Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Investigation of AlInN barrier ISFET structures with GaN capping for pH detection

The pH response of GaN/AlInN/AlN/GaN ion-sensitive field effect transistor (ISFET) on Si substrates has been characterized. We analyzed the variation of the surface potential (ΔVsp/ΔpH) and current (ΔIds/ΔpH) with solution pH in devices with the same indium content (17%, in-plane lattice-matched to GaN) and different AlInN thickness (6 nm and 10 nm), and compared with the literature. The shrinkage of the barrier, that has the effect to increase the transconductance of the device, makes the 2-dimensional electron density (2DEG) at the interface very sensitive to changes in the surface. Although the surface potential sensitivity to pH is similar in the two devices, the current change with pH (ΔIds/ΔpH), when biasing the ISFET by a Ag/AgCl reference electrode, is almost 50% higher in the device with 6 nm AlInN barrier, compared to the device with 10 nm barrier. When measuring the current response (ΔIds/ΔpH) without reference electrode, the device with thinner AlInN layer has a larger response than the thicker one, of a factor of 140%, and that current response without reference electrode is only 22% lower than its maximum response obtained using reference electrode.

The SSN ontology of the W3C semantic sensor network incubator group

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations ? the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

A Development Methodology to Facilitate the Integration of Smart Spaces into the Web of Things

How to create or integrate large Smart Spaces (considered as mash-ups of sensors and actuators) into the paradigm of ?Web of Things? has been the motivation of many recent works. A cutting-edge approach deals with developing and deploying web-enabled embedded devices with two major objectives: 1) to integrate sensor and actuator technologies into everyday objects, and 2) to allow a diversity of devices to plug to Internet. Currently, developers who want to use this Internet-oriented approach need have solid understanding about sensorial platforms and semantic technologies. In this paper we propose a Resource-Oriented and Ontology-Driven Development (ROOD) methodology, based on Model Driven Architecture (MDA), to facilitate to any developer the development and deployment of Smart Spaces. Early evaluations of the ROOD methodology have been successfully accomplished through a partial deployment of a Smart Hotel.

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.

Air pollution data classification by SOM Neural Network

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables.

Controlador MIDI basado en Arduino

El presente proyecto tiene como objetivo la creación de un controlador MIDI económico que haga uso de la tecnología actual, y partiendo de la idea del instrumento clásico, el Theremin, desarrollado por Lev Serguéievich Termen. Para ello se ha dividido el proyecto en dos principales bloques, el primero, hardware y el segundo, software. En la parte del hardware, se explica cual ha sido la razón de la utilización del microprocesador Arduino Uno, sus características técnicas y el uso de sensores de ultrasonido, ya que proporcionan la característica de poder interactuar con el controlador a través de gestos con las manos, al igual que un Theremin clásico. Se explica el montaje de los dispositivos que conforman el controlador, así como la mejora realizada, con la utilización de 4 de estos sensores, para dar más capacidades de interactuación con el controlador MIDI. También se ve en ese apartado, como se programa la tarjeta de Arduino, para que se encargue de realizar medidas con los sensores y enviarlas por el puerto serial USB. En el apartado del software se da una introducción al entorno de programación Max/MSP. Se ve el plug in desarrollado con este lenguaje, para poder comunicar el controlador MIDI con un software de audio profesional (Ableton Live) y se explica con detalle los bloques que conforman el plug in de control de sensores y como es transformada la información que entrega el microprocesador Arduino por el puerto USB, en datos MIDI. También, se da una explicación sobre el manejo correcto del controlador a la hora de mover las manos sobre los sensores y de donde situar el instrumento para que no se produzcan problemas de interferencias con las señales que envían los ultrasonidos. Además, se proporciona un presupuesto del coste de los materiales, y otro del coste del desarrollo realizado por el ingeniero. ABSTRACT The aim of this Project is the creation of an economical MIDI controller that uses nowadays technology and that is based on the idea of the Theremin, a classical instrument conceived by Lev Serguéievich Termen. In order to accomplish this, the project has been divided into two sections: hardware and software. The hardware section explains why the microprocessor Arduino Uno has been chosen, sets out its technical specifications and the use of ultrasonic sensors. These sensors enable the user to interact with the controller through hand gestures like the Theremin. The assembly of the devices is exposed as well as the improvements made with the use of four of these sensors to offer more interactive capabilities with the MIDI controller. The Arduino singleboard programming that performs the measurements with the sensors and sends these measurements through the USB serial port is also explained here. The software section introduces Max/MSP programming environment as well as the plug in developed with this language that connects the MIDI controller with professional audio software (Ableton Live). The blocks that build the sensor controller plug in are explained in detail along with the way the Arduino delivers the information through the USB port into MIDI data. In addition, an explanation of the correct handling of the MIDI controller is given focusing on how the user should move his hands above the sensors and where to place the instrument to avoid interference problems with the signals sent. Also, a cost estimation of both materials and engineering is provided.

