Search strategies and the automated control of plant diseases

We propose the use of the "infotaxis" search strategy as the navigation system of a robotic platform, able to search and localize infectious foci by detecting the changes in the profile of volatile organic compounds emitted by and infected plant. We builded a simple and cost effective robot platform that substitutes odour sensors in favour of light sensors and study their robustness and performance under non ideal conditions such as the exitence of obstacles due to land topology or weeds.

Low cost time division multiplexing of identical optical fibre ring intensity sensors

A time division multiplexing (TDM) array for passive multiplexing of identical fibre, optic intensity sensors has been demonstrated. Microbending loss sensors are introduced in fibre optic rings and pressure information is directly detected, demultiplexed and demodulated from the relative amplitude of the first two pulses produced on each ring. Several dynamic ranges from 6 dB to 14 dB are shown. A comparison between both fibre optic ring and Mach-Zehnder structure impulse responses is carried out and the consequences derived from second- and higher-order recirculating ring pulses are also evaluated. This technique can be applied to those TDM intensity sensing schemes which require low cost, high number of identical sensors, and suffer high element loss and undersirable intensity fluctuations at low frequencies.

Packaging and testing of fiber Bragg gratings for use as strain sensor for rock specimens

This paper reports a packaging and calibration procedure for surface mounting of fiber Bragg grating (FBG) sensors to measure strain in rocks. The packaging of FBG sensors is performed with glass fiber and polyester resin, and then subjected to tensile loads in order to obtain strength and deformability parameters, necessaries to assess the mechanical performance of the sensor packaging. For a specific package, an optimal curing condition has been found, showing good repeatability and adaptability for non-planar surfaces, such as occurs in rock engineering. The successfully packaged sensors and electrical strain gages were attached to standard rock specimens of gabbro. Longitudinal and transversal strains under compression loads were measured with both techniques, showing that response of FBG sensors is linear and reliable. An analytical model is used to characterize the influences of rock substrate and FBG packaging in strain transmission. As a result, we obtained a sensor packaging for non-planar and complex natural material under acceptable sensitivity suitable for very small strains as occurs in hard rocks.

Functional and effective connectivity in MEG. Application to the study of epilepsy

