In vivo microvascular oximetry using multispectral imaging

This thesis describes the application of multispectral imaging to several novel oximetry applications. Chapter 1 motivates optical microvascular oximetry, outlines oxygen transport in the body, describes the theory of oximetry, and describes the challenges associated with in vivo oximetry, in particular imaging through tissue. Chapter 2 reviews various imaging techniques for quantitative in vivo oximetry of the microvasculature, including multispectral and hyperspectral imaging, photoacoustic imaging, optical coherence tomography, and laser speckle techniques. Chapter 3 describes a two-wavelength oximetry study of two microvascular beds in the anterior segment of the eye: the bulbar conjunctival and episcleral microvasculature. This study reveals previously unseen oxygen diffusion from ambient air into the bulbar conjunctival microvasculature, altering the oxygen saturation of the bulbar conjunctiva. The response of the bulbar conjunctival and episcleral microvascular beds to acute mild hypoxia is quantified and the rate at which oxygen diffuses into bulbar conjunctival vessels is measured. Chapter 4 describes the development and application of a highly novel non-invasive retinal angiography technique: Oximetric Ratio Contrast Angiography (ORCA). ORCA requires only multispectral imaging and a small perturbation of blood oxygen saturation to produce angiographic sequences. A pilot study of ORCA in human subjects was conducted. This study demonstrates that ORCA can produce angiographic sequences with features such as sequential vessel filling and laminar flow. The application and challenges of ORCA are discussed, with emphasis on comparison with other angiography techniques, such as fluorescein angiography. Chapter 5 describes the development of a multispectral microscope for oximetry in the spinal cord dorsal vein of rats. Measurements of blood oxygen saturation are made in the dorsal vein of both healthy rats, and in rats with the Experimental autoimmune encephalomyelitis (EAE) disease model of multiple sclerosis. The venous blood oxygen saturation of EAE disease model rats was found to be significantly lower than that of healthy controls, indicating increased oxygen uptake from blood in the EAE disease model of multiple sclerosis. Chapter 6 describes the development of video-rate red eye oximetry; a technique which could enable stand-off oximetry of the blood-supply of the eye with high temporal resolution. The various challenges associated with video-rate red eye oximetry are investigated and their influence quantified. The eventual aim of this research is to track circulating deoxygenation perturbations as they arrive in both eyes, which could provide a screening method for carotid artery stenosis, which is major risk-factor for stroke. However, due to time constraints, it was not possible to thoroughly investigate if video-rate red eye can detect such perturbations. Directions and recommendations for future research are outlined.

Estudo da homogeneidade e discriminação de madeiras através de espectroscopia de imagens, imagens digitais e ferramentas quimiométricas

Tese (doutorado)–Universidade de Brasília, Instituto de Química, Programa de Pós-Graduação em Química, 2016.

Self-healing thermal annealing: surface morphological restructuring control of GaN nanorods

With advances in nanolithography and dry etching, top-down methods of nanostructuring have become a widely used tool for improving the efficiency of optoelectronics. These nano dimensions can offer various benefits to the device performance in terms of light extraction and efficiency, but often at the expense of emission color quality. Broadening of the target emission peak and unwanted yellow luminescence are characteristic defect-related effects due to the ion beam etching damage, particularly for III–N based materials. In this article we focus on GaN based nanorods, showing that through thermal annealing the surface roughness and deformities of the crystal structure can be “self-healed”. Correlative electron microscopy and atomic force microscopy show the change from spherical nanorods to faceted hexagonal structures, revealing the temperature-dependent surface morphology faceting evolution. The faceted nanorods were shown to be strain- and defect-free by cathodoluminescence hyperspectral imaging, micro-Raman, and transmission electron microscopy (TEM). In-situ TEM thermal annealing experiments allowed for real time observation of dislocation movements and surface restructuring observed in ex-situ annealing TEM sampling. This thermal annealing investigation gives new insight into the redistribution path of GaN material and dislocation movement post growth, allowing for improved understanding and in turn advances in optoelectronic device processing of compound semiconductors.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Estimating the colors of paintings

Observers can adjust the spectrum of illumination on paintings for optimal viewing experience. But can they adjust the colors of paintings for the best visual impression? In an experiment carried out on a calibrated color moni- tor images of four abstract paintings obtained from hyperspectral data were shown to observers that were unfamiliar with the paintings. The color volume of the images could be manipulated by rotating the volume around the axis through the average (a*, b*) point for each painting in CIELAB color space. The task of the observers was to adjust the angle of rotation to produce the best subjective impression from the paintings. It was found that the distribution of angles selected for data pooled across paintings and observers could be de- scribed by a Gaussian function centered at 10o, i.e. very close to the original colors of the paintings. This result suggest that painters are able to predict well what compositions of colors observers prefer.

