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.

Space-time Measurements of Oceanic Sea States

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. We present an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea and near the southern seashore of the Crimean peninsula, in the Black Sea. We use classical epipolar techniques to reconstruct the sea surface from the stereo pairs sequentially in time, viz. a sequence of spatial snapshots. We also present a variational approach that exploits the entire data image set providing a global space–time imaging of the sea surface, viz. simultaneous reconstruction of several spatial snapshots of the surface in order to guarantee continuity of the sea surface both in space and time. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directional spectra and wave statistics at a point in time that agrees well with probabilistic models. In particular, WASS stereo imaging is able to capture typical features of the wave surface, especially the crest-to-trough asymmetry due to second order nonlinearities, and the observed shape of large waves are fairly described by theoretical models based on the theory of quasi-determinism (Boccotti, 2000). Further, we investigate space–time extremes of the observed stationary sea states, viz. the largest surface wave heights expected over a given area during the sea state duration. The WASS analysis provides the first experimental proof that a space–time extreme is generally larger than that observed in time via point measurements, in agreement with the predictions based on stochastic theories for global maxima of Gaussian fields.

Inspección técnica de la presa Santa Marta, mediante medidas subacuáticas de potencial espontáneo

Las filtraciones de agua, con la consecuente erosión interna en presas de materiales sueltos, es una de las causas principales de fallos y accidentes. Las consecuencias del fallo de estas estructuras, pueden ser, pérdidas tanto económicas como de vidas humanas. Por lo cual en este proyecto se describe la aplicación de un método de prospección geofísica no invasiva, medidas de potencial espontáneo, para detectar posibles filtraciones de agua en el cuerpo de la presa. El flujo de agua a través de un material poroso y permeable crea un campo de potencial eléctrico de una magnitud de decenas o centenas de milivoltios, el cual puede ser medido y así detectar infiltraciones de agua en presas de materiales sueltos. Se ha aplicado esta técnica en la Presa Santa Marta, y mediante una interpretación cualitativa de los datos medidos, tomados en la cara aguas arriba de la presa (medidas subacuáticas), se logró identificar un flujo de agua vertical y otro subhorizontal, que estaban ingresando en el cuerpo de la presa, los cuales estaban causando erosión interna y la formación de una tubificación. ABSTRACT Water leakages and internal erosion in embankment dams is one of the main causes of failures and accidents. The consequences of the failure of these structures may cause losses both, economical and of human lives. Therefore, this project describes the application of a noninvasive geophysical prospecting method, self potential measurements, to detect water leakages in the body of the dam. Water flow through a porous and pervious medium creates an electric potential field with a magnitude of tens or hundreds of milivolts, which can be measured and thus detect water leakage in embankment dams. This technique has been applied to the Santa Marta dam, and through a qualitative self potential data interpretation, of the measurements obtained in an upstream direction (underwater measurements), a vertical and sub horizontal water flows entering in the body dam were identified, which were causing internal erosion and developing a piping

Looking for a more comprehensive approach

The necessity/convenience for improving accuracy in determining the flood frequency is widely accepted further than among hydrologists, and is increasingly deepened in relationship with the statement of different thresholds related to the respective management systems. And both Scientific and Management Communities fully accept the necessity of living with determined levels of flood risk. Most of the approaches for “Advancing Methods” improving concentrate on the statistical ways, even since Climate in fact is not a Stationary process. The question is here reflected since the SMARTeST research and final highlights, policy and recommendations. The paper looks at a better agreement between Hydrology and the whole Climate as the result of the Global Thermal Machine and takes mainly into account a historical approach, trying to show the necessity of a wider collection and analysis of climate data for statistical approaches.

Looking for a more comprehensive approach

The necessity/convenience for improving accuracy in determining the flood frequency is widely accepted further than among hydrologists, and is increasingly deepened in relationship with the statement of different thresholds related to the respective management systems. And both Scientific and Management Communities fully accept the necessity of living with determined levels of flood risk. Most of the approaches for “Advancing Methods” improving concentrate on the statistical ways, even since Climate in fact is not a Stationary process. The question is here reflected since the SMARTeST research and final highlights, policy and recommendations. The paper looks at a better agreement between Hydrology and the whole Climate as the result of the Global Thermal Machine and takes mainly into account a historical approach, trying to show the necessity of a wider collection and analysis of climate data for statistical approaches.