Nuestro cerebro contiene cerca de 1014 sinapsis neuronales. Esta enorme cantidad de conexiones proporciona un entorno ideal donde distintos grupos de neuronas se sincronizan transitoriamente para provocar la aparición de funciones cognitivas, como la percepción, el aprendizaje o el pensamiento. Comprender la organización de esta compleja red cerebral en base a datos neurofisiológicos, representa uno de los desafíos más importantes y emocionantes en el campo de la neurociencia. Se han propuesto recientemente varias medidas para evaluar cómo se comunican las diferentes partes del cerebro a diversas escalas (células individuales, columnas corticales, o áreas cerebrales). Podemos clasificarlos, según su simetría, en dos grupos: por una parte, la medidas simétricas, como la correlación, la coherencia o la sincronización de fase, que evalúan la conectividad funcional (FC); mientras que las medidas asimétricas, como la causalidad de Granger o transferencia de entropía, son capaces de detectar la dirección de la interacción, lo que denominamos conectividad efectiva (EC). En la neurociencia moderna ha aumentado el interés por el estudio de las redes funcionales cerebrales, en gran medida debido a la aparición de estos nuevos algoritmos que permiten analizar la interdependencia entre señales temporales, además de la emergente teoría de redes complejas y la introducción de técnicas novedosas, como la magnetoencefalografía (MEG), para registrar datos neurofisiológicos con gran resolución. Sin embargo, nos hallamos ante un campo novedoso que presenta aun varias cuestiones metodológicas sin resolver, algunas de las cuales trataran de abordarse en esta tesis. En primer lugar, el creciente número de aproximaciones para determinar la existencia de FC/EC entre dos o más señales temporales, junto con la complejidad matemática de las herramientas de análisis, hacen deseable organizarlas todas en un paquete software intuitivo y fácil de usar. Aquí presento HERMES (http://hermes.ctb.upm.es), una toolbox en MatlabR, diseñada precisamente con este fin. Creo que esta herramienta será de gran ayuda para todos aquellos investigadores que trabajen en el campo emergente del análisis de conectividad cerebral y supondrá un gran valor para la comunidad científica. La segunda cuestión practica que se aborda es el estudio de la sensibilidad a las fuentes cerebrales profundas a través de dos tipos de sensores MEG: gradiómetros planares y magnetómetros, esta aproximación además se combina con un enfoque metodológico, utilizando dos índices de sincronización de fase: phase locking value (PLV) y phase lag index (PLI), este ultimo menos sensible a efecto la conducción volumen. Por lo tanto, se compara su comportamiento al estudiar las redes cerebrales, obteniendo que magnetómetros y PLV presentan, respectivamente, redes más densamente conectadas que gradiómetros planares y PLI, por los valores artificiales que crea el problema de la conducción de volumen. Sin embargo, cuando se trata de caracterizar redes epilépticas, el PLV ofrece mejores resultados, debido a la gran dispersión de las redes obtenidas con PLI. El análisis de redes complejas ha proporcionado nuevos conceptos que mejoran caracterización de la interacción de sistemas dinámicos. Se considera que una red está compuesta por nodos, que simbolizan sistemas, cuyas interacciones se representan por enlaces, y su comportamiento y topología puede caracterizarse por un elevado número de medidas. Existe evidencia teórica y empírica de que muchas de ellas están fuertemente correlacionadas entre sí. Por lo tanto, se ha conseguido seleccionar un pequeño grupo que caracteriza eficazmente estas redes, y condensa la información redundante. Para el análisis de redes funcionales, la selección de un umbral adecuado para decidir si un determinado valor de conectividad de la matriz de FC es significativo y debe ser incluido para un análisis posterior, se convierte en un paso crucial. En esta tesis, se han obtenido resultados más precisos al utilizar un test de subrogadas, basado en los datos, para evaluar individualmente cada uno de los enlaces, que al establecer a priori un umbral fijo para la densidad de conexiones. Finalmente, todas estas cuestiones se han aplicado al estudio de la epilepsia, caso práctico en el que se analizan las redes funcionales MEG, en estado de reposo, de dos grupos de pacientes epilépticos (generalizada idiopática y focal frontal) en comparación con sujetos control sanos. La epilepsia es uno de los trastornos neurológicos más comunes, con más de 55 millones de afectados en el mundo. Esta enfermedad se caracteriza por la predisposición a generar ataques epilépticos de actividad neuronal anormal y excesiva o bien síncrona, y por tanto, es el escenario perfecto para este tipo de análisis al tiempo que presenta un gran interés tanto desde el punto de vista clínico como de investigación. Los resultados manifiestan