing of the Earth. Remote sensing systems measure and record electromagnetic disturbances that the surveyed objects induce in their surroundings, by means of different sensors mounted on airborne or space platforms. Map updating is important for management and decisions making people, because of the fast changes that usually happen in natural, urban and semi urban areas. It is necessary to optimize the methodology for obtaining the best from remote sensing techniques from hyperspectral data. The first problem with hyperspectral data is to reduce the dimensionality, keeping the maximum amount of information. Hyperspectral sensors augment considerably the amount of information, this allows us to obtain a better precision on the separation of material but at the same time it is necessary to calculate a bigger number of parameters, and the precision lowers with the increase in the number of bands. This is known as the Hughes effects (Bellman, 1957) . Hyperspectral imagery allows us to discriminate between a huge number of different materials however some land and urban covers are made up with similar material and respond similarly which produces confusion in the classification. The training and the algorithm used for mapping are also important for the final result and some properties of thermal spectrum for detecting land cover will be studied. In summary, this Thesis presents a new technique for anomaly detection in hyperspectral data called DAFT, as a PP's variant, based on dimensionality reduction by projecting anomalies or targets with unknown spectral signature to the background, in a range thermal spectrum wavelengths. The proposed methodology has been tested with hyperspectral images from different imaging spectrometers corresponding to several places or scenarios, therefore with different spectral background. The results show the benefits of the approach to the detection of a variety of targets whose spectral signatures have sufficient deviation in relation to the background. DAFT is an automated technique in the sense that there is not necessary to adjust parameters, providing significant results in all cases. Subpixel anomalies which cannot be distinguished by the human eye, on the original image, however can be detected as outliers due to the projection of the VNIR end members with a very strong thermal contrast. Furthermore, a comparison between the proposed approach and the well-known RX detector is performed at both modes, global and local. The proposed method outperforms the existents in particular scenarios, demonstrating its performance to reduce the probability of false alarms. The results of the automatic algorithm DAFT have demonstrated improvement in the qualitative definition of the spectral anomalies by replacing the classical model by the normal distribution with a robust method. For their achievement has been necessary to analyze the relationship between biophysical parameters such as reflectance and emissivity, and the spatial distribution of detected entities with respect to their environment, as for example some buried or semi-buried materials, or building covers of asbestos, cellular polycarbonate-PVC or metal composites. Finally, the DAFT method has been chosen as the most suitable for anomaly detection using imaging spectrometers that acquire them in the thermal infrared spectrum, since it presents the best results in comparison with the reference data, demonstrating great computational efficiency that facilitates its implementation in a mapping system towards, what is called, Real-Time Mapping.

Risk-based methodology for parameter calibration of a reservoir flood control model

Flash floods are of major relevance in natural disaster management in the Mediterranean region. In many cases, the damaging effects of flash floods can be mitigated by adequate management of flood control reservoirs. This requires the development of suitable models for optimal operation of reservoirs. A probabilistic methodology for calibrating the parameters of a reservoir flood control model (RFCM) that takes into account the stochastic variability of flood events is presented. This study addresses the crucial problem of operating reservoirs during flood events, considering downstream river damages and dam failure risk as conflicting operation criteria. These two criteria are aggregated into a single objective of total expected damages from both the maximum released flows and stored volumes (overall risk index). For each selected parameter set the RFCM is run under a wide range of hydrologic loads (determined through Monte Carlo simulation). The optimal parameter set is obtained through the overall risk index (balanced solution) and then compared with other solutions of the Pareto front. The proposed methodology is implemented at three different reservoirs in the southeast of Spain. The results obtained show that the balanced solution offers a good compromise between the two main objectives of reservoir flood control management

Mechanical separation of the complementary strands of DNA

We describe the mechanical separation of the two complementary strands of a single molecule of bacteriophage λ DNA. The 3′ and 5′ extremities on one end of the molecule are pulled progressively apart, and this leads to the opening of the double helix. The typical forces along the opening are in the range of 10–15 pN. The separation force signal is shown to be related to the local GC vs. AT content along the molecule. Variations of this content on a typical scale of 100–500 bases are presently detected.