Non-Destructive Global and Localized 2D T1/T2 NMR Relaxometry to Resolve Microstructure in Apples Affected by Watercore.

Apples can be considered as having a complex system formed by several structures at different organization levels: macroscale (mayor que100 ?m) and microscale (menor que100 ?m). This work implements 2D T1/T2 global and localized relaxometry sequences on whole apples to be able to perform an intensive non-destructive and non-invasive microstructure study. The 2D T1/T2 cross-correlation spectroscopy allows the extraction of quantitative information about the water compartmentation in different subcellular organelles. A clear difference is found as sound apples show neat peaks for water in different subcellular compartments, such as vacuolar, cytoplasmatic and extracellular water, while in watercore-affected tissues such compartments appear merged. Localized relaxometry allows for the predefinition of slices in order to understand the microstructure of a particular region of the fruit, providing information that cannot be derived from global 2D T1/T2 relaxometry.

Desing and development of a robotic platform for the evaluation of search algorithms

Plant diseases represent a major economic and environmental problem in agriculture and forestry. Upon infection, a plant develops symptoms that affect different parts of the plant causing a significant agronomic impact. As many such diseases spread in time over the whole crop, a system for early disease detection can aid to mitigate the losses produced by the plant diseases and can further prevent their spread [1]. In recent years, several mathematical algorithms of search have been proposed [2,3] that could be used as a non-invasive, fast, reliable and cost-effective methods to localize in space infectious focus by detecting changes in the profile of volatile organic compounds. Tracking scents and locating odor sources is a major challenge in robotics, on one hand because odour plumes consists of non-uniform intermittent odour patches dispersed by the wind and on the other hand because of the lack of precise and reliable odour sensors. Notwithstanding, we have develop a simple robotic platform to study the robustness and effectiveness of different search algorithms [4], with respect to specific problems to be found in their further application in agriculture, namely errors committed in the motion and sensing and to the existence of spatial constraints due to land topology or the presence of obstacles.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Non-Destructive Global and Localized 2D T1/T2 NMR Relaxometry to Resolve Microstructure in Apples Affected by Watercore

Apples can be considered as having a complex system formed by several structures at different organization levels: macroscale (>100 μm) and microscale (<100 μm). This work implements 2D T1/T2 global and localized relaxometry sequences on whole apples to be able to perform an intensive non-destructive and non-invasive microstructure study. The 2D T1/T2 cross-correlation spectroscopy allows the extraction of quantitative information about the water compartmentation in different subcellular organelles. A clear difference is found as sound apples show neat peaks for water in different subcellular compartments, such as vacuolar, cytoplasmatic and extracellular water, while in watercore-affected tissues such compartments appear merged. Localized relaxometry allows for the predefinition of slices in order to understand the microstructure of a particular region of the fruit, providing information that cannot be derived from global 2D T1/T2 relaxometry.

Determinación de la amplificación sísmica local en el Campus Sur UPM mediante la técnica de sismología de microtremores ReMi.

En el Campus Sur de la Universidad Politécnica de Madrid se ha llevado a cabo un proyecto para obtener una caracterización del subsuelo mediante ensayos ReMi, en colaboración con el departamento de Geofísica del Instituto Geográfico Nacional. La técnica ReMi (Refraction Microtremor) permite, mediante ensayos geofísicos realizados localmente sobre el terreno,obtener los parámetros físicos del mismo, que resultan de especial interés en el ámbito de la ingeniería civil. Esta técnica se caracteriza por englobarse dentro de la sísmica pasiva, muy empleada en prospección geofísica y basada en la obtención del modelo subyacente de distribución de velocidades de propagación de la onda S en función de la profundidad, con la ventaja de aprovechar el ruido sísmico ambiental como fuente de energía. Fue desarrollada en el Laboratorio Sismológico de Nevada (EEUU) por Louie (2001), con el objetivo de presentar una técnica innovadora en la obtención de las velocidades de propagación de manera experimental. Presenta ciertas ventajas, como la observación directa de la dispersión de ondas superficiales,que da un buen resultado de la velocidad de onda S, siendo un método no invasivo, de bajo coste y buena resolución, aplicable en entornos urbanos o sensibles en los que tanto otras técnicas sismológicas como otras variedades de prospección presentan dificultades. La velocidad de propagación de la onda S en los 30 primeros metros VS30, es ampliamente reconocida como un parámetro equivalente válido para caracterizar geotécnicamente el subsuelo y se halla matemáticamente relacionada con la velocidad de propagación de las ondas superficiales a observar mediante la técnica ReMi. Su observación permite el análisis espectral de los registros adquiridos, obteniéndose un modelo representado por la curva de dispersión de cada emplazamiento, de modo que mediante una inversión se obtiene el modelo de velocidad de propagación en función de la profundidad. A través de estos modelos, pueden obtenerse otros parámetros de interés sismológico. Estos resultados se representan sobre mapas isométricos para obtener una relación espacial de los mismos, particularmente conocido como zonación sísmica. De este análisis se extrae que la VS30 promedio del Campus no es baja en exceso, correspondiéndose a posteriori con los resultados de amplificación sísmica, período fundamental de resonancia del lugar y profundidad del sustrato rocoso. En última instancia se comprueba que los valores de amplificación sísmica máxima y el período al cual se produce posiblemente coincidan con los períodos fundamentales de resonancia de algunos edificios del Campus. ABSTRACT In South Campus at Polytechnic University of Madrid, a project has been carried out to obtain a proper subsoil description by applying ReMi tests, in collaboration with the Department of Geophysics of the National Geographic Institute. Through geophysical tests conducted locally, the ReMi (Refraction Microtremor) technique allows to establish the physical parameters of soil, which are of special interest in the field of civil engineering. This technique is part of passive seismic methods, often used in geophysical prospecting. It focuses in obtaining the underlying model of propagation velocity distribution of the shear wave according to depth and has the advantage of being able to use seismic ambient noise as a source of energy. It was developed in the Nevada Seismological Laboratory (USA) by Louie (2001) as an innovative technique for obtaining propagation velocities experimentally. It has several other advantages, including the direct observation of the dispersion of surface waves, which allows to reliably measure S wave velocity. This is a non-invasive, low cost and good resolution method, which can be applied in urban or sensitive environments where other prospection methods present difficulties. The propagation velocity of shear waves in the first 30 meters Vs30 is widely recognized as a valid equivalent parameter to geotechnically characterize the subsurface. It is mathematically related to surface wave's velocity of propagation, which are to observe using REMI technique. Spectral analysis of acquired data sets up a model represented by the dispersion curve at each site, so that, using an inversion process, propagation velocity model in relation to depth is obtained. Through this models, other seismologically interesting parameters can be obtained. These results are represented on isometric maps in order to obtain a spatial relationship between them, a process which is known as seismic zonation. This analysis infers that Vs30 at South Campus is not alarmingly low , corresponding with subsequent results of seismic amplification, fundamental period of resonance of soil and depth of bedrock. Ultimately, it's found that calculated values of soil's fundamental periods at which maximum seismic amplification occurs, may possibly match fundamental periods of some Campus buildings.

Some recent applications of non-linear models to the engineering practice

The Department of Structural Analysis of the University of Santander has been for a longtime involved in the solution of the country´s practical engineering problems. Some of these have required the use of non-conventional methods of analysis, in order to achieve adequate engineering answers. As an example of the increasing application of non-linear computer codes in the nowadays engineering practice, some cases will be briefly presented. In each case, only the main features of the problem involved and the solution used to solve it will be shown

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

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