alteraciones especificas en la conectividad y un cambio en la topología de las redes en cerebros epilépticos, desplazando la importancia del ‘foco’ a la ‘red’, enfoque que va adquiriendo relevancia en las investigaciones recientes sobre epilepsia. ABSTRACT There are about 1014 neuronal synapses in the human brain. This huge number of connections provides the substrate for neuronal ensembles to become transiently synchronized, producing the emergence of cognitive functions such as perception, learning or thinking. Understanding the complex brain network organization on the basis of neuroimaging data represents one of the most important and exciting challenges for systems neuroscience. Several measures have been recently proposed to evaluate at various scales (single cells, cortical columns, or brain areas) how the different parts of the brain communicate. We can classify them, according to their symmetry, into two groups: symmetric measures, such as correlation, coherence or phase synchronization indexes, evaluate functional connectivity (FC); and on the other hand, the asymmetric ones, such as Granger causality or transfer entropy, are able to detect effective connectivity (EC) revealing the direction of the interaction. In modern neurosciences, the interest in functional brain networks has increased strongly with the onset of new algorithms to study interdependence between time series, the advent of modern complex network theory and the introduction of powerful techniques to record neurophysiological data, such as magnetoencephalography (MEG). However, when analyzing neurophysiological data with this approach several questions arise. In this thesis, I intend to tackle some of the practical open problems in the field. First of all, the increase in the number of time series analysis algorithms to study brain FC/EC, along with their mathematical complexity, creates the necessity of arranging them into a single, unified toolbox that allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them. I developed such a toolbox for this aim, it is named HERMES (http://hermes.ctb.upm.es), and encompasses several of the most common indexes for the assessment of FC and EC running for MatlabR environment. I believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis and will entail a great value for the scientific community. The second important practical issue tackled in this thesis is the evaluation of the sensitivity to deep brain sources of two different MEG sensors: planar gradiometers and magnetometers, in combination with the related methodological approach, using two phase synchronization indexes: phase locking value (PLV) y phase lag index (PLI), the latter one being less sensitive to volume conduction effect. Thus, I compared their performance when studying brain networks, obtaining that magnetometer sensors and PLV presented higher artificial values as compared with planar gradiometers and PLI respectively. However, when it came to characterize epileptic networks it was the PLV which gives better results, as PLI FC networks where very sparse. Complex network analysis has provided new concepts which improved characterization of interacting dynamical systems. With this background, networks could be considered composed of nodes, symbolizing systems, whose interactions with each other are represented by edges. A growing number of network measures is been applied in network analysis. However, there is theoretical and empirical evidence that many of these indexes are strongly correlated with each other. Therefore, in this thesis I reduced them to a small set, which could more efficiently characterize networks. Within this framework, selecting an appropriate threshold to decide whether a certain connectivity value of the FC matrix is significant and should be included in the network analysis becomes a crucial step, in this thesis, I used the surrogate data tests to make an individual data-driven evaluation of each of the edges significance and confirmed more accurate results than when just setting to a fixed value the density of connections. All these methodologies were applied to the study of epilepsy, analysing resting state MEG functional networks, in two groups of epileptic patients (generalized and focal epilepsy) that were compared to matching control subjects. Epilepsy is one of the most common neurological disorders, with more than 55 million people affected worldwide, characterized by its predisposition to generate epileptic seizures of abnormal excessive or synchronous neuronal activity, and thus, this scenario and analysis, present a great interest from both the clinical and the research perspective. Results revealed specific disruptions in connectivity and network topology and evidenced that networks’ topology is changed in epileptic brains, supporting the shift from ‘focus’ to ‘networks’ which is gaining importance in modern epilepsy research.

