Supervised classification for a family of Gaussian functional models

In the framework of supervised classification (discrimination) for functional data, it is shown that the optimal classification rule can be explicitly obtained for a class of Gaussian processes with "triangular" covariance functions. This explicit knowledge has two practical consequences. First, the consistency of the well-known nearest neighbors classifier (which is not guaranteed in the problems with functional data) is established for the indicated class of processes. Second, and more important, parametric and nonparametric plug-in classifiers can be obtained by estimating the unknown elements in the optimal rule. The performance of these new plug-in classifiers is checked, with positive results, through a simulation study and a real data example.Comment: 30 pages, 6 figures, 2 table

Potentiostatic infrared titration of 11-Mercaptoundecanoic acid monolayers

Acknowledgment This work was supported by the Spanish DGICYT under grant CTQ2008-00371 and by the Junta de Andalucía under grant P07-FQM-02492.Peer reviewedPostprin

Empleo de técnicas de teledetección con diferentes niveles de resolución para la mejora de la gestión del riego

Currently there is a growing interest in improving water management in Mediterranean agriculture due to the foreseeable results of climate change and to the competition with other sectors such as the environmental. For this reason different methodologies have been evaluated in this thesis to increase water use efficiency in Andalusian agriculture by means of the improvement in the estimation of crop irrigation water requirements, using different remote sensing techniques and spatial analysis. In this work the two main parameters involved in crop evapotranspiration determination were addressed: reference evapotranspiration (Chapters 1 and 2) and crop coefficient (Chapters 3 and 4). More specifically, in Chapter 1, different interpolation methods were applied to meteorological data and results were assessed in order to determine which of them provided the most accurate reference evapotranspiration (ETo) estimates. The ETo estimates obtained from the interpolation methods were compared with the ETo values provided by the Land Surface Analysis Satellite Application Facility (LSA SAF), based on the daily solar radiation derived from Meteosat Second Generation (MSG) and air temperature at 2 m forecasts provided by European Center for Medium-range Weather Forecasts (ECMWF). Additionally, new techniques were proposed for ETo estimation improvement in areas without a nearby weather station, which were based on the analysis of the spatial location of the weather stations and the temporal evolution of ETo. Also related to ETo estimation and its practical application for irrigation management, Chapter 2 presents an innovative methodology for performing irrigation schedules easily usable by farmers and technicians, using weather forecasts provided by the National Meteorological Agency (AEMET) and by ECMWF for ETo estimation. In addition, the effect that the different methods for ETo estimation has on the crop water requirements and on the crop yield simulated using the AquaCrop model was also assessed. Once accurate ETo values were determined by means of the methodologies developed in Chapters 1 and 2, it is necessary to determine crop coefficient values for the correct estimation of the crop water demands. This issue was addressed in Chapter 3, where different atmospheric corrections were applied to Landsat 7 satellite images, with the aim of eliminating the effect that the atmosphere causes during the image acquisition process. In this way, it was possible to obtain much more accurate surface temperature measurements, in order to assess the effect of the different atmospheric corrections on the determination of the olive crop coefficient. However, the effect that atmosphere has on the satellite images acquisition process analyzed in Chapter 3 is not the only issue to be taken into account when using remote sensing techniques. Thus, spatial resolution is also a key factor for the application of these techniques in irrigation management. Therefore, in Chapter 4 the influence of spatial resolution on the different energy balance components estimated by the METRIC energy balance model was evaluated, paying special attention to crop evapotranspiration.Actualmente existe un interés creciente por la mejora de la gestión del agua en la agricultura mediterránea debido a las previsibles consecuencias del cambio climático y a la competencia con otros sectores como el medioambiental. Por este motivo en esta tesis se han evaluado diferentes metodologías para incrementar la eficiencia en el uso del agua en la agricultura andaluza por medio de la mejora en la estimación de las necesidades de riego de los cultivos, empleando diferentes técnicas de teledetección y análisis espacial. De este modo, en este trabajo se abordó el estudio de los dos principales parámetros involucrados en la determinación de la evapotranspiración de cultivo: la evapotranspiración de referencia (Capítulos 1 y 2) y el coeficiente de cultivo (Capítulos 3 y 4). Más específicamente, en el Capítulo 1 se evaluaron diferentes métodos de interpolación de información obtenida desde estaciones meteorológicas para determinar cuál de ellos proporcionaba unas estimaciones de evapotranspiración de referencia (ETo) más precisas. Las estimaciones de ETo obtenidas con dichos métodos de interpolación se compararon con los valores de ETo proporcionados por Land Surface Analysis Satellite Application Facility (LSA SAF), a partir de la radiación solar diaria derivada de Meteosat Second Generation (MSG) y de las prediciones de la temperatura del aire a 2 m proporcionadas por European Centre for Medium-range Weather Forecasts (ECMWF). Adicionalmente, se propusieron técnicas para la mejora en la estimación de la ETo en zonas sin estación meteorológica cercana, basadas en el análisis de localización espacial de las estaciones meteorológicas y en la evolución temporal de ETo en las mismas. Relacionado también con la estimación de la ETo y su aplicación práctica para la gestión del riego, en el Capítulo 2 se presenta una innovadora metodología para la realización de calendarios de riego fácilmente utilizable por agricultores y técnicos, utilizando predicciones meteorológicas para la estimación de ETo proporcionadas por la Agencia Estatal de Meteorología (AEMET) y por el ECMWF. Además, se analizó el efecto de la consideración de diferentes métodos para la estimación de la ETo sobre las necesidades de riego y sobre el rendimiento del cultivo simulado utilizando el modelo AquaCrop. Una vez determinados valores fiables de ETo mediante las metodologías desarrolladas en los Capítulos 1 y 2, para la correcta estimación de las necesidades de riego de los cultivos, es preciso obtener valores de coeficiente de cultivo ajustados al estado de los mismos. Esta cuestión se trató en el Capítulo 3, donde se aplicaron diferentes correcciones atmosféricas sobre imágenes del satélite Landsat 7, con el objetivo de eliminar el efecto que la atmósfera causa durante el proceso de adquisición de las mismas. De este modo, se consiguió obtener unas medidas de temperatura superficial mucho más precisas, para finalmente conocer el efecto de las diferentes correcciones atmosféricas sobre la determinación del coeficiente de cultivo del olivar. Sin embargo, el efecto de la atmósfera en el proceso de adquisición de imágenes de satélite analizado en el Capítulo 3 no es el único aspecto a tener en cuenta al emplear técnicas de teledetección. Así, la resolución espacial también es un factor clave para la correcta aplicación de estas técnicas en la gestión del riego. Es por ello que en el Capítulo 4 se evaluó la influencia de la resolución espacial sobre los diferentes componentes de balance de energía estimados mediante el modelo de balance de energía METRIC, prestando especial atención a la evapotranspiración del cultivo