Agglomération et hétéroagglomération des nanoparticules d'argent en eaux douces

Les nanomatériaux sont une classe de contaminants qui est de plus en plus présent dans l’environnement. Leur impact sur l’environnement dépendra de leur persistance, mobilité, toxicité et bioaccumulation. Chacun de ces paramètres dépendra de leur comportement physicochimique dans les eaux naturelles (i.e. dissolution et agglomération). L’objectif de cette étude est de comprendre l’agglomération et l’hétéroagglomération des nanoparticules d’argent dans l’environnement. Deux différentes sortes de nanoparticules d’argent (nAg; avec enrobage de citrate et avec enrobage d’acide polyacrylique) de 5 nm de diamètre ont été marquées de manière covalente à l’aide d’un marqueur fluorescent et ont été mélangées avec des colloïdes d’oxyde de silice (SiO2) ou d’argile (montmorillonite). L’homo- et hétéroagglomération des nAg ont été étudiés dans des conditions représentatives d’eaux douces naturelles (pH 7,0; force ionique 10 7 à 10-1 M de Ca2+). Les tailles ont été mesurées par spectroscopie de corrélation par fluorescence (FCS) et les résultats ont été confirmés à l’aide de la microscopie en champ sombre avec imagerie hyperspectrale (HSI). Les résultats ont démontrés que les nanoparticules d’argent à enrobage d’acide polyacrylique sont extrêmement stables sous toutes les conditions imposées, incluant la présence d’autres colloïdes et à des forces ioniques très élevées tandis que les nanoparticules d’argent avec enrobage de citrate ont formées des hétéroagrégats en présence des deux particules colloïdales.

Spectroscopy and detectability of liquid brines on mars

Recent geomorphological observations as well as chemical and thermodynamic studies demonstrate that liquid water should be stable today on the Martian surface at some times of the day. In Martian conditions, brines would be particularly more stable than pure water because salts can depress the freezing point and lower the evaporation rate of water. Despite this evidence, no clear spectral signature of liquid has been observed so far by the hyperspectral imaging spectrometers OMEGA and CRISM. However, past spectral analysis lacks a good characterization of brines׳ spectral signatures. This study thus aims to determine how liquid brines can be detected on Mars by spectroscopy. In this way, laboratory experiments were performed for reproducing hydration and dehydration cycles of various brines while measuring their spectral signatures. The resulting spectra first reveal a very similar spectral evolution for the various brine types and pure water, with the main difference observed at the end of the dehydration with the crystallization of various hydrated minerals from brines. The main characteristic of this spectral behavior is an important decoupling between the evolution of albedo and hydration bands depths. During most of the wetting/drying processes, spectra usually display a low albedo associated with shallow water absorption band depths. Strong water absorption band depth and high albedo are respectively only observed when the surface is very wet and when the surface is very dry. These experiments can thus explain why the currently active Martian features attributed to the action of a liquid are only associated with low albedo and very weak spectral signatures. Hydration experiments also reveal that deliquescence occurs easily even at low temperature and moderate soil water vapor pressure and could thus cause seasonal darkening on Mars. These experiments demonstrate that the absence of water absorptions in CRISM in the middle afternoon does not rule out water activity and suggest future spectral investigations to identify water on the Martian surface.

Chlorophyll and true-color maps of sediments from the intertidal zones in Winyah Bay, South Carolina, USA and List, Sylt, Germany

We used hyperspectral imaging to study short-term effects of bioturbation by lugworms (Arenicola marina) on the surficial biomass of microphytobenthos (MPB) in permeable marine sediments. Within days to weeks after the addition of a lugworm to a homogenized and recomposed sediment, the average surficial MPB biomass and its spatial heterogeneity were, respectively, 150 - 250% and 280% higher than in sediments without lugworms. The surficial sediment area impacted by a single medium-sized lugworm (~4 g wet weight) over this time-scale was at least 340 cm**2. While sediment reworking was the primary cause of the increased spatial heterogeneity, experiments with lugworm-mimics together with modeling showed that bioadvective porewater transport from depth to the sediment surface, as induced by the lugworm ventilating its burrow, was the main cause of the increased surficial MPB biomass. Although direct measurements of nutrient fluxes are lacking, our present data show that enhanced advective supply of nutrients from deeper sediment layers induced by faunal ventilation is an important mechanism that fuels high primary productivity at the surface of permeable sediments even though these systems are generally characterized by low standing stocks of nutrients and organic material.