Piezoelectric sensors system for impact detecting

This work describes an acoustic system that allows the automatic detection and location of mechanical impacts on metallic based structures, which is suitable in robotics and industrial applications. The system is based on the time delays of propagation of the acoustic waves along the metallic based structure and it determines the instant and the position when and were the impact has been produced by piezoelectric sensors and an electronic-computerized system. We have obtained that for distance impact of 40 cm and 50 cm the time delay is 2 s and 72 s respectively.

Implementation of the Heated Pulsed Theory using actively heated fiber optics: measurements of soil volumetric water content and soil volumetric heat capacity

Existe una creciente necesidad de hacer el mejor uso del agua para regadío. Una alternativa eficiente consiste en la monitorización del contenido volumétrico de agua (θ), utilizando sensores de humedad. A pesar de existir una gran diversidad de sensores y tecnologías disponibles, actualmente ninguna de ellas permite obtener medidas distribuidas en perfiles verticales de un metro y en escalas laterales de 0.1-1,000 m. En este sentido, es necesario buscar tecnologías alternativas que sirvan de puente entre las medidas puntuales y las escalas intermedias. Esta tesis doctoral se basa en el uso de Fibra Óptica (FO) con sistema de medida de temperatura distribuida (DTS), una tecnología alternativa de reciente creación que ha levantado gran expectación en las últimas dos décadas. Específicamente utilizamos el método de fibra calentada, en inglés Actively Heated Fiber Optic (AHFO), en la cual los cables de Fibra Óptica se utilizan como sondas de calor mediante la aplicación de corriente eléctrica a través de la camisa de acero inoxidable, o de un conductor eléctrico simétricamente posicionado, envuelto, alrededor del haz de fibra óptica. El uso de fibra calentada se basa en la utilización de la teoría de los pulsos de calor, en inglés Heated Pulsed Theory (HPP), por la cual el conductor se aproxima a una fuente de calor lineal e infinitesimal que introduce calor en el suelo. Mediante el análisis del tiempo de ocurrencia y magnitud de la respuesta térmica ante un pulso de calor, es posible estimar algunas propiedades específicas del suelo, tales como el contenido de humedad, calor específico (C) y conductividad térmica. Estos parámetros pueden ser estimados utilizando un sensor de temperatura adyacente a la sonda de calor [método simple, en inglés single heated pulsed probes (SHPP)], ó a una distancia radial r [método doble, en inglés dual heated pulsed probes (DHPP)]. Esta tesis doctoral pretende probar la idoneidad de los sistemas de fibra óptica calentada para la aplicación de la teoría clásica de sondas calentadas. Para ello, se desarrollarán dos sistemas FO-DTS. El primero se sitúa en un campo agrícola de La Nava de Arévalo (Ávila, España), en el cual se aplica la teoría SHPP para estimar θ. El segundo sistema se desarrolla en laboratorio y emplea la teoría DHPP para medir tanto θ como C. La teoría SHPP puede ser implementada con fibra óptica calentada para obtener medidas distribuidas de θ, mediante la utilización de sistemas FO-DTS y el uso de curvas de calibración específicas para cada suelo. Sin embargo, la mayoría de aplicaciones AHFO se han desarrollado exclusivamente en laboratorio utilizando medios porosos homogéneos. En esta tesis se utiliza el programa Hydrus 2D/3D para definir tales curvas de calibración. El modelo propuesto es validado en un segmento de cable enterrado en una instalación de fibra óptica y es capaz de predecir la respuesta térmica del suelo en puntos concretos de la instalación una vez que las propiedades físicas y térmicas de éste son definidas. La exactitud de la metodología para predecir θ frente a medidas puntuales tomadas con sensores de humedad comerciales fue de 0.001 a 0.022 m3 m-3 La implementación de la teoría DHPP con AHFO para medir C y θ suponen una oportunidad sin precedentes para aplicaciones medioambientales. En esta tesis se emplean diferentes combinaciones de cables y fuentes emisoras de calor, que se colocan en paralelo y utilizan un rango variado de espaciamientos, todo ello en el laboratorio. La amplitud de la señal y el tiempo de llegada se han observado como funciones del calor específico del suelo. Medidas de C, utilizando esta metodología y ante un rango variado de contenidos de humedad, sugirieron la idoneidad del método, aunque también se observaron importantes errores en contenidos bajos