The DDG^G-classifier in the functional setting

The Maximum Depth was the first attempt to use data depths instead of multivariate raw data to construct a classification rule. Recently, the DD-classifier has solved several serious limitations of the Maximum Depth classifier but some issues still remain. This paper is devoted to extending the DD-classifier in the following ways: first, to surpass the limitation of the DD-classifier when more than two groups are involved. Second to apply regular classification methods (like $k$NN, linear or quadratic classifiers, recursive partitioning,...) to DD-plots to obtain useful insights through the diagnostics of these methods. And third, to integrate different sources of information (data depths or multivariate functional data) in a unified way in the classification procedure. Besides, as the DD-classifier trick is especially useful in the functional framework, an enhanced revision of several functional data depths is done in the paper. A simulation study and applications to some classical real datasets are also provided showing the power of the new proposal.Comment: 29 pages, 6 figures, 6 tables, Supplemental R Code and Dat

The Secretary of State for Information: origins, aims and structure (1978-1982)

La transición democrática se inició con un aparato institucional en materia de comunicación propio del régimen franquista. Era urgente su reforma, pero ésta no dio sus primeros pasos hasta la creación de la Secretaría de Estado para la Información, en septiembre de 1978. De esta forma, el Gobierno de UCD creó una estructura informativa que, mediante constantes reformas, intentó responder a la relación nueva y cada vez más compleja entre el poder institucional y los medios de comunicación, desarrollando un comportamiento propio del Estado democrático.The Spanish Transition to Democracy began with an institutional organism on communication typical of Franco's regime. Its reform was urgent, but it did not take its first steps until the Secretary of State for Information was created, on September 1978. Therefore, an informative structure was founded by the Government of UCD that, throughout continuous reforms, attempted to react to the new and frequently complex relationship between the power and the Mass Media, carrying out an equivalent behavior of others democratic Nation

Applying feature reduction analysis to a PPRLM-multiple Gaussian language identification system

This paper presents the application of a feature selection technique such as LDA to a language identification (LID) system. The baseline system consists of a PPRLM module followed by a multiple-Gaussian classifier. This classifier makes use of acoustic scores and duration features of each input utterance. We applied a dimension reduction of the feature space in order to achieve a faster and easier-trainable system. We imputed missing values of our vectors before projecting them on the new space. Our experiments show a very low performance reduction due to the dimension reduction approach. Using a single dimension projection the error rates we have obtained are about 8.73% taking into account the 22 most significant features