Debonding identification in FRP-plated RC structures using PZT sensors

The application of the Electro-Mechanical Impedance (EMI) method for damage detection in Structural Health Monitoring has noticeable increased in recent years. EMI method utilizes piezoelectric transducers for directly measuring the mechanical properties of the host structure, obtaining the so called impedance measurement, highly influenced by the variations of dynamic parameters of the structure. These measurements usually contain a large number of frequency points, as well as a high number of dimensions, since each frequency range swept can be considered as an independent variable. That makes this kind of data hard to handle, increasing the computational costs and being substantially time-consuming. In that sense, the Principal Component Analysis (PCA)-based data compression has been employed in this work, in order to enhance the analysis capability of the raw data. Furthermore, a Support Vector Machine (SVM), which has been widespread used in machine learning and pattern recognition fields, has been applied in this study in order to model any possible existing pattern in the PCAcompress data, using for that just the first two Principal Components. Different known non-damaged and damaged measurements of an experimental tested beam were used as training input data for the SVM algorithm, using as test input data the same amount of cases measured in beams with unknown structural health conditions. Thus, the purpose of this work is to demonstrate how, with a few impedance measurements of a beam as raw data, its healthy status can be determined based on pattern recognition procedures.

Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.

Functional characterization of YODA, a mitogen-activated protein kinase kinase kinase (MAP3K) that regulates a novel innate immunity pathway in Arabidopsis thaliana