Offshore stereo measurements of gravity waves

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. To prove this, we consider an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea. In particular, we deployed WASS at the oceanographic platform Acqua Alta, off the Venice coast, Italy. Three experimental studies were performed, and the overlapping field of view of the acquired stereo images covered an area of approximately 1100 m2. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directional spectra and wave statistics that agree well with theoretical models. From the observed wavenumber-frequency spectrum one can also predict the vertical profile of the current flow underneath the wave surface. Finally, future improvements of WASS and applications are discussed.

Análisis fundamental aplicado a la industria minera : un estudio multivariante

Este trabajo tiene por objeto aplicar los principios del Value Investing a veinticuatro empresas del sector minero y definir las claves para extrapolar, en base a un análisis fundamental, una calificación para cada una de las empresas. Con este fin, se ha realizado un estudio estadístico multivariante para comparar las correlaciones existentes entre cada ratio fundamental y su evolución en bolsa a uno, tres y cinco años vista. Para procesar los datos se han utilizado los programas MATLAB y EXCEL. Sobre ellos se ha planteado una Matriz de Correlaciones de Pearson y un estudio de dispersión por cruce de pares. El análisis demostró que es posible aplicar la metodología del Value Investing a empresas del sector minero con resultados positivos aunque, el ajuste de las correlaciones, sugiere utilizar series temporales más largas y un mayor número de empresas para ganar fiabilidad en el contraste de estas hipótesis. De los estudios realizados, se deduce que unos buenos fundamentales influyen, de manera notable, a la revalorización bursátil a 3 y 5 años destacando, además, que el ajuste es mejor cuanto mayor sea este tiempo. Abstract This study aims to apply the principles of Value Investing to twenty four mining companies and, based on this fundamental study, develop a rating to classify those companies. For this purpose, we have performed a multivariate statistical study to compare the correlations between each fundamental ratio and its stock revalorization for one, three and five years. MATLAB and EXCEL have been used to process data. The statistical methods used are Pearson Matrix of Correlations and a Cross Pairs Scattering Study. The analysis showed that it is possible to apply the methodology of Value Investing to mining companies, although, the adjustment of correlations suggests using longer time series and a larger amount of companies to test these hypothesis. From the studies performed, it follows that good fundamentals significantly influence the stock market value at 3 and 5 years, noting that, the larger the period under study, the better the fit.

Modelización del efecto de la pendiente de la vía sobre el consumo de vehículos turismo

El presente trabajo tiene por objetivo generar una metodología validada que permita predecir el consumo de vehículos turismo circulando en cualquier tramo de vía a partir del perfil orográfico y del diagrama velocidad-tiempo. Para la generación de la metodología, se ha realizado un modelo de simulación con el programa ADVISOR que permite calcular el consumo de combustible para un determinado recorrido en el que se tiene en cuenta el perfil orográfico. Este modelo fue validado con datos reales medidos con equipos on-board y se usó para calcular el consumo de combustible diferencial debido al efecto de la pendiente de la vía, al poderse simular con y sin pendiente. Se realizaron múltiples simulaciones de recorridos con velocidad máxima variable con el fin de obtener un número significativo de datos. Con los resultados de las diferentes simulaciones, se realizó un estudio estadístico, para determinar las variables influyentes y se generó una función estadística (Ecuación de Consumo Estimado – ECE) que permite calcular el consumo de combustible debido a la pendiente de la vía, conociendo el consumo del vehículo en carretera llana (sin pendiente). Esta función estadística generada (ECE), se validó con datos reales medidos en tráfico real. Con el fin de darle generalidad y aplicabilidad a la función generada, y teniendo en cuenta que el consumo de combustible en carretera llana no está siempre disponible, se ha calculado el consumo de combustible sin pendiente utilizando la metodología Copert 4, metodología oficial desarrollada por la Agencia de Medio Ambiente de Europa (EEA) para la estimación de emisiones y consumo de combustible que está basada en datos experimentales pero que no tiene en cuenta la pendiente de la vía. La Ecuación de Consumo Estimado (ECE) aplicada a los consumos calculados por la metodología Copert 4, se valida también usando datos reales medidos en tráfico real y se comprueba que esta función se ajusta considerablemente bien a la realidad, con un error en el consumo acumulado frente al del ensayo real de un 1% y una correlación con el consumo instantáneo del ensayo real de 0,93. Se concluye, que la Función de Consumo Estimado (ECE), permite predecir el efecto de la pendiente sobre el consumo de combustible de un vehículo turismo en tráfico real con un error menor del 1%. Abstract This projects aims to develop a validated methodology that enables to predict cars consumption while circulating at any kind of road section based on its orographic outline and the speed-time diagram. In order to develop this methodology, a simulation model has been performed with the programme ADVISOR, that allows fuel consumption calculation for an specific route in which the orographic outline is considered. This model was validated by real data measured with an on-board equipment and it was used to calculate the differential fuel consumption caused by the effect of the slope on the road, as it was possible to simulate with or without slope. Many simulations were run with routes with variable maximum speed, aiming to obtain a significant amount of data. An statistical study was carried out with the results of those simulations with the purpose to determine the influential variables and an statistical function ( Estimated Consumption Equation – ECE) that enables fuel consumption calculation due to the road’s slope when the consumption of a vehicle on horizontal road (without any slope) is known. This statistical function (ECE) was validated by real data measured in real traffic conditions. With the purpose to generalise the function and increase its applicability, considering that the consumption of a vehicle on horizontal road is not always available, the nonslope fuel consumption has been calculated through Copert 4 methodology, which is the official methodology developed by the European Environmental Agency (EEA) for emissions and fuel consumption calculation based on experimental data, but without taking into consideration the road’s slope. The Estimated Consumption Equation (ECE) applied to the consumption calculated through Copert 4 methodology is also validated using real data measured in real traffic conditions. It was verified that this function considerably adjusts to reality, with an error on the accumulated consumption compared to the real test of 1% and a correlation with the real test immediate fuel consumption of 0,93. It is concluded that the Estimated Consumption Equation (ECE) enables to predict the effect of the slope on the fuel consumption of a car in real traffic conditions with an error less than 1%.