Hyperespectral images for the evaluation of the quality of minimally processed vegetables (spinach)

The production of minimally processed vegetables and fruits is an emergent sector, however these processes reduce the useful life of the products. Main preservation techniques such cold storage and modified atmosphere are limited. New treatments are being applied (O3 , UV‐C radiation, biodegradable films…etc.). The sector precise of cheap and fast techniques to evaluate the general quality and the security of the processed products, that constitute a tool of aid to the decision in the implementation of new procedures of packaging and/or treatments. Objectives: To explore hyperspectral imaging for monitoring the evolution of minimally processed leafy vegetables during shelf‐life . To identify and classify deterioration rates of the leaves through Multivariate analysis techniques (PLS‐DA)

Monitoring leafy vegetables through packaging films with

Fresh-cut or minimally processed fruit and vegetables have been physically modified from its original form (by peeling, trimming, washing and cutting) to obtain a 100% edible product that is subsequently packaged (usually under modified atmosphere packaging –MAP) and kept in refrigerated storage. In fresh-cut products, physiological activity and microbiological spoilage, determine their deterioration and shelf-life. The major preservation techniques applied to delay spoilage are chilling storage and MAP, combined with chemical treatments antimicrobial solutions antibrowning, acidulants, antioxidants, etc.). The industry looks for safer alternatives. Consequently, the sector is asking for innovative, fast, cheap and objective techniques to evaluate the overall quality and safety of fresh-cut products in order to obtain decision tools for implementing new packaging materials and procedures. In recent years, hyperspectral imaging technique has been regarded as a tool for analyses conducted for quality evaluation of food products in research, control and industries. The hyperspectral imaging system allows integrating spectroscopic and imaging techniques to enable direct identification of different components or quality characteristics and their spatial distribution in the tested sample. The objective of this work is to develop hyperspectral image processing methods for the supervision through plastic films of changes related to quality deterioration in packed readyto-use leafy vegetables during shelf life. The evolutions of ready-to-use spinach and watercress samples covered with three different common transparent plastic films were studied. Samples were stored at 4 ºC during the monitoring period (until 21 days). More than 60 hyperspectral images (from 400 to 1000 nm) per species were analyzed using ad hoc routines and commercial toolboxes of MatLab®. Besides common spectral treatments for removing additive and multiplicative effects, additional correction, previously to any other correction, was performed in the images of leaves in order to avoid the modification in their spectra due to the presence of the plastic transparent film. Findings from this study suggest that the developed images analysis system is able to deal with the effects caused in the images by the presence of plastic films in the supervision of shelf-life in leafy vegetables, in which different stages of quality has been identified.

Generación de una librería RVC – CAL para la etapa de determinación de endmembers en el proceso de análisis de imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar dos de las cuatro fases propias del procesado espectral: reducción dimensional y extracción de endmembers. Cabe mencionar que este trabajo se complementa con el realizado por Raquel Lazcano en su Proyecto Fin de Grado, donde se desarrollan las funciones necesarias para completar las otras dos fases necesarias en la cadena de desmezclado. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Proyecto Fin de Grado y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como los medios y las plataformas que servirán para realizar la división en núcleos y detectar las distintas problemáticas con las que nos podamos encontrar al realizar dicha división. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para componer la cadena de desmezclado y generar la librería; un punto importante en este apartado es la utilización de librerías especializadas en operaciones matriciales complejas, implementadas en C++. Tras explicar el método utilizado, se exponen los resultados obtenidos primero por etapas y, posteriormente, con la cadena de procesado completa, implementada en uno o varios núcleos. Por último, se aportan una serie de conclusiones obtenidas tras analizar los distintos algoritmos en cuanto a bondad de resultados, tiempos de procesado y consumo de recursos y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement two of the four stages of the hyperspectral imaging processing chain: dimensionality reduction and endmember extraction. This research is complemented with the research conducted by Raquel Lazcano in her Diploma Project, where she studies the other two stages of the processing chain. The document is divided in several chapters. The first of them introduces the motivation of the Diploma Project and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images and the software and hardware that we will use to parallelize the system and to analyze its performance. Once we have exposed the theoretical bases, we will explain the followed methodology to compose the processing chain and to generate the library; one of the most important issues in this chapter is the use of some C++ libraries specialized in complex matrix operations. At this point, we will expose the results obtained in the individual stage analysis and then, the results of the full processing chain implemented in one or several cores. Finally, we will extract some conclusions related with algorithm behavior, time processing and system performance. In the same way, we propose some future research lines according to the results obtained in this document

