Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Secure access control by means of human stress detection

This paper proposes a stress detection system based on fuzzy logic and the physiological signals heart rate and galvanic skin response. The main contribution of this method relies on the creation of a stress template, collecting the behaviour of previous signals under situations with a different level of stress in each individual. The creation of this template provides an accuracy of 99.5% in stress detection, improving the results obtained by current pattern recognition techniques like GMM, k-NN, SVM or Fisher Linear Discriminant. In addition, this system can be embedded in security systems to detect critical situations in accesses as cross-border control. Furthermore, its applications can be extended to other fields as vehicle driver state-of-mind management, medicine or sport training.

A comparative study on unconstrained hand biometrics

Biometrics applied to mobile devices are of great interest for security applications. Daily scenarios can benefit of a combination of both the most secure systems and most simple and extended devices. This document presents a hand biometric system oriented to mobile devices, proposing a non-intrusive, contact-less acquisition process where final users should take a picture of their hand in free-space with a mobile device without removals of rings, bracelets or watches. The main contribution of this paper is threefold: firstly, a feature extraction method is proposed, providing invariant hand measurements to previous changes; second contribution consists of providing a template creation based on hand geometric distances, requiring information from only one individual, without considering data from the rest of individuals within the database; finally, a proposal for template matching is proposed, minimizing the intra-class similarity and maximizing the inter-class likeliness. The proposed method is evaluated using three publicly available contact-less, platform-free databases. In addition, the results obtained with these databases will be compared to the results provided by two competitive pattern recognition techniques, namely Support Vector Machines (SVM) and k-Nearest Neighbour, often employed within the literature. Therefore, this approach provides an appropriate solution to adapt hand biometrics to mobile devices, with an accurate results and a non-intrusive acquisition procedure which increases the overall acceptance from the final user.

Unconstrained and Contactless Hand Geometry Biometrics

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices

Mejora de la calidad de los electrocardiogramas recogidos por terminales m��viles

Inicio del desarrollo de un algoritmo eficiente orientado a dispositivos con baja capacidad de proceso, que ayude a personas sin necesariamente una preparaci��n adecuada a llevar a cabo un proceso de toma de una se��al biol��gica, como puede ser un electrocardiograma. La aplicaci��n deber��, por tanto, asesorar en la toma de la se��al al usuario, evaluar la calidad de la grabaci��n obtenida, y en tiempo seudo real, comprobar si la calidad de la se��al obtenida es suficientemente buena para su posterior diagn��stico, de tal modo que en caso de que sea necesaria una repetici��n de la prueba m��dica, esta pueda realizarse de inmediato. Adem��s, el algoritmo debe extraer las caracter��sticas m��s relevantes de la se��al electrocardiogr��fica, procesarlas, y obtener una serie de patrones significativos que permitan la orientaci��n a la diagnosis de algunas de las patolog��as m��s comunes que se puedan extraer de la informaci��n de las se��ales card��acas. Para la extracci��n, evaluaci��n y toma de decisiones de este proceso previo a la generaci��n del diagn��stico, se seguir�� la arquitectura cl��sica de un sistema de detecci��n de patrones, definiendo las clases que sean necesarias seg��n el n��mero de patolog��as que se deseen identificar. Esta informaci��n de diagnosis, obtenida mediante la identificaci��n del sistema de reconocimiento de patrones, podr��a ser de ayuda u orientaci��n para la posterior revisi��n de la prueba por parte de un profesional m��dico cualificado y de manera remota, evitando as�� el desplazamiento del mismo a zonas donde, por los medios existentes a d��a de hoy, es muy remota la posibilidad de presencia de personal sanitario. ABTRACT Start of development of an efficient algorithm designed to devices with low processing power, which could help people without adequate preparation to undertake a process of taking a biological signal, such as an electrocardiogram. Therefore, the application must assist the user in taking the signal and evaluating the quality of the recording. All of this must to be in live time. It must to check the quality of the signal obtained, and if is it necessary a repetition of the test, this could be done immediately. Furthermore, the algorithm must extract the most relevant features of the ECG signal, process it, and get meaningful patterns that allow to a diagnosis orientation of some of the more common diseases that can be drawn from the cardiac signal information. For the extraction, evaluation and decision making in this previous process to the generation of diagnosis, we will follow the classic architecture of a pattern recognition system, defining the necessary classes according to the number of pathologies that we wish to identify. This diagnostic information obtained by identifying the pattern recognition system could be for help or guidance for further review of the signal by a qualified medical professional, and it could be done remotely, thus avoiding the movements to areas where nowadays it is extremely unlikely to place any health staff, due to the poor economic condition.