de humedad de hasta un 22%. La mejora del método requerirá otros modelos más precisos que tengan en cuenta el diámetro del cable, así como la posible influencia térmica del mismo. ABSTRACT There is an increasing need to make the most efficient use of water for irrigation. A good approach to make irrigation as efficient as possible is to monitor soil water content (θ) using soil moisture sensors. Although, there is a broad range of different sensors and technologies, currently, none of them can practically and accurately provide vertical and lateral moisture profiles spanning 0-1 m depth and 0.1-1,000 m lateral scales. In this regard, further research to fulfill the intermediate scale and to bridge single-point measurement with the broaden scales is still needed. This dissertation is based on the use of Fiber Optics with Distributed Temperature Sensing (FO-DTS), a novel approach which has been receiving growing interest in the last two decades. Specifically, we employ the so called Actively Heated Fiber Optic (AHFO) method, in which FO cables are employed as heat probe conductors by applying electricity to the stainless steel armoring jacket or an added conductor symmetrically positioned (wrapped) about the FO cable. AHFO is based on the classic Heated Pulsed Theory (HPP) which usually employs a heat probe conductor that approximates to an infinite line heat source which injects heat into the soil. Observation of the timing and magnitude of the thermal response to the energy input provide enough information to derive certain specific soil thermal characteristics such as the soil heat capacity, soil thermal conductivity or soil water content. These parameters can be estimated by capturing the soil thermal response (using a thermal sensor) adjacent to the heat source (the heating and the thermal sources are mounted together in the so called single heated pulsed probe (SHPP)), or separated at a certain distance, r (dual heated pulsed method (DHPP) This dissertation aims to test the feasibility of heated fiber optics to implement the HPP theory. Specifically, we focus on measuring soil water content (θ) and soil heat capacity (C) by employing two types of FO-DTS systems. The first one is located in an agricultural field in La Nava de Arévalo (Ávila, Spain) and employ the SHPP theory to estimate θ. The second one is developed in the laboratory using the procedures described in the DHPP theory, and focuses on estimating both C and θ. The SHPP theory can be implemented with actively heated fiber optics (AHFO) to obtain distributed measurements of soil water content (θ) by using reported soil thermal responses in Distributed Temperature Sensing (DTS) and with a soil-specific calibration relationship. However, most reported AHFO applications have been calibrated under laboratory homogeneous soil conditions, while inexpensive efficient calibration procedures useful in heterogeneous soils are lacking. In this PhD thesis, we employ the Hydrus 2D/3D code to define these soil-specific calibration curves. The model is then validated at a selected FO transect of the DTS installation. The model was able to predict the soil thermal response at specific locations of the fiber optic cable once the surrounding soil hydraulic and thermal properties were known. Results using electromagnetic moisture sensors at the same specific locations demonstrate the feasibility of the model to detect θ within an accuracy of 0.001 to 0.022 m3 m-3. Implementation of the Dual Heated Pulsed Probe (DPHP) theory for measurement of volumetric heat capacity (C) and water content (θ) with Distributed Temperature Sensing (DTS) heated fiber optic (FO) systems presents an unprecedented opportunity for environmental monitoring. We test the method using different combinations of FO cables and heat sources at a range of spacings in a laboratory setting. The amplitude and phase-shift in the heat signal with distance was found to be a function of the soil volumetric heat capacity (referred, here, to as Cs). Estimations of Cs at a range of θ suggest feasibility via responsiveness to the changes in θ (we observed a linear relationship in all FO combinations), though observed bias with decreasing soil water contents (up to 22%) was also reported. Optimization will require further models to account for the finite radius and thermal influence of the FO cables, employed here as “needle probes”. Also, consideration of the range of soil conditions and cable spacing and jacket configurations, suggested here to be valuable subjects of further study and development.