Generación de una librería RVC - CAL para la etapa de estimación de abundancias en el proceso de análisis de imágenes hiperespectrales

Las imágenes hiperespectrales permiten extraer información con una gran resolución espectral, que se suele extender desde el espectro ultravioleta hasta el infrarrojo. Aunque esta tecnología fue aplicada inicialmente a la observación de la superficie terrestre, esta característica ha hecho que, en los últimos años, la aplicación de estas imágenes se haya expandido a otros campos, como la medicina y, en concreto, la detección del cáncer. Sin embargo, este nuevo ámbito de aplicación ha generado nuevas necesidades, como la del procesado de las imágenes en tiempo real. Debido, precisamente, a la gran resolución espectral, estas imágenes requieren una elevada capacidad computacional para ser procesadas, lo que imposibilita la consecución de este objetivo con las técnicas tradicionales de procesado. En este sentido, una de las principales líneas de investigación persigue el objetivo del tiempo real mediante la paralelización del procesamiento, dividiendo esta carga computacional en varios núcleos que trabajen simultáneamente. A este respecto, en el presente documento se describe el desarrollo de una librería de procesado hiperespectral para el lenguaje RVC - CAL, que está específicamente pensado para el desarrollo de aplicaciones multimedia y proporciona las herramientas necesarias para paralelizar las aplicaciones. En concreto, en este Proyecto Fin de Grado se han desarrollado las funciones necesarias para implementar dos de las cuatro fases de la cadena de análisis de una imagen hiperespectral - en concreto, las fases de estimación del número de endmembers y de la estimación de la distribución de los mismos en la imagen -; conviene destacar que este trabajo se complementa con el realizado por Daniel Madroñal en su Proyecto Fin de Grado, donde desarrolla las funciones necesarias para completar las otras dos fases de la cadena. El presente documento sigue la estructura clásica de un trabajo de investigación, exponiendo, en primer lugar, las motivaciones que han cimentado este Proyecto Fin de Grado y los objetivos que se esperan alcanzar con él. A continuación, se realiza un amplio análisis del estado del arte de las tecnologías necesarias para su desarrollo, explicando, por un lado, las imágenes hiperespectrales y, por otro, todos los recursos hardware y software necesarios para la implementación de la librería. De esta forma, se proporcionarán todos los conceptos técnicos necesarios para el correcto seguimiento de este documento. Tras ello, se detallará la metodología seguida para la generación de la mencionada librería, así como el proceso de implementación de una cadena completa de procesado de imágenes hiperespectrales que permita la evaluación tanto de la bondad de la librería como del tiempo necesario para analizar una imagen hiperespectral completa. Una vez expuesta la metodología utilizada, se analizarán en detalle los resultados obtenidos en las pruebas realizadas; en primer lugar, se explicarán los resultados individuales extraídos del análisis de las dos etapas implementadas y, posteriormente, se discutirán los arrojados por el análisis de la ejecución de la cadena completa, tanto en uno como en varios núcleos. Por último, como resultado de este estudio se extraen una serie de conclusiones, que engloban aspectos como bondad de resultados, tiempos de ejecución y consumo de recursos; asimismo, se proponen una serie de líneas futuras de actuación con las que se podría continuar y complementar la investigación desarrollada en este documento. ABSTRACT. Hyperspectral imaging collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for example, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. For that reason, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization: in order to reduce the computational load, this solution executes image analysis in several processors simultaneously; in that way, this computational load is divided among the different cores, and real-time specifications can be accomplished. This document describes the construction of a new hyperspectral processing library for RVC - CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This Diploma Project develops the required library functions to implement two of the four stages of the hyperspectral imaging processing chain - endmember and abundance estimations -. The two other stages - dimensionality reduction and endmember extraction - are studied in the Diploma Project of Daniel Madroñal, which complements the research work described in this document. The document follows the classical structure of a research work. Firstly, it introduces the motivations that have inspired this Diploma Project and the main objectives to achieve. After that, it thoroughly studies the state of the art of the technologies related to the development of the library. The state of the art contains all the concepts needed to understand the contents of this research work, like the definition and applications of hyperspectral imaging and the typical processing chain. Thirdly, it explains the methodology of the library implementation, as well as the construction of a complete processing chain in RVC - CAL applying the mentioned library. This chain will test both the correct behavior of the library and the time requirements for the complete analysis of one hyperspectral image, either executing the chain in one processor or in several ones. Afterwards, the collected results will be carefully analyzed: first of all, individual results -from endmember and abundance estimations stages - will be discussed and, after that, complete results will be studied; this results will be obtained from the complete processing chain, so they will analyze the effects of multithreading and system parallelization on the mentioned processing chain. Finally, as a result of this discussion, some conclusions will be gathered regarding some relevant aspects, such as algorithm behavior, execution times and processing performance. Likewise, this document will conclude with the proposal of some future research lines that could continue the research work described in this document.

Implementación de una Support Vector Machine en RVC – CAL para imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar el clasificador conocido como Support Vector Machine – SVM. Cabe mencionar que este trabajo complementa el realizado en [1] y [2] donde se desarrollaron las funciones necesarias para implementar una cadena de procesado que utiliza el método unmixing para procesar la imagen hiperespectral. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Trabajo de Investigación y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como sus métodos de procesado y, en concreto, se detallará el método que utiliza el clasificador SVM. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para convertir una versión en Matlab del clasificador SVM optimizado para analizar imágenes hiperespectrales; un punto importante en este apartado es que se desarrolla la versión secuencial del algoritmo y se asientan las bases para una futura paralelización del clasificador. Tras explicar el método utilizado, se exponen los resultados obtenidos primero comparando ambas versiones y, posteriormente, analizando por etapas la versión adaptada al lenguaje RVC – CAL. Por último, se aportan una serie de conclusiones obtenidas tras analizar las dos versiones del clasificador SVM en cuanto a bondad de resultados y tiempos de procesado y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement the Support Vector Machine – SVM - classifier. This research complements the research conducted in [1] and [2] where the necessary functions to implement the unmixing method to analyze hyperspectral images were developed. The document is divided in several chapters. The first of them introduces the motivation of the Master Thesis and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images, their processing methods and, concretely, the SVM classifier. Once we have exposed the theoretical bases, we will explain the followed methodology to translate a Matlab version of the SVM classifier optimized to process an hyperspectral image to RVC – CAL language; one of the most important issues in this chapter is that a sequential implementation is developed and the bases of a future parallelization of the SVM classifier are set. At this point, we will expose the results obtained in the comparative between versions and then, the results of the different steps that compose the SVM in its RVC – CAL version. Finally, we will extract some conclusions related with algorithm behavior and time processing. In the same way, we propose some future research lines according to the results obtained in this document.

Site controlled red-yellow-green light emitting InGaN quantum discs on nano-tipped GaN rods

We report a method of growing site controlled InGaN multiple quantum discs (QDs) at uniform wafer scale on coalescence free ultra-high density (>80%) nanorod templates by metal organic chemical vapour deposition (MOCVD). The dislocation and coalescence free nature of the GaN space filling nanorod arrays eliminates the well-known emission problems seen in InGaN based visible light sources that these types of crystallographic defects cause. Correlative scanning transmission electron microscopy (STEM), energy-dispersive X-ray (EDX) mapping and cathodoluminescence (CL) hyperspectral imaging illustrates the controlled site selection of the red, yellow and green (RYG) emission at these nano tips. This article reveals that the nanorod tips' broad emission in the RYG visible range is in fact achieved by manipulating the InGaN QD's confinement dimensions, rather than significantly increasing the In%. This article details the easily controlled method of manipulating the QDs dimensions producing high crystal quality InGaN without complicated growth conditions needed for strain relaxation and alloy compositional changes seen for bulk planar GaN templates.

Features combination for art authentication studies: brushstroke and materials analysis of Amadeo de Souza-Cardoso

This work presents a tool to support authentication studies of paintings attributed to the modernist Portuguese artist Amadeo de Souza-Cardoso (1887-1918). The strategy adopted was to quantify and combine the information extracted from the analysis of the brushstroke with information on the pigments present in the paintings. The brushstroke analysis was performed combining Gabor filter and Scale Invariant Feature Transform. Hyperspectral imaging and elemental analysis were used to compare the materials in the painting with those present in a database of oil paint tubes used by the artist. The outputs of the tool are a quantitative indicator for authenticity, and a mapping image that indicates the areas where materials not coherent with Amadeo's palette were detected, if any. This output is a simple and effective way of assessing the results of the system. The method was tested in twelve paintings obtaining promising results.