Detecci��n de patrones y anomal��as espectrales del terreno mediante espectrometr��a de imagen de alta resoluci��n : reconocimiento, optimizaci��n y evaluaci��n 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.

Image analysis by moment invariants using a set of step-like basis functions

Moment invariants have been thoroughly studied and repeatedly proposed as one of the most powerful tools for 2D shape identification. In this paper a set of such descriptors is proposed, being the basis functions discontinuous in a finite number of points. The goal of using discontinuous functions is to avoid the Gibbs phenomenon, and therefore to yield a better approximation capability for discontinuous signals, as images. Moreover, the proposed set of moments allows the definition of rotation invariants, being this the other main design concern. Translation and scale invariance are achieved by means of standard image normalization. Tests are conducted to evaluate the behavior of these descriptors in noisy environments, where images are corrupted with Gaussian noise up to different SNR values. Results are compared to those obtained using Zernike moments, showing that the proposed descriptor has the same performance in image retrieval tasks in noisy environments, but demanding much less computational power for every stage in the query chain.

Smart aeronautical structures: development and experimental validation of a structural health monitoring system for damage detection

En muchas ��reas de la ingenier��a, la integridad y confiabilidad de las estructuras son aspectos de extrema importancia. Estos son controlados mediante el adecuado conocimiento de danos existentes. T��picamente, alcanzar el nivel de conocimiento necesario que permita caracterizar la integridad estructural implica el uso de t��cnicas de ensayos no destructivos. Estas t��cnicas son a menudo costosas y consumen mucho tiempo. En la actualidad, muchas industrias buscan incrementar la confiabilidad de las estructuras que emplean. Mediante el uso de t��cnicas de ��ltima tecnolog��a es posible monitorizar las estructuras y en algunos casos, es factible detectar da��os incipientes que pueden desencadenar en fallos catastr��ficos. Desafortunadamente, a medida que la complejidad de las estructuras, los componentes y sistemas incrementa, el riesgo de la aparici��n de da��os y fallas tambi��n incrementa. Al mismo tiempo, la detecci��n de dichas fallas y defectos se torna m��s compleja. En a��os recientes, la industria aeroespacial ha realizado grandes esfuerzos para integrar los sensores dentro de las estructuras, adem��s de desarrollar algoritmos que permitan determinar la integridad estructural en tiempo real. Esta filosof��a ha sido llamada ���Structural Health Monitoring��� (o ���Monitorizaci��n de Salud Estructural��� en espa��ol) y este tipo de estructuras han recibido el nombre de ���Smart Structures��� (o ���Estructuras Inteligentes��� en espa��ol). Este nuevo tipo de estructuras integran materiales, sensores, actuadores y algoritmos para detectar, cuantificar y localizar da��os dentro de ellas mismas. Una novedosa metodolog��a para detecci��n de da��os en estructuras se propone en este trabajo. La metodolog��a est�� basada en mediciones de deformaci��n y consiste en desarrollar t��cnicas de reconocimiento de patrones en el campo de deformaciones. Estas ��ltimas, basadas en PCA (An��lisis de Componentes Principales) y otras t��cnicas de reducci��n dimensional. Se propone el uso de Redes de difracci��n de Bragg y medidas distribuidas como sensores de deformaci��n. La metodolog��a se valid�� mediante pruebas a escala de laboratorio y pruebas a escala real con estructuras complejas. Los efectos de las condiciones de carga variables fueron estudiados y diversos experimentos fueron realizados para condiciones de carga est��ticas y din��micas, demostrando que la metodolog��a es robusta ante condiciones de carga desconocidas. ABSTRACT In many engineering fields, the integrity and reliability of the structures are extremely important aspects. They are controlled by the adequate knowledge of existing damages. Typically, achieving the level of knowledge necessary to characterize the structural integrity involves the usage of nondestructive testing techniques. These are often expensive and time consuming. Nowadays, many industries look to increase the reliability of the structures used. By using leading edge techniques it is possible to monitoring these structures and in some cases, detect incipient damage that could trigger catastrophic failures. Unfortunately, as the complexity of the structures, components and systems increases, the risk of damages and failures also increases. At the same time, the detection of such failures and defects becomes more difficult. In recent years, the aerospace industry has done great efforts to integrate the sensors within the structures and, to develop algorithms for determining the structural integrity in real time. The ���philosophy��� has being called ���Structural Health Monitoring��� and these structures have been called ���smart structures���. These new types of structures integrate materials, sensors, actuators and algorithms to detect, quantify and locate damage within itself. A novel methodology for damage detection in structures is proposed. The methodology is based on strain measurements and consists in the development of strain field pattern recognition techniques. The aforementioned are based on PCA (Principal Component Analysis) and other dimensional reduction techniques. The use of fiber Bragg gratings and distributed sensing as strain sensors is proposed. The methodology have been validated by using laboratory scale tests and real scale tests with complex structures. The effects of the variable load conditions were studied and several experiments were performed for static and dynamic load conditions, demonstrating that the methodology is robust under unknown load conditions.

