Metodología del análisis de imágenes digitales para el estudio del contenido de humedad en vertisoles

Desde finales del siglo pasado, el procesamiento y análisis de imágenes digitales, se ha convertido en una poderosa herramienta para la investigación de las propiedades del suelo a múltiples resoluciones, sin embargo todavía no existen los mejores resultados en cuanto a estos trabajos. El principal problema para investigar el drenaje vertical a partir de la distribución de humedad en un perfil de vertisol es la búsqueda de métodos factibles que usen este procedimiento. El objetivo general es implementar una metodología para el procesamiento y análisis de imágenes digitales, que permita caracterizar la distribución del contenido de humedad de un perfil de vertisol. Para el estudio, doce calicatas fueron excavadas en un Mazic Pellic Vertisol, seis de ellas en mayo 13/2011 y el resto en mayo 19/2011 después de moderados eventos de lluvia. Las imágenes RGB de los perfiles fueron tomadas con una cámara Kodak™; con tamaños seleccionados de 1600 x 945 píxeles cada una fue procesada para homogeneizar el brillo y se aplicaron filtros suavizadores de diferentes tamaños de ventana, hasta obtener el óptimo. Cada imagen se dividió en sus matrices componentes, seleccionando los umbrales de cada una para ser aplicado y obtener el patrón digital binario. Este último fue analizado a través de la estimación de dos exponentes fractales: dimensión de conteo de cajas (DBC) y dimensión fractal de interfase húmedo seco (Di). Además, fueron determinados tres coeficientes prefractales a la máxima resolución: número total de cajas interceptados en el plano del patrón (A), la lagunaridad fractal (λ1) y la entropía de Shannon (S1). Para todas las imágenes obtenidas, basado en la entropía, los análisis de clúster y de histogramