Tomas Lopez's Geographic Atlas of Spain (1787) and its Hydrographic Network: GIS analysis of the Reyno de Jaen

Knowledge of the development of hydrographic networks can be useful for a number of research works in hydraulic engineering. We thus, intend to analyse the cartography regarding the first work that systematically encompasses the entire hydrographic network: Tomas Lopez’s Geographic Atlas of Spain (1787). In order to achieve this goal, we will first analyze –by way of the Geographic Information System (GIS) – both the present and referred historical cartographies. In comparing them, we will use the then-existing population centres that correspond to modern ones. The aim is to compare the following research variables in the hydrographic network: former toponyms, length of riverbeds and distance to population centres. The results of this study will show the variation in the riverbeds and the probable change in their denomination.

Hybridizing concentrated solar power (CSP) with biogas and biomethane as an alternative to natural gas: Analysis of environmental performance using LCA

Concentrating Solar Power (CSP) plants typically incorporate one or various auxiliary boilers operating in parallel to the solar field to facilitate start up operations, provide system stability, avoid freezing of heat transfer fluid (HTF) and increase generation capacity. The environmental performance of these plants is highly influenced by the energy input and the type of auxiliary fuel, which in most cases is natural gas (NG). Replacing the NG with biogas or biomethane (BM) in commercial CSP installations is being considered as a means to produce electricity that is fully renewable and free from fossil inputs. Despite their renewable nature, the use of these biofuels also generates environmental impacts that need to be adequately identified and quantified. This paper investigates the environmental performance of a commercial wet-cooled parabolic trough 50 MWe CSP plant in Spain operating according to two strategies: solar-only, with minimum technically viable energy non-solar contribution; and hybrid operation, where 12 % of the electricity derives from auxiliary fuels (as permitted by Spanish legislation). The analysis was based on standard Life Cycle Assessment (LCA) methodology (ISO 14040-14040). The technical viability and the environmental profile of operating the CSP plant with different auxiliary fuels was evaluated, including: NG; biogas from an adjacent plant; and BM withdrawn from the gas network. The effect of using different substrates (biowaste, sewage sludge, grass and a mix of biowaste with animal manure) for the production of the biofuels was also investigated. The results showed that NG is responsible for most of the environmental damage associated with the operation of the plant in hybrid mode. Replacing NG with biogas resulted in a significant improvement of the environmental performance of the installation, primarily due to reduced impact in the following categories: natural land transformation, depletion of fossil resources, and climate change. However, despite the renewable nature of the biofuels, other environmental categories like human toxicity, eutrophication, acidification and marine ecotoxicity scored higher when using biogas and BM.

Promoting mixed stands through conversion treatments. Effect of holm oak coppices thinning on black pine regeneration

The effects of conversion treatments, depending on ecological factors and silvicultural parameters (thinning intensity, thinning type and rotation, among others) have been studied during the last fifteen years in an experimental trial in Central Spain. The general climate is continental Mediterranean; soils are low depth and limy; vegetation is an homogeneous dense coppices of Quercus ilex with isolated Pinus nigra trees. The experimental design (three locations) includes different thinning intensities (from 0 to 100% of extracted basal area). Inventories have been carried out in 1994 and 2010; thinning treatments were done in 1995 and 2011. Analysis of the effects of the conversion treatment show the increment of diameter and height growth rates, the canopy recovery and the stand resprouting, finding differences in these effects between thinning treatments. Besides the induced changes at holm oak stand, the application of conversion treatment clearly changed the woodland dynamics. Fifteen years after the thinnings, floristic composition varied and an abundant pine regeneration was installed in the woodland. In this work we describe the changes between inventories in tree species composition and diameter distribution, specially in the case of black pine. The conversion treatment caused changes in forest dynamics in the short term, increasing biodiversity and diversifying the forest structure. The fast installation of Pinus regeneration suggests the potential of the zone for the establishment of multipurpose mixed Quercus-Pinus stands in wide areas where Quercus species were favoured by human populations for firewood production. Conversion treatment of coppices, with the creation of mixed stands, constitutes a good management alternative for extensive areas and an interesting technique to adaptation to global change.