Aplicación de herramientas gis para la cartografía y correlación de datos de ensayos no destructivos en el diagnóstico de edificios históricos

La presente tesis propone un nuevo método de cartografía de ensayos no destructivos en edificios históricos mediante el uso de técnicas basadas en SIG. Primeramente, se define el método por el cual es posible elaborar y convertir una cartografía 3D basada en nubes de puntos de un elemento arquitectónico obtenida mediante fotogrametría, en cartografía raster y vectorial, legible por los sistemas SIG mediante un sistema de coordenadas particular que referencian cada punto de la nube obtenida por fotogrametría. A esta cartografía inicial la denominaremos cartografía base. Después, se define el método por el cual los puntos donde se realiza un ensayo NDT se referencian al sistema de coordenadas del plano base, lo que permite la generación de cartografías de los ensayos referenciadas y la posibilidad de obtener sobre un mismo plano base diferentes datos de múltiples ensayos. Estas nuevas cartografías las denominaremos cartografías de datos, y se demostrará la utilidad de las mismas en el estudio del deterioro y la humedad. Se incluirá el factor tiempo en las cartografías, y se mostrará cómo este nuevo hecho posibilita el trabajo interdisciplinar en la elaboración del diagnóstico. Finalmente, se generarán nuevas cartografías inéditas hasta entonces consistentes en la combinación de diferentes cartografías de datos con la misma planimetría base. Estas nuevas cartografías, darán pie a la obtención de lo que se ha definido como mapas de isograma de humedad, mapa de isograma de salinidad, factor de humedad, factor de evaporación, factor de salinidad y factor de degradación del material. Mediante este sistema se facilitará una mejor visión del conjunto de los datos obtenidos en el estudio del edificio histórico, lo que favorecerá la correcta y rigurosa interpretación de los datos para su posterior restauración. ABSTRACT This research work proposes a new mapping method of non-destructive testing in historical buildings, by using techniques based on GIS. First of all, the method that makes it possible to produce and convert a 3D map based on cloud points from an architectural element obtained by photogrammetry, are defined, as raster and vector, legible by GIS mapping systems using a particular coordinate system that will refer each cloud point obtained by photogrammetry. This initial mapping will be named base planimetry. Afterwards, the method by which the points where the NDT test is performed are referenced to the coordinate system of the base plane , which allows the generation of maps of the referenced tests and the possibility of obtaining different data from multiple tests on the same base plane. These new maps will be named mapping data and their usefulness will be demonstrated in the deterioration and moisture study. The time factor in maps will be included, and how this new fact will enable the interdisciplinary work in the elaboration of the diagnosis will be proved. Finally, new maps (unpublished until now) will be generated by combining different mapping from the same planimetry data base. These new maps will enable us to obtain what have been called isograma moisture maps, isograma salinity- maps, humidity factor, evaporation factor, salinity factor and the material degradation factor. This system will provide a better vision of all data obtained in the study of historical buildings , and will ease the proper and rigorous data interpretation for its subsequent restoration.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.