A General Purpose Configurable Controller for Indoors and Outdoors GPS-Denied Navigation for Multirotor Unmanned Aerial Vehicles

This research on odometry based GPS-denied navigation on multirotor Unmanned Aerial Vehicles is focused among the interactions between the odometry sensors and the navigation controller. More precisely, we present a controller architecture that allows to specify a speed specified flight envelope where the quality of the odometry measurements is guaranteed. The controller utilizes a simple point mass kinematic model, described by a set of configurable parameters, to generate a complying speed plan. For experimental testing, we have used down-facing camera optical-flow as odometry measurement. This work is a continuation of prior research to outdoors environments using an AR Drone 2.0 vehicle, as it provides reliable optical flow on a wide range of flying conditions and floor textures. Our experiments show that the architecture is realiable for outdoors flight on altitudes lower than 9 m. A prior version of our code was utilized to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012. The code will be released as an open-source ROS stack hosted on GitHub.

A proposal for a modular and application-aware autonomic manager of private cloud infrastructures

Recientemente, el paradigma de la computación en la nube ha recibido mucho interés por parte tanto de la industria como del mundo académico. Las infraestructuras cloud públicas están posibilitando nuevos modelos de negocio y ayudando a reducir costes. Sin embargo, una compañía podría desear ubicar sus datos y servicios en sus propias instalaciones, o tener que atenerse a leyes de protección de datos. Estas circunstancias hacen a las infraestructuras cloud privadas ciertamente deseables, ya sea para complementar a las públicas o para sustituirlas por completo. Por desgracia, las carencias en materia de estándares han impedido que las soluciones para la gestión de infraestructuras privadas se hayan desarrollado adecuadamente. Además, la multitud de opciones disponibles ha creado en los clientes el miedo a depender de una tecnología concreta (technology lock-in). Una de las causas de este problema es la falta de alineación entre la investigación académica y los productos comerciales, ya que aquella está centrada en el estudio de escenarios idealizados sin correspondencia con el mundo real, mientras que éstos consisten en soluciones desarrolladas sin tener en cuenta cómo van a encajar con los estándares más comunes o sin preocuparse de hacer públicos sus resultados. Con objeto de resolver este problema, propongo un sistema de gestión modular para infraestructuras cloud privadas enfocado en tratar con las aplicaciones en lugar de centrarse únicamente en los recursos hardware. Este sistema de gestión sigue el paradigma de la computación autónoma y está diseñado en torno a un modelo de información sencillo, desarrollado para ser compatible con los estándares más comunes. Este modelo divide el entorno en dos vistas, que sirven para separar aquello que debe preocupar a cada actor involucrado del resto de información, pero al mismo tiempo permitiendo relacionar el entorno físico con las máquinas virtuales que se despliegan encima de él. En dicho modelo, las aplicaciones cloud están divididas en tres tipos genéricos (Servicios, Trabajos de Big Data y Reservas de Instancias), para que así el sistema de gestión pueda sacar partido de las características propias de cada tipo. El modelo de información está complementado por un conjunto de acciones de gestión atómicas, reversibles e independientes, que determinan las operaciones que se pueden llevar a cabo sobre el entorno y que es usado para hacer posible la escalabilidad en el entorno. También describo un motor de gestión encargado de, a partir del estado del entorno y usando el ya mencionado conjunto de acciones, la colocación de recursos. Está dividido en dos niveles: la capa de Gestores de Aplicación, encargada de tratar sólo con las aplicaciones; y la capa del Gestor de Infraestructura, responsable de los recursos físicos. Dicho motor de gestión obedece un ciclo de vida con dos fases, para así modelar mejor el comportamiento de una infraestructura real. El problema de la colocación de recursos es atacado durante una de las fases (la de consolidación) por un resolutor de programación entera, y durante la otra (la online) por un heurístico hecho ex-profeso. Varias pruebas han demostrado que este acercamiento combinado es superior a otras estrategias. Para terminar, el sistema de gestión está acoplado a arquitecturas de monitorización y de actuadores. Aquella estando encargada de recolectar información del entorno, y ésta siendo modular en su diseño y capaz de conectarse con varias tecnologías y ofrecer varios modos de acceso. ABSTRACT The cloud computing paradigm has raised in popularity within the industry and the academia. Public cloud infrastructures are enabling new business models and helping to reduce costs. However, the desire to host company’s data and services on premises, and the need to abide to data protection laws, make private cloud infrastructures desirable, either to complement or even fully substitute public oferings. Unfortunately, a lack of standardization has precluded private infrastructure management solutions to be developed to a certain level, and a myriad of diferent options have induced the fear of lock-in in customers. One of the causes of this problem is the misalignment between academic research and industry ofering, with the former focusing in studying idealized scenarios dissimilar from real-world situations, and the latter developing solutions without taking care about how they f t with common standards, or even not disseminating their results. With the aim to solve this problem I propose a modular management system for private cloud infrastructures that is focused on the applications instead of just the hardware resources. This management system follows the autonomic system paradigm, and is designed around a simple information model developed to be compatible with common standards. This model splits the environment in two views that serve to separate the concerns of the stakeholders while at the same time enabling the traceability between the physical environment and the virtual machines deployed onto it. In it, cloud applications are classifed in three broad types (Services, Big Data Jobs and Instance Reservations), in order for the management system to take advantage of each type’s features. The information model is paired with a set of atomic, reversible and independent management actions which determine the operations that can be performed over the environment and is used to realize the cloud environment’s scalability. From the environment’s state and using the aforementioned set of actions, I also describe a management engine tasked with the resource placement. It is divided in two tiers: the Application Managers layer, concerned just with applications; and the Infrastructure Manager layer, responsible of the actual physical resources. This management engine follows a lifecycle with two phases, to better model the behavior of a real infrastructure. The placement problem is tackled during one phase (consolidation) by using an integer programming solver, and during the other (online) with a custom heuristic. Tests have demonstrated that this combined approach is superior to other strategies. Finally, the management system is paired with monitoring and actuators architectures. The former able to collect the necessary information from the environment, and the later modular in design and capable of interfacing with several technologies and ofering several access interfaces.

Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

A robust and fast method for 6DoF motion estimation from generalized 3D data

Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six degrees of freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.

A multimodal tablet-based interface for designing and reviewing 3D engineering models

The ability to view and interact with 3D models has been happening for a long time. However, vision-based 3D modeling has only seen limited success in applications, as it faces many technical challenges. Hand-held mobile devices have changed the way we interact with virtual reality environments. Their high mobility and technical features, such as inertial sensors, cameras and fast processors, are especially attractive for advancing the state of the art in virtual reality systems. Also, their ubiquity and fast Internet connection open a path to distributed and collaborative development. However, such path has not been fully explored in many domains. VR systems for real world engineering contexts are still difficult to use, especially when geographically dispersed engineering teams need to collaboratively visualize and review 3D CAD models. Another challenge is the ability to rendering these environments at the required interactive rates and with high fidelity. In this document it is presented a virtual reality system mobile for visualization, navigation and reviewing large scale 3D CAD models, held under the CEDAR (Collaborative Engineering Design and Review) project. It’s focused on interaction using different navigation modes. The system uses the mobile device's inertial sensors and camera to allow users to navigate through large scale models. IT professionals, architects, civil engineers and oil industry experts were involved in a qualitative assessment of the CEDAR system, in the form of direct user interaction with the prototypes and audio-recorded interviews about the prototypes. The lessons learned are valuable and are presented on this document. Subsequently it was prepared a quantitative study on the different navigation modes to analyze the best mode to use it in a given situation.