Small-scale variation of vegetation in a mixed forest understorey is partly controlled by the effect of overstory composition on litter accumulation

We investigated how richness and composition of vascular plant species in the understory of a mixed hardwood forest stand varied with respect to the abundance and composition of the overstory. The stand is in central Spain and represents the southernmost range of distribution of several tree and herbaceous species in Europe. Understory species were identified in 46 quadrats (0.25 m2) where variables litter depth and light availability were measured. In addition, we estimated tree density, basal area, and percent basal area by tree species within 6-m-radius areas around each plot. Species richness and composition were studied using path analysis and scale-dependent geostatistical methods, respectively. We found that the relative abundance of certain trees species in the overstory was more important than total overstory abundance in explaining understory species richness. Richness decreased as soil litter depth increased, and soil litter increased as the relative proportion of Fagus sylvatica in the overstory increased, which accounted for a negative, indirect effect of Fagus sylvatica on richness. Regarding understory species composition, we found that some species distributed preferentially below certain tree species. For example, Melica uniflora was most frequent below Fagus sylvatica and Quercus petraea while the increasing proportion of Q. pyrenaica in the overstory favored the presence of Cruciata glabra, Arenaria montana, Prunus avium, Conopodium bourgaei, Holcus mollis, Stellaria media and Galium aparine in the understory. Overall, these results emphasize the importance of individual tree species in controlling the assemblage and richness of understory species in mixed stands. We conclude that soil litter accumulation is one way through which overstory composition shapes the understory community.

Natural regeneration in mediterranean mixed forests: effect of environmental conditions on the physiological optimum niche of the species = Regeneración natural en masas mixtas mediterráneas: efecto de las condiciones ambientales en el nicho óptimo fisiológico de las especies