Vigilancia por análisis de ruido de los sensores de temperatura

En este proyecto se van a aplicar las técnicas de análisis de ruido para caracterizar la respuesta dinámica de varios sensores de temperatura, tanto termorresistencias de platino como de termopares. Estos sensores son imprescindibles para él correcto funcionamiento de las centrales nucleares y requieren vigilancia para garantizar la exactitud de las medidas. Las técnicas de análisis de ruido son técnicas pasivas, es decir, no afectan a la operación de la planta y permiten realizar una vigilancia in situ de los sensores. Para el caso de los sensores de temperatura, dado que se pueden asimilar a sistemas de primer orden, el parámetro fundamental a vigilar es el tiempo de respuesta. Éste puede obtenerse para cada una de las sondas por medio de técnicas en el dominio de la frecuencia (análisis espectral) o por medio de técnicas en el dominio del tiempo (modelos autorregresivos). Además de la estimación del tiempo de respuesta, se realizará una caracterización estadística de las sondas. El objetivo es conocer el comportamiento de los sensores y vigilarlos de manera que se puedan diagnosticar las averías aunque éstas estén en una etapa incipiente. ABSTRACT In this project we use noise analysis technique to study the dynamic response of RTDs (Resistant temperature detectors) and thermocouples. These sensors are essential for the proper functioning of nuclear power plants and therefore need to be monitored to guarantee accurate measurements. The noise analysis techniques do not affect plant operation and allow in situ monitoring of the sensors. Temperature sensors are equivalent to first order systems. In these systems the main parameter to monitor is the response time which can be obtained by means of techniques in the frequency domain (spectral analysis) as well as time domain (autoregressive models). Besides response time estimation the project will also include a statistical study of the probes. The goal is to understand the behavior of the sensors and monitor them in order to detect any anomalies or malfunctions even if they occur in an early stage.

Sistema para cambiar la geometría de un motor

En este proyecto se desarrolla un sistema electrónico para variar la geometría de un motor de un monoplaza que participa en la competición Fórmula SAE. Fórmula SAE es una competición de diseño de monoplazas para estudiantes, organizado por “Society of Automotive Enginners” (SAE). Este concurso busca la innovación tecnológica de la automoción, así como que estudiantes participen en un trabajo real, en el cual el objetivo es obtener resultados competitivos cumpliendo con una serie de requisitos. La variación de la geometría de un motor en un vehículo permite mejorar el rendimiento del monoplaza consiguiendo elevar el par de potencia del motor. Cualquier mejora en del vehículo en un ámbito de competición puede resultar determinante en el desenlace de la misma. El objetivo del proyecto es realizar esta variación mediante el control de la longitud de los tubos de admisión de aire o “runners” del motor de combustión, empleando un motor lineal paso a paso. A partir de la información obtenida por sensores de revoluciones del motor de combustión y la posición del acelerador se debe controlar la distancia de dichos tubos. Integrando este sistema en el bus CAN del vehículo para que comparta la información medida al resto de módulos. Por todo esto se realiza un estudio aclarando los aspectos generales del objetivo del trabajo, para la comprensión del proyecto a realizar, las posibilidades de realización y adquisición de conocimientos para un mejor desarrollo. Se presenta una solución basada en el control del motor lineal paso a paso mediante el microcontrolador PIC32MX795F512-L. Dispositivo del fabricante Microchip con una arquitectura de 32 bits. Este dispone de un módulo CAN integrado y distintos periféricos que se emplean en la medición de los sensores y actuación sobre el motor paso a paso empleando el driver de Texas Instruments DRV8805. Entonces el trabajo se realiza en dos líneas, una parte software de programación del control del sistema, empleando el software de Microchip MPLABX IDE y otra parte hardware de diseño de una PCB y circuitos acondicionadores para la conexión del microcontrolador, con los sensores, driver, motor paso a paso y bus CAN. El software empleado para la realización de la PCB es Orcad9.2/Layout. Para la evaluación de las medidas obtenidas por los sensores y la comprobación del bus CAN se emplea el kit de desarrollo de Microchip, MCP2515 CAN Bus Monitor Demo Board, que permite ver la información en el bus CAN e introducir tramas al mismo. ABSTRACT. This project develops an electronic system to vary the geometry of a car engine which runs the Formula SAE competition. Formula SAE is a design car competition for students, organized by "Society of Automotive Engineers" (SAE). This competition seeks technological innovation in the automotive industry and brings in students to participate in a real job, in which the objective is to obtain competitive results in compliance with certain requirements. Varying engine’s geometry in a vehicle improves car’s performance raising engine output torque. Any improvement in the vehicle in a competition field can be decisive in the outcome of it. The goal of the project is the variation by controlling the length of the air intake pipe or "runners" in a combustion engine, using a linear motor step. For these, uses the information gathered by speed sensors from the combustion engine and by the throttle position to control the distance of these tubes. This system is integrated in the vehicle CAN bus to share the information with the other modules. For all this is made a study to clarify the general aspects of the project in order to understand the activities developed inside the project, the different options available and also, to acquire knowledge for a better development of the project. The solution is based on linear stepper motor control by the microcontroller PIC32MX795F512-L. Device from manufacturer Microchip with a 32-bit architecture. This module has an integrated CAN various peripherals that are used in measuring the performance of the sensors and drives the stepper motor using Texas Instruments DRV8805 driver. Then the work is done in two lines, first, control programming software system using software MPLABX Microchip IDE and, second, hardware design of a PCB and conditioning circuits for connecting the microcontroller, with sensors, driver stepper motor and CAN bus. The software used to carry out the PCB is Orcad9.2/Layout. For the evaluation of the measurements obtained by the sensors and CAN bus checking is used Microchip development kit, MCP2515 CAN Bus Monitor Demo Board, that allows you to see the information on the CAN bus and enter new frames in the bus.