Speed-independent gait identification for mobile devices

Due to the intensive use of mobile phones for diferent purposes, these devices usually contain condential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner s gait. Nowadays, gait identication in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identication problem, they show di��culties when dealing with gaits at diferent speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at diferent velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at diferent motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

Sistema de autenticaci��n accesible para el hogar digital

El Hogar Digital Accesible (HDA) de la ETSIST nace con el prop��sito de acercar las nuevas Tecnolog��as de la Informaci��n a las personas que precisan de necesidades concretas de accesibilidad y usabilidad, dot��ndoles de herramientas que les permitan aumentar su calidad de vida, confort, seguridad y autonom��a. El entorno del HDA consta de elementos de control para puertas, persianas, iluminaci��n, agua o gas, sensores de temperatura, incendios, gas, sistemas de climatizaci��n, sistemas de entretenimiento y sistemas de seguridad tales como detectores de presencia y alarmas. Todo ello apoyado sobre una arquitectura de red que proporciona una pasarela residencial y un acceso a banda ancha. El objetivo principal de este PFG ha sido el desarrollo de un sistema de autenticaci��n para el Hogar Digital Accesible de bajo coste. La idea de integrar un sistema de autenticaci��n en el HDA, surge de la necesidad de proteger de accesos no deseados determinados servicios disponibles dentro de un ��mbito privado. Algunos de estos servicios pueden ser tales como el acceso a la lectura de los mensajes disponibles en el contestador autom��tico, el uso de equipos multimedia, la desconexi��n de alarmas de seguridad o simplemente la configuraci��n de ambientes seg��n el usuario que est�� autenticado (intensidad de luz, temperatura de la sala, etc.). En el desarrollo han primado los principios de accesibilidad, usabilidad y seguridad necesarios para la creaci��n de un entorno no invasivo, que permitiera acreditar la identidad del usuario frente al sistema HDA. Se ha planteado como posible soluci��n, un sistema basado en el reconocimiento de un trazo realizado por el usuario. Este trazo se usar�� como clave de cara a validar a los usuarios. El usuario deber�� repetir el trazado que registr�� en el sistema para autenticarse. Durante la ejecuci��n del presente PFG, se justificar�� la elecci��n de este mecanismo de autenticaci��n frente a otras alternativas disponibles en el mercado. Para probar la aplicaci��n, se ha podido contar con dos perif��ricos de distintas gamas, el uDraw creado para la PS3 que se compone de una tableta digitalizadora y un l��piz que permite recoger los trazos realizados por el usuario de forma inal��mbrica y la tableta digitalizadora Bamboo de Wacom. La herramienta desarrollada permite a su vez, la posibilidad de ser usada por otro tipo de dispositivos como es el caso del reloj con aceler��metro de 3 ejes de Texas Instruments Chronos eZ430 capaz de trasladar los movimientos del usuario al puntero de un rat��n. El PFG se encuentra dividido en tres grandes bloques de flujo de trabajo. El primero se centra en el an��lisis del sistema y las tecnolog��as que lo componen, incluyendo los distintos algoritmos disponibles para realizar la autenticaci��n basada en reconocimiento de patrones aplicados a im��genes que mejor se adaptan a las necesidades del usuario. En el segundo bloque se recoge una versi��n de prueba basada en el an��lisis y el dise��o UML realizado previamente, sobre la que se efectuaron pruebas de concepto y se comprob�� la viabilidad del proyecto. El ��ltimo bloque incluye la verificaci��n y validaci��n del sistema mediante pruebas que certifican que se han alcanzado los niveles de calidad necesarios para la consecuci��n de los objetivos planteados, generando finalmente la documentaci��n necesaria. Como resultado del trabajo realizado, se ha obtenido un sistema que plantea una arquitectura f��cilmente ampliable lograda a trav��s del uso de t��cnicas como la introspecci��n, que permiten