Los montes Mediterráneos han experimentado múltiples cambios en las últimas décadas (tanto en clima como en usos), lo que ha conducido a variaciones en la distribución de especies. El aumento previsto de las temperaturas medias junto con la mayor variabilidad intra e inter anual en cuanto a la ocurrencia de eventos extremos o disturbios naturales (como periodos prolongados de sequía, olas de frío o calor, incendios forestales o vendavales) pueden dañar significativamente al regenerado, llevándolo hasta la muerte, y jugando un papel decisivo en la composición de especies y en la dinámica del monte. La amplitud ecológica de muchas especies forestales puede verse afectada, de forma que se esperan cambios en sus nichos actuales de regeneración. Sin embargo, la migración latitudinal de las especies en busca de mejores condiciones, podría ser una explicación demasiado simplista de un proceso mucho más complejo de interacción entre la temperatura y la precipitación, que afectaría a cada especie de un modo distinto. En este sentido tanto la capacidad de adaptación al estrés ambiental de una determinada especie, así como su habilidad para competir por los recursos limitados, podría significar variaciones dentro de una comunidad. Las características fisiológicas y morfológicas propias de cada especie se encuentran fuertemente relacionadas con el lugar donde cada una puede surgir, qué especies pueden convivir y como éstas responden a las condiciones ambientales. En este sentido, el conocimiento sobre las distintas respuestas ecofisiológicas observadas ante cambios ambientales puede ser fundamentales para la predicción de variaciones en la distribución de especies, composición de la comunidad y productividad del monte ante el cambio global. En esta tesis investigamos el grado de tolerancia y sensibilidad que cada una de las tres especies de estudio, coexistentes en el interior peninsular ibérico (Pinus pinea, Quercus ilex y Juniperus oxycedrus), muestra ante los factores abióticos de estrés típicos de la región Mediterránea. Nuestro trabajo se ha basado en la definición del nicho óptimo fisiológico para el regenerado de cada especie a través de la investigación en profundidad del efecto de la sequía, la temperatura y el ambiente lumínico. Para ello, hemos desarrollado un modelo de predicción de la tasa de asimilación de carbono que nos ha permitido identificar las condiciones óptimas ambientales donde el regenerado de cada especie podría establecerse con mayor facilidad. En apoyo a este trabajo y con la idea de estudiar el efecto de la sequía a nivel de toda la planta hemos desarrollado un experimento paralelo en invernadero. Aquí se han aplicado dos regímenes hídricos para estudiar las características fisiológicas y morfológicas de cada especie, sobre todo a nivel de raíz y crecimiento del tallo, y relacionarlas con las diferentes estrategias en el uso del agua de las especies. Por último, hemos estudiado los patrones de aclimatación y desaclimatación al frio de cada especie, identificando los periodos de sensibilidad a heladas, así como cuellos de botella donde la competencia entre especies podría surgir. A pesar de que el pino piñonero ha sido la especie objeto de la gestión de estas masas durante siglos, actualmente se encuentra en la posición más desfavorable para combatir el cambio global, presentado el nicho fisiológico más estrecho de las tres especies. La encina sin embargo, ha resultado ser la especie mejor cualificada para afrontar este cambio, seguida muy de cerca por el enebro. Nuestros resultados sugieren una posible expansión en el rango de distribución de la encina, un aumento en la presencia del enebro y una disminución progresiva del pino piñonero a medio plazo en estas masas. ABSTRACT Mediterranean forests have undergone multiple changes over the last decades (in both climate and land use), which have lead to variations in the distribution of species. The expected increase in mean annual temperature together with the greater inter and intra-annual variability in extreme events and disturbances occurrence (such as prolonged drought periods, cold or heat waves, wildfires or strong winds) can significantly damage natural regeneration, up to causing death, playing a decisive role on species composition and forest dynamics. The ecological amplitude for adaptation of many species can be affected in such a way that changes in the current regeneration niches of many species are expected. However, the forecasted poleward migration of species seeking better conditions could be an oversimplification of what is a more complex phenomenon of interactions among temperature and precipitation, that would affect different species in different ways. In this regard, either the ability to adapt to environmental stresses or to compete for limited resources of a single species in a mixed forest could lead to variations within a community. The ecophysiological and morphological traits specific to each species are strongly related to the place where each species can emerge, which species can coexist, and how they respond to environmental conditions. In this regard, the understanding of the ecophysiological responses observed against changes in environmental conditions can be essential for predicting variations in species distribution, community composition, and forest productivity in the context of global change. In this thesis we investigated the degree of tolerance and sensitivity that each of the three studied species, co-occurring in central of the Iberian Peninsula (Pinus pinea, Quercus ilex and Juniperus oxycedrus), show against the typical abiotic stress factors in the Mediterranean region. Our work is based on the optimal physiological niche for regeneration of each species through in-depth research on the effect of drought, temperature and light environment. For this purpose, we developed a model to predict the carbon assimilation rate which allows us to identify the optimal environmental conditions where regeneration from each species could establish itself more easily. To obtain a better understanding about the effect of low temperature on regeneration, we studied the acclimation and deacclimation patterns to cold of each species, identifying period of frost sensitivity, as well as bottlenecks where competition between species can arise. Finally, to support our results about the effect of water availabilty, we conducted a greenhouse experiment with a view of studying the drought effect at the whole plant level. Here, two watering regimes were applied in order to study the physiological and morphological traits of each species, mainly at the level of the root system and stem growth, and so relate them to the different water use strategies of the species. Despite the fact that stone pine has been the target species for centuries, nowadays this species is in the most unfavorable position to cope with climate change. Holm oak, however, resulted the species that is best adapted to tolerate the predicted changes, followed closely by prickly juniper. Our results suggest a feasible expansion of the distribution range in holm oak, an increase in the prickly juniper presence and a progressive decreasing of stone pine presence in the medium term in these stone pine-holm oak-prickly juniper mixed forests.