Integrated architecture for vision-based indoor localization and mapping of a quadrotor micro-air vehicle

Atualmente os sistemas de pilotagem autónoma de quadricópteros estão a ser desenvolvidos de forma a efetuarem navegação em espaços exteriores, onde o sinal de GPS pode ser utilizado para definir waypoints de navegação, modos de position e altitude hold, returning home, entre outros. Contudo, o problema de navegação autónoma em espaços fechados sem que se utilize um sistema de posicionamento global dentro de uma sala, subsiste como um problema desafiante e sem solução fechada. Grande parte das soluções são baseadas em sensores dispendiosos, como o LIDAR ou como sistemas de posicionamento externos (p.ex. Vicon, Optitrack). Algumas destas soluções reservam a capacidade de processamento de dados dos sensores e dos algoritmos mais exigentes para sistemas de computação exteriores ao veículo, o que também retira a componente de autonomia total que se pretende num veículo com estas características. O objetivo desta tese pretende, assim, a preparação de um sistema aéreo não-tripulado de pequeno porte, nomeadamente um quadricóptero, que integre diferentes módulos que lhe permitam simultânea localização e mapeamento em espaços interiores onde o sinal GPS ´e negado, utilizando, para tal, uma câmara RGB-D, em conjunto com outros sensores internos e externos do quadricóptero, integrados num sistema que processa o posicionamento baseado em visão e com o qual se pretende que efectue, num futuro próximo, planeamento de movimento para navegação. O resultado deste trabalho foi uma arquitetura integrada para análise de módulos de localização, mapeamento e navegação, baseada em hardware aberto e barato e frameworks state-of-the-art disponíveis em código aberto. Foi também possível testar parcialmente alguns módulos de localização, sob certas condições de ensaio e certos parâmetros dos algoritmos. A capacidade de mapeamento da framework também foi testada e aprovada. A framework obtida encontra-se pronta para navegação, necessitando apenas de alguns ajustes e testes.

Minimum, maximum and mean annual ground temperatures in permafrost around Abisko ANS between 2008-2009

Monitoring of permafrost has been ongoing since 1978 in the Abisko area, northernmost Sweden, when measurements of active layer thickness started. In 1980, boreholes were drilled in three mires in the area to record permafrost temperatures. Recordings were made twice per year, and the last data were obtained in 2002. During the International Polar Year (2007-2008), new boreholes were drilled within the 'Back to the Future' (BTF) and 'Thermal State of Permafrost' (TSP) projects that enabled year-round temperature monitoring. Mean annual ground temperatures (MAGT) in the mires are close to 0°C, ranging from -0.16 to -0.47°C at 5 m depth. Data from the boreholes show increasing ground temperatures in the upper and lower part by 0.4 to 1°C between 1980 and 2002. At one mire, permafrost thickness has decreased from 15 m in 1980 to ca. 9 m in 2009, with an accelerating thawing trend during the last decade.

Cardiovascular disease and PPARdelta: Targeting the risk factors

Metabolism, in part, is regulated by the peroxisome proliferator-activated receptors (PPARs). The PPARs act as nutritional lipid sensors and three mammalian PPAR subtypes designated PPARalpha (NR1C1), PPARgamma (NR1C3) and PPARdelta (NR1C2) have been identified. This subgroup of nuclear hormone receptors binds DNA and controls gene expression at the nexus of pathways that regulate lipid and glucose homeostasis, energy storage and expenditure in an organ-specific manner. Recent evidence has demonstrated activation of PPARdelta in the major mass peripheral tissue (ie, adipose and skeletal muscle). It enhances glucose tolerance, insulin-stimulated glucose disposal, lipid catabolism, energy expenditure, cholesterol efflux and oxygen consumption. These effects positively influence the blood-lipid profile. Furthermore, PPARdelta activation produces a predominant type I/slow twitch/oxidative muscle fiber phenotype that leads to increased endurance, insulin sensitivity and resistance to obesity. PPARdelta has rapidly emerged as a potential target in the battle against dyslipidemia, insulin insensitivity, type II diabetes and obesity, with therapeutic efficacy in the treatment of cardiovascular disease risk factors. GW-501516 is currently undergoing phase II safety and efficacy trials in human volunteers for the treatment of dyslipidemia. The outcome of these clinical trials are eagerly awaited against a background of conflicting reports about cancer risks in genetically predisposed animal models. This review focuses on the potential pharmacological utility of selective PPARdelta agonists in the context of risk factors associated with metabolic and cardiovascular disease.