separar la l��gica de la capa de negocio del c��digo que la implementa, pudiendo de forma simple e intuitiva sustituir c��digo mediante ficheros de configuraci��n, lo que hace que el sistema sea flexible y escalable. Tras la realizaci��n del PFG, se puede concluir que el producto final obtenido ha respondido de forma satisfactoria alcanzando los niveles de calidad requeridos, siendo capaz de proporcionar un sistema de autenticaci��n alternativo a los convencionales, manteniendo unas cotas de seguridad elevadas y haciendo de la accesibilidad y el precio sus caracter��sticas m��s rese��ables. ABSTRACT. Accessible Digital Home (HDA) of the ETSIST was created with the aim of bringing the latest information and communications technologies closer to the people who has special needs of accessibility and usability increasing their quality of life, comfort, security and autonomy. The HDA environment has different control elements for doors, blinds, lighting, water or gas, temperature sensors, fire protection systems, gas flashover, air conditioning systems, entertainments systems and security systems such as intruders detectors and alarms. Everything supported by an architecture net which provides a broadband residential services gateway. The main goal of this PFG was the development of a low-cost authentication system for the Accessible Digital Home. The idea of integrating an authentication system on the HDA, stems from the need to safeguard certain private key network resources from unauthorized access. Some of said resources are the access to the answering machine messages, the use of multimedia devices, the alarms deactivation or the parameter settings for each environment as programmed by the authenticated user (light intensity, room temperature, etc.). During the development priority was given to concepts like accessibility, usability and security. All of them necessary to create a non invasive environment that allows the users to certify their identity. A system based on stroke pattern recognition, was considered as a possible solution. This stroke is used as a key to validate users. The user must repeat the stroke that was saved on the system to validate access. The selection of this authentication mechanism among the others available options will be justified during this PFG. Two peripherals with different ranges were used to test the application. One of them was uDraw design for the PS3. It is wireless and is formed by a pen and a drawing tablet that allow us to register the different strokes drawn by the user. The other one was the Wacom Bamboo tablet, that supports the same functionality but with better accuracy. The developed tool allows another kind of peripherals like the 3-axes accelerometer digital wristwatch Texas Instruments Chronos eZ430 capable of transfering user movements to the mouse cursor. The PFG is divided by three big blocks that represent different workflows. The first block is focused on the system analysis and the technologies related to it, including algorithms for image pattern recognition that fits the user's needs. The second block describes how the beta version was developed based on the UML analysis and design previously done. It was tested and the viability of the project was verified. The last block contains the system verification and validation. These processes certify that the requirements have been fulfilled as well as the quality levels needed to reach the planned goals. Finally all the documentation has been produced. As a result of the work, an expandable system has been created, due to the introspection that provides the opportunity to separate the business logic from the code that implements it. With this technique, the code could be replaced throughout configuration files which makes the system flexible and highly scalable. Once the PFG has finished, it must therefore be concluded that the final product has been a success and high levels of quality have been achieved. This authentication tool gives us a low-cost alternative to the conventional ones. The new authentication system remains security levels reasonably high giving particular emphasis to the accessibility and the price.