Testing a Cloud Provider Network for Hybrid P2P and Cloud Streaming Architectures

The number of online real-time streaming services deployed over network topologies like P2P or centralized ones has remarkably increased in the recent years. This has revealed the lack of networks that are well prepared to respond to this kind of traffic. A hybrid distribution network can be an efficient solution for real-time streaming services. This paper contains the experimental results of streaming distribution in a hybrid architecture that consist of mixed connections among P2P and Cloud nodes that can interoperate together. We have chosen to represent the P2P nodes as Planet Lab machines over the world and the cloud nodes using a Cloud provider's network. First we present an experimental validation of the Cloud infrastructure's ability to distribute streaming sessions with respect to some key streaming QoS parameters: jitter, throughput and packet losses. Next we show the results obtained from different test scenarios, when a hybrid distribution network is used. The scenarios measure the improvement of the multimedia QoS parameters, when nodes in the streaming distribution network (located in different continents) are gradually moved into the Cloud provider infrastructure. The overall conclusion is that the QoS of a streaming service can be efficiently improved, unlike in traditional P2P systems and CDN, by deploying a hybrid streaming architecture. This enhancement can be obtained by strategic placing of certain distribution network nodes into the Cloud provider infrastructure, taking advantage of the reduced packet loss and low latency that exists among its datacenters.

Designing Rapid Transit Network Design with Alternative routes

The aim of this paper is to propose a model for the design of a robust rapid transit network. In this paper, a network is said to be robust when the effect of disruption on total trip coverage is minimized. The proposed model is constrained by three different kinds of flow conditions. These constraints will yield a network that provides several alternative routes for given origin–destination pairs, therefore increasing robustness. The paper includes computational experiments which show how the introduction of robustness influences network design

An empirical mixed model to quantify climate influence on the growth of Pinus halepensis Mill. stands in South-Eastern Spain

The influence of climate on forest stand composition, development and growth is undeniable. Many studies have tried to quantify the effect of climatic variables on forest growth and yield. These works become especially important because there is a need to predict the effects of climate change on the development of forest ecosystems. One of the ways of facing this problem is the inclusion of climatic variables into the classic empirical growth models. The work has a double objective: (i) to identify the indicators which best describe the effect of climate on Pinus halepensis growth and (ii) to quantify such effect in several scenarios of rainfall decrease which are likely to occur in the Mediterranean area. A growth mixed model for P. halepensis including climatic variables is presented in this work. Growth estimates are based on data from the Spanish National Forest Inventory (SNFI). The best results are obtained for the indices including rainfall, or rainfall and temperature together, with annual precipitation, precipitation effectiveness, Emberger?s index or free bioclimatic intensity standing out among them. The final model includes Emberger?s index, free bioclimatic intensity and interactions between competition and climate indices. The results obtained show that a rainfall decrease about 5% leads to a decrease in volume growth of 5.5?7.5% depending on site quality.

Mixing effect on volume growth of Fagus sylvatica and Pinus sylvestris is modulated by stand density

Despite the increasing relevance of mixed stands due to their potential benefits; little information is available with regard to the effect of mixtures on yield in forest systems. Hence, it is necessary to study inter-specific relationships, and the resulting yield in mixed stands, which may vary with stand development, site or stand density, etc. In Spain, the province of Navarra is considered one of the biodiversity reservoirs; however, mixed forests occupy only a small area, probably as a consequence of management plans, in which there is an excessive focus on the productivity aspect, favoring the presence of pure stands of the most marketable species. The aim of this paper is to study how growth efficiencies of beech (Fagus sylvatica) and pine (Pinus sylvestris) are modified by the admixture of the other species and to determine whether stand density modifies interspecific relationships and to what extent. Two models were fitted from Spanish National Forest Inventory data, for P. sylvestris and F. sylvatica respectively, which relate the growth efficiency of the species, i.e. the volume increment of the species divided by the species proportion by area, with dominant height, quadratic mean diameter, stocking degree, and the species proportions by area of each species. Growth efficiency of pine increased with the admixture of beech, decreasing this positive effect when stocking degree increased. However, the positive effect of pine admixture on beech growth was greater at higher stocking degrees. Growth efficiency of beech was also dependent on stand dominant height, resulting in a net negative mixing effect when stand dominant heights and stocking degrees were simultaneously low. There is a relatively large range of species proportions and stocking degrees which results in transgressive overyielding: higher volume increments in mixed stands than that of the most productive pure pine stands. We concluded that stocking degree is a key factor in between-species interactions, being the effects of mixing not always greater at higher stand densities, but it depends on species composition.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Cattle-powered nodes experience in a heterogeneous network for localization of herds