Las cascadas de señalización mediadas por proteína quinasas activadas por mitógeno (MAP quinasas) son capaces de integrar y transducir señales ambientales en respuestas celulares. Entre estas señales se encuentran los PAMPs/MAMPs (Pathogen/Microbe-Associated Molecular Patterns), que son moléculas de patógenos o microorganismos, o los DAMPs (Damaged-Associated Molecular Patterns), que son moléculas derivadas de las plantas producidas en respuesta a daño celular. Tras el reconocimiento de los PAMPs/DAMPs por receptores de membrana denominados PRRs (Pattern Recognition Receptors), como los receptores con dominio quinasa (RLKs) o los receptores sin dominio quinasa (RLPs), se activan respuestas moleculares, incluidas cascadas de MAP quinasas, que regulan la puesta en marcha de la inmunidad activada por PAMPs (PTI). Esta Tesis describe la caracterización funcional de la MAP quinasa quinasa quinasa (MAP3K) YODA (YDA), que actúa como un regulador clave de la PTI en Arabidopsis. Se ha descrito previamente que YDA controla varios procesos de desarrollo, como la regulación del patrón estomático, la elongación del zigoto y la arquitectura floral. Hemos caracterizado un alelo mutante hipomórfico de YDA (elk2 o yda11) que presenta una elevada susceptibilidad a patógenos biótrofos y necrótrofos. Notablemente, plantas que expresan una forma constitutivamente activa de YDA (CA-YDA), con una deleción en el dominio N-terminal, presentan una resistencia de amplio espectro frente a diferentes tipos de patógenos, incluyendo hongos, oomicetos y bacterias, lo que indica que YDA juega un papel importante en la regulación de la resistencia de las plantas a patógenos. Nuestros datos indican que esta función es independiente de las respuestas inmunes mediadas por los receptores previamente caracterizados FLS2 y CERK1, que reconocen los PAMPs flg22 y quitina, respectivamente, y que están implicados en la resistencia de Arabidopsis frente a bacterias y hongos. Hemos demostrado que YDA controla la resistencia frente al hongo necrótrofo Plectosphaerella cucumerina y el patrón estomático mediante su interacción genética con la RLK ERECTA (ER), un PRR implicado en la regulación de estos procesos. Por el contrario, la interacción genética entre ER y YDA en la regulación de otros procesos de desarrollo es aditiva en lugar de epistática. Análisis genéticos indicaron que MPK3, una MAP quinasa que funciona aguas abajo de YDA en el desarrollo estomático, es un componente de la ruta de señalización mediada por YDA para la resistencia frente a P. cucumerina, lo que sugiere que el desarrollo de las plantas y la PTI comparten el módulo de transducción de MAP quinasas asociado a YDA. Nuestros experimentos han revelado que la resistencia mediada por YDA es independiente de las rutas de señalización reguladas por las hormonas de defensa ácido salicílico, ácido jasmónico, ácido abscísico o etileno, y también es independiente de la ruta de metabolitos secundarios derivados del triptófano, que están implicados en inmunidad vegetal. Además, hemos demostrado que respuestas asociadas a PTI, como el aumento en la concentración de calcio citoplásmico, la producción de especies reactivas de oxígeno, la fosforilación de MAP quinasas y la expresión de genes de defensa, no están afectadas en el mutante yda11. La expresión constitutiva de la proteína CA-YDA en plantas de Arabidopsis no provoca un aumento de las respuestas PTI, lo que sugiere la existencia de mecanismos de resistencia adicionales regulados por YDA que son diferentes de los regulados por FLS2 y CERK1. En línea con estos resultados, nuestros datos transcriptómicos revelan una sobre-representación en plantas CA-YDA de genes de defensa que codifican, por ejemplo, péptidos antimicrobianos o reguladores de muerte celular, o proteínas implicadas en la biogénesis de la pared celular, lo que sugiere una conexión potencial entre la composición e integridad de la pared celular y la resistencia de amplio espectro mediada por YDA. Además, análisis de fosfoproteómica indican la fosforilación diferencial de proteínas relacionadas con la pared celular en plantas CA-YDA en comparación con plantas silvestres. El posible papel de la ruta ER-YDA en la regulación de la integridad de la pared celular está apoyado por análisis bioquímicos y glicómicos de las paredes celulares de plantas er, yda11 y CA-YDA, que revelaron cambios significativos en la composición de la pared celular de estos genotipos en comparación con la de plantas silvestres. En resumen, nuestros datos indican que ER y YDA forman parte de una nueva ruta de inmunidad que regula la integridad de la pared celular y respuestas defensivas, confiriendo una resistencia de amplio espectro frente a patógenos. ABSTRACT Plant mitogen-activated protein kinase (MAPK) cascades transduce environmental signals and developmental cues into cellular responses. Among these signals are the pathogen- or microbe-associated molecular patterns (PAMPs or MAMPs) and the damage-associated molecular patterns (DAMPs). These PAMPs/DAMPs, upon recognition by plant pattern recognition receptors (PRRs), such as Receptor-Like Kinases (RLKs) and Receptor-Like Proteins (RLPs), activate molecular responses, including MAPK cascades, which regulate the onset of PAMP-triggered immunity (PTI). This Thesis describes the functional characterization of the MAPK kinase kinase (MAP3K) YODA (YDA) as a key regulator of Arabidopsis PTI. YDA has been previously described to control several developmental processes, such as stomatal patterning, zygote elongation and inflorescence architecture. We characterized a hypomorphic, non-embryo lethal mutant allele of YDA (elk2 or yda11) that was found to be highly susceptible to biotrophic and necrotrophic pathogens. Remarkably, plants expressing a constitutive active form of YDA (CA-YDA), with a deletion in the N-terminal domain, showed broad-spectrum resistance to different types of pathogens, including fungi, oomycetes and bacteria, indicating that YDA plays a relevant function in plant resistance to pathogens. Our data indicated that this function is independent of the immune responses regulated by the well characterized FLS2 and CERK1 RLKs, which are the PRRs recognizing flg22 and chitin PAMPs, respectively, and are required for Arabidopsis resistance to bacteria and fungi. We demonstrate that YDA controls resistance to the necrotrophic fungus Plectosphaerella cucumerina and stomatal patterning by genetically interacting with ERECTA (ER) RLK, a PRR involved in regulating these processes. In contrast, the genetic interaction between ER and YDA in the regulation of other ER-associated developmental processes was additive, rather than epistatic. Genetic analyses indicated that MPK3, a MAP kinase that functions downstream of YDA in stomatal development, also regulates plant resistance to P. cucumerina in a YDA-dependent manner, suggesting that the YDA-associated MAPK transduction module is shared in plant development and PTI. Our experiments revealed that YDA-mediated resistance was independent of signalling pathways regulated by defensive hormones like salicylic acid, jasmonic acid, abscisic acid or ethylene, and of the tryptophan-derived metabolites pathway, which are involved in plant immunity. In addition, we showed that PAMP-mediated PTI responses, such as the increase of cytoplasmic Ca2+ concentration, reactive oxygen species (ROS) burst, MAPK phosphorylation, and expression of defense-related genes are not impaired in the yda11 mutant. Furthermore, the expression of CA-YDA protein does not result in enhanced PTI responses, further suggesting the existence of additional mechanisms of resistance regulated by YDA that differ from those regulated by the PTI receptors FLS2 and CERK1. In line with these observations, our transcriptomic data revealed the over-representation in CA-YDA plants of defensive genes, such as those encoding antimicrobial peptides and cell death regulators, and genes encoding cell wall-related proteins, suggesting a potential link between plant cell wall composition and integrity and broad spectrum resistance mediated by YDA. In addition, phosphoproteomic data revealed an over-representation of genes encoding wall-related proteins in CA-YDA plants in comparison with wild-type plants. The putative role of the ER-YDA pathway in regulating cell wall integrity was further supported by biochemical and glycomics analyses of er, yda11 and CA-YDA cell walls, which revealed significant changes in the cell wall composition of these genotypes compared with that of wild-type plants. In summary, our data indicate that ER and YDA are components of a novel immune pathway that regulates cell wall integrity and defensive responses, which confer broad-spectrum resistance to pathogens.