Optical bistable devices as sensing elements

The nonlinear optical properties of many materials and devices have been the main object of research as potential candidates for sensing in different places. Just one of these properties has been, in most of the cases, the basis for the sensing operation. As a consequence, just one parameter can be detected. In this paper, although just one property will be employed too, we will show the possibility to sense different parameters with just one type of sensor. The way adopted in this work is the use of the optical bistability obtained from different photonic structures. Because this optical bistability has a strong dependence on many different parameters the possibility to sense different inputs appears. In our case, we will report the use of some non-linear optical devices, mainly Semiconductor Optical Amplifiers, as sensing elements. Because their outputs depend on many parameters, as the incident light wavelength, polarization, intensity and direction, applied voltage and feedback characteristics, they can be employed to detect, at the same time, different type of signals. This is because the way these different signals affect to the sensor response is very different too and appears under a different set of characteristics.

Unattended motion detection system based on the mammalian retina

Sensing systems in living bodies offer a large variety of possible different configurations and philosophies able to be emulated in artificial sensing systems. Motion detection is one of the areas where different animals adopt different solutions and, in most of the cases, these solutions reflect a very sophisticated form. One of them, the mammalian visual system, presents several advantages with respect to the artificial ones. The main objective of this paper is to present a system, based on this biological structure, able to detect motion, its sense and its characteristics. The configuration adopted responds to the internal structure of the mammalian retina, where just five types of cells arranged in five layers are able to differentiate a large number of characteristics of the image impinging onto it. Its main advantage is that the detection of these properties is based purely on its hardware. A simple unit, based in a previous optical logic cell employed in optical computing, is the basis for emulating the different behaviors of the biological neurons. No software is present and, in this way, no possible interference from outside affects to the final behavior. This type of structure is able to work, once the internal configuration is implemented, without any further attention. Different possibilities are present in the architecture to be presented: detection of motion, of its direction and intensity. Moreover, some other characteristics, as symmetry may be obtained.

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

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

Polypropylene/glass fiber hierarchical composites incorporating inorganic fullerene-like nanoparticles for advanced technological applications

Novel isotactic polypropylene (iPP)/glass fiber (GF) laminates reinforced with inorganic fullerene-like tungsten disulfide (IF-WS2) nanoparticles as environmentally friendly fillers have been successfully fabricated by simple melt-blending and fiber impregnation in a hot-press without the addition of any compatibilizer. The influence of IF-WS2 concentration on the morphology, viscosity. and thermal and mechanical behavior of the hierarchical composites has been investigated. Results revealed an unprecedented 62 °C increase in the degradation temperature of iPP/GF upon addition of only 4.0 wt % IF-WS2. The coexistence of both micro- and nanoscale fillers resulted in synergistic effects on enhancing the stiffness, strength, crystallinity, thermal stability, glass transition (Tg) and heat distortion temperature (HDT) of the matrix. The approach used in this work is an efficient, versatile, scalable and economic strategy to improve the mechanical and thermal behavior of GF-reinforced thermoplastics with a view to extend their use in advanced technological applications. This new type of composite materials shows great potential to improve the efficiency and sustainability of many forms of transport.

Anisotropic magnetoresistive study in bilayer NiFe-NiO for sensor applications

Related with the detection of weak magnetic fields, the anisotropic magnetoresistive (AMR) effect is widely utilized in sensor applications. Exchange coupling between an antiferromagnet (AF) and the ferromagnet (FM) has been known as a significant parameter in the field sensitivity of magnetoresistance because of pinning effects on magnetic domain in FM layer by the bias field in AF. In this work we have studied the thermal evolution of the magnetization reversal processes in nanocrystalline exchange biased Ni80Fe20/Ni-O bilayers with large training effects and we report the anisotropic magnetoresistance ratio arising from field orientation in the bilayer.

E-Flow: A communication system for user notification in dynamic evacuation scenarios

Most of the current evacuation plans are based on static signaling, fixed monitoring infrastructure, and limited user notification and feedback mechanisms. These facts lead to lower situation awareness, in the case event of an emergency, such as blocked emergency exits, while delaying the reaction time of individuals. In this context, we introduce the E-Flow communication system, which improves the user awareness by integrating personal, mobile and fixed devices with the existing monitoring infrastructure. Our system broadens the notification and monitoring alternatives, in real time, among, safety staff, end-users and evacuation related devices, such as sensors and actuators.