A heterogeneous network, mainly based on nodes that use harvested energy to self-energize is presented and its use demonstrated. The network, mostly kinetically powered, has been used for the localization of herds in grazing areas under extreme climate conditions. The network consists of secondary and primary nodes. The former, powered by a kinetic generator, take advantage of animal movements to broadcast a unique identifier. The latter are battery-powered and gather secondarynode transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. Because a limited human interaction is desirable, the aim of this network is to reduce the battery count of the system.

Positional Accuracy in Smart-Phones and Its Effect on LBS Applications.

Location-based services (LBS) highly rely on the location of the mobile user in order to provide the service tailored to that location. This location is calculated differently depending on the technology available in the used mobile device. No matter which technology is used, the location will never be calculated 100% correctly; instead there will always be a margin of error generated during the calculation, which is referred to as positional accuracy. This research has reviewed the eight most common positioning technologies available in the major current smart-phones and assessed their positional accuracy with respect to its usage by LBS applications. Given the vast majority of these applications, this research classified them into thirteen categories, and these categories were also classified depending on their level criticality as low, medium, or high critical, and whether they function indoor or outdoor. The accuracies of different positioning technologies are compared to these two criteria. Low critical outdoor and high critical indoor applications were found technologically covered; high and medium critical outdoor ones weren?t fully resolved. Finally three potential solutions are suggested to be implemented in future smartphones to resolve this technological gap: Real-Time Kinematics Global Positioning System (RTK GPS), terrestrial transmitters, and combination of Wireless Sensors Network and Radio Frequency Identification (WSN-RFID).

Finite element simulation of sandwich panels of plasterboard and rock wool under mixed mode fracture

This paper presents the results of research on mixed mode fracture of sandwich panels of plasterboard and rock wool. The experimental data of the performed tests are supplied. The specimens were made from commercial panels. Asymmetrical three-point bending tests were performed on notched specimens. Three sizes of geometrically similar specimens were tested for studying the size effect. The paper also includes the numerical simulation of the experimental results by using an embedded cohesive crack model.The involved parameters for modelling are previously measured by standardised tests.

The counteractive effect of the sensor housing on the unit sensitivity across a series of dynamic temperature profiles.

The advantages of wireless sensing implemented on the cold chain of fresh products are well known. These sensor systems consist of a combination of delicate internal electronic circuitry enclosed in a special housing unit. Manufacturers however are presented with the challenge that the housing required to withstand the harsh environment in which the sensors are being used all too often take from the functionality of the sensor. Therefore the target of this study is to determine the dynamic behavior and the counteractive effects of the sensor housing on temperature recording accuracy in the wireless nodes of Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) semi-passive tags. Two kind of semi-passive Turbo Tags were used (T700 and T702-B), which consisted of sensors with and without a cover, and two kind of WSN nodes, IRIS (sensors Intersema and Sensirion soldered in the motherboard) and NLAZA (Sensirion in a cable and soldered to the motherboard). To recreate the temperature profiles the devices were rotated between a cold room(5 ºC) through a ambient room(23 ºC) to a heated environment (35ºC) and vice versa. Analysis revealed the differences between housing and no housing are 308.22s to 21.99s respectively in the step from 5 to 35 ºC. As is demonstrated in these experiments the influence of the housing significantly hinders sensor accuracy.