Cognitive Surveillance Architecture for Scenario Understanding

Uno de los mayores retos para la comunidad científica es conseguir que las máquinas posean en un futuro la capacidad del sistema visual y cognitivo humanos, de forma que, por ejemplo, en entornos de video vigilancia, puedan llegar a proporcionar de manera automática una descripción fiable de lo que está ocurriendo en la escena. En la presente tesis, mediante la propuesta de un marco de trabajo de referencia, se discuten y plantean los pasos necesarios para el desarrollo de sistemas más inteligentes capaces de extraer y analizar, a diferentes niveles de abstracción y mediante distintos módulos de procesamiento independientes, la información necesaria para comprender qué está sucediendo en un conjunto amplio de escenarios de distinta naturaleza. Se parte de un análisis de requisitos y se identifican los retos para este tipo de sistemas en la actualidad, lo que constituye en sí mismo los objetivos de esta tesis, contribuyendo así a un modelo de datos basado en el conocimiento que permitirá analizar distintas situaciones en las que personas y vehículos son los actores principales, dejando no obstante la puerta abierta a la adaptación a otros dominios. Así mismo, se estudian los distintos procesos que se pueden lanzar a nivel interno así como la necesidad de integrar mecanismos de realimentación a distintos niveles que permitan al sistema adaptarse mejor a cambios en el entorno. Como resultado, se propone un marco de referencia jerárquico que integra las capacidades de percepción, interpretación y aprendizaje para superar los retos identificados en este ámbito; y así poder desarrollar sistemas de vigilancia más robustos, flexibles e inteligentes, capaces de operar en una variedad de entornos. Resultados experimentales ejecutados sobre distintas muestras de datos (secuencias de vídeo principalmente) demuestran la efectividad del marco de trabajo propuesto respecto a otros propuestos en el pasado. Un primer caso de estudio, permite demostrar la creación de un sistema de monitorización de entornos de parking en exteriores para la detección de vehículos y el análisis de plazas libres de aparcamiento. Un segundo caso de estudio, permite demostrar la flexibilidad del marco de referencia propuesto para adaptarse a los requisitos de un entorno de vigilancia completamente distinto, como es un hogar inteligente donde el análisis automático de actividades de la vida cotidiana centra la atención del estudio. ABSTRACT One of the most ambitious objectives for the Computer Vision and Pattern Recognition research community is that machines can achieve similar capacities to the human's visual and cognitive system, and thus provide a trustworthy description of what is happening in the scene under surveillance. Thus, a number of well-established scenario understanding architectural frameworks to develop applications working on a variety of environments can be found in the literature. In this Thesis, a highly descriptive methodology for the development of scene understanding applications is presented. It consists of a set of formal guidelines to let machines extract and analyse, at different levels of abstraction and by means of independent processing modules that interact with each other, the necessary information to understand a broad set of different real World surveillance scenarios. Taking into account the challenges that working at both low and high levels offer, we contribute with a highly descriptive knowledge-based data model for the analysis of different situations in which people and vehicles are the main actors, leaving the door open for the development of interesting applications in diverse smart domains. Recommendations to let systems achieve high-level behaviour understanding will be also provided. Furthermore, feedback mechanisms are proposed to be integrated in order to let any system to understand better the environment and the logical context around, reducing thus the uncertainty and noise, and increasing its robustness and precision in front of low-level or high-level errors. As a result, a hierarchical cognitive architecture of reference which integrates the necessary perception, interpretation, attention and learning capabilities to overcome main challenges identified in this area of research is proposed; thus allowing to develop more robust, flexible and smart surveillance systems to cope with the different requirements of a variety of environments. Once crucial issues that should be treated explicitly in the design of this kind of systems have been formulated and discussed, experimental results shows the effectiveness of the proposed framework compared with other proposed in the past. Two case studies were implemented to test the capabilities of the framework. The first case study presents how the proposed framework can be used to create intelligent parking monitoring systems. The second case study demonstrates the flexibility of the system to cope with the requirements of a completely different environment, a smart home where activities of daily living are performed. Finally, general conclusions and future work lines to further enhancing the capabilities of the proposed framework are presented.