Anomaly Detection Using Remote Sensing for the Archaeological Heritage Registration

The aim of this work is an approach using multisensor remote sensing techniques to recognize the potential remains and recreate the original landscape of three archaeological sites. We investigate the spectral characteristics of the reflectance parameter and emissivity in the pattern recognition of archaeological materials in several hyperspectral scenes of the prehispanic site in Palmar Sur (Costa Rica), the Jarama Valley site and the celtiberian city of Segeda in Spain. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of HyMAP, AHS, MASTER and ATM have been used. Several experiments on natural scenarios of Costa Rica and Spain of different complexity, have been designed. Spectral patterns and thermal anomalies have been calculated as evidences of buried remains and change detection. First results, land cover change analyses and their consequences in the digital heritage registration are discussed.

Aplicaci��n de t��cnicas de inteligencia artificial para contribuir en la detecci��n de microcalcificaciones en mamograf��a digitalizada

A nivel mundial, el c��ncer de mama es el tipo de c��ncer m��s frecuente adem��s de una de las principales causas de muerte entre la poblaci��n femenina. Actualmente, el m��todo m��s eficaz para detectar lesiones mamarias en una etapa temprana es la mamograf��a. ��sta contribuye decisivamente al diagn��stico precoz de esta enfermedad que, si se detecta a tiempo, tiene una probabilidad de curaci��n muy alta. Uno de los principales y m��s frecuentes hallazgos en una mamograf��a, son las microcalcificaciones, las cuales son consideradas como un indicador importante de c��ncer de mama. En el momento de analizar las mamograf��as, factores como la capacidad de visualizaci��n, la fatiga o la experiencia profesional del especialista radi��logo hacen que el riesgo de omitir ciertas lesiones presentes se vea incrementado. Para disminuir dicho riesgo es importante contar con diferentes alternativas como por ejemplo, una segunda opini��n por otro especialista o un doble an��lisis por el mismo. En la primera opci��n se eleva el coste y en ambas se prolonga el tiempo del diagn��stico. Esto supone una gran motivaci��n para el desarrollo de sistemas de apoyo o asistencia en la toma de decisiones. En este trabajo de tesis se propone, se desarrolla y se justifica un sistema capaz de detectar microcalcificaciones en regiones de inter��s extra��das de mamograf��as digitalizadas, para contribuir a la detecci��n temprana del c��ncer demama. Dicho sistema estar�� basado en t��cnicas de procesamiento de imagen digital, de reconocimiento de patrones y de inteligencia artificial. Para su desarrollo, se tienen en cuenta las siguientes consideraciones: 1. Con el objetivo de entrenar y probar el sistema propuesto, se crear�� una base de datos de im��genes, las cuales pertenecen a regiones de inter��s extra��das de mamograf��as digitalizadas. 2. Se propone la aplicaci��n de la transformada Top-Hat, una t��cnica de procesamiento digital de imagen basada en operaciones de morfolog��a matem��tica. La finalidad de aplicar esta t��cnica es la de mejorar el contraste entre las microcalcificaciones y el tejido presente en la imagen. 3. Se propone un algoritmo novel llamado sub-segmentaci��n, el cual est�� basado en t��cnicas de reconocimiento de patrones aplicando un algoritmo de agrupamiento no supervisado, el PFCM (Possibilistic Fuzzy c-Means). El objetivo es encontrar las regiones correspondientes a las microcalcificaciones y diferenciarlas del tejido sano. Adem��s, con la finalidad de mostrar las ventajas y desventajas del algoritmo propuesto, ��ste es comparado con dos algoritmos del mismo tipo: el k-means y el FCM (Fuzzy c-Means). Por otro lado, es importante destacar que en este trabajo por primera vez la sub-segmentaci��n es utilizada para detectar regiones pertenecientes a microcalcificaciones en im��genes de mamograf��a. 4. Finalmente, se propone el uso de un clasificador basado en una red neuronal artificial, espec��ficamente un MLP (Multi-layer Perceptron). El prop��sito del clasificador es discriminar de manera binaria los patrones creados a partir de la intensidad de niveles de gris de la imagen original. Dicha clasificaci��n distingue entre microcalcificaci��n y tejido sano. ABSTRACT Breast cancer is one of the leading causes of women mortality in the world and its early detection continues being a key piece to improve the prognosis and survival. Currently, the most reliable and practical method for early detection of breast cancer is mammography.The presence of microcalcifications has been considered as a very important indicator ofmalignant types of breast cancer and its detection and classification are important to prevent and treat the disease. However, the detection and classification of microcalcifications continue being a hard work due to that, in mammograms there is a poor contrast between microcalcifications and the tissue around them. Factors such as visualization, tiredness or insufficient experience of the specialist increase the risk of omit some present lesions. To reduce this risk, is important to have alternatives such as a second opinion or a double analysis for the same specialist. In the first option, the cost increases and diagnosis time also increases for both of them. This is the reason why there is a great motivation for development of help systems or assistance in the decision making process. This work presents, develops and justifies a system for the detection of microcalcifications in regions of interest extracted fromdigitizedmammographies to contribute to the early detection of breast cancer. This systemis based on image processing techniques, pattern recognition and artificial intelligence. For system development the following features are considered: With the aim of training and testing the system, an images database is created, belonging to a region of interest extracted from digitized mammograms. The application of the top-hat transformis proposed. This image processing technique is based on mathematical morphology operations. The aim of this technique is to improve the contrast betweenmicrocalcifications and tissue present in the image. A novel algorithm called sub-segmentation is proposed. The sub-segmentation is based on pattern recognition techniques applying a non-supervised clustering algorithm known as Possibilistic Fuzzy c-Means (PFCM). The aim is to find regions corresponding to the microcalcifications and distinguish them from the healthy tissue. Furthermore,with the aim of showing themain advantages and disadvantages this is compared with two algorithms of same type: the k-means and the fuzzy c-means (FCM). On the other hand, it is important to highlight in this work for the first time the sub-segmentation is used for microcalcifications detection. Finally, a classifier based on an artificial neural network such as Multi-layer Perceptron is used. The purpose of this classifier is to discriminate froma binary perspective the patterns built from gray level intensity of the original image. This classification distinguishes between microcalcifications and healthy tissue.

Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance ��� at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.