Synchronizing sound from different devices over a TCP network

Nowadays, we can send audio on the Internet for multiples uses like telephony, broadcast audio or teleconferencing. The issue comes when you need to synchronize the sound from different sources because the network where we are going to work could lose packets and introduce delay in the delivery. This can also come because the sound cards could be work in different speeds. In this project, we will work with two computers emitting sound (one will simulate the left channel (mono) of a stereo signal, and the other the right channel) and connected with a third computer by a TCP network. The last computer must get the sound from both computers and reproduce it in a speaker properly (without delay). So, basically, the main goal of the project is to synchronize multi-track sound over a network. TCP networks introduce latency into data transfers. Streaming audio suffers from two problems: a delay and an offset between the channels. This project explores the causes of latency, investigates the affect of the inter-channel offset and proposes a solution to synchronize the received channels. In conclusion, a good synchronization of the sound is required in a time when several audio applications are being developed. When two devices are ready to send audio over a network, this multi-track sound will arrive at the third computer with an offset giving a negative effect to the listener. This project has dealt with this offset achieving a good synchronization of the multitrack sound getting a good effect on the listener. This was achieved thanks to the division of the project into several steps having constantly a good vision of the problem, a good scalability and having controlled the latency at all times. As we can see in the chapter 4 of the project, a lack of synchronization over c. 100μs is audible to the listener. RESUMEN. A día de hoy, podemos transmitir audio a través de Internet por varios motivos como pueden ser: una llamada telefónica, una emisión de audio o una teleconferencia. El problema viene cuando necesitas sincronizar ese sonido producido por los diferentes orígenes ya que la red a la que nos vamos a conectar puede perder los paquetes y/o introducir un retardo en las entregas de los mismos. Así mismo, estos retardos también pueden venir producidos por las diferentes velocidades a las que trabajan las tarjetas de sonido de cada dispositivo. En este proyecto, se ha trabajado con dos ordenadores emitiendo sonido de manera intermitente (uno se encargará de simular el canal izquierdo (mono) de la señal estéreo emitida, y el otro del canal derecho), estando conectados a través de una red TCP a un tercer ordenador, el cual debe recibir el sonido y reproducirlo en unos altavoces adecuadamente y sin retardo (deberá juntar los dos canales y reproducirlo como si de estéreo de tratara). Así, el objetivo principal de este proyecto es el de encontrar la manera de sincronizar el sonido producido por los dos ordenadores y escuchar el conjunto en unos altavoces finales. Las redes TCP introducen latencia en la transferencia de datos. El streaming de audio emitido a través de una red de este tipo puede sufrir dos grandes contratiempos: retardo y offset, los dos existentes en las comunicaciones entre ambos canales. Este proyecto se centra en las causas de ese retardo, investiga el efecto que provoca el offset entre ambos canales y propone una solución para sincronizar los canales en el dispositivo receptor. Para terminar, una buena sincronización del sonido es requerida en una época donde las aplicaciones de audio se están desarrollando continuamente. Cuando los dos dispositivos estén preparados para enviar audio a través de la red, la señal de sonido multi-canal llegará al tercer ordenador con un offset añadido, por lo que resultará en una mala experiencia en la escucha final. En este proyecto se ha tenido que lidiar con ese offset mencionado anteriormente y se ha conseguido una buena sincronización del sonido multi-canal obteniendo un buen efecto en la escucha final. Esto ha sido posible gracias a una división del proyecto en diversas etapas que proporcionaban la facilidad de poder solucionar los errores en cada paso dando una importante visión del problema y teniendo controlada la latencia en todo momento. Como se puede ver en el capítulo 4 del proyecto, la falta de sincronización sobre una diferencia de 100μs entre dos canales (offset) empieza a ser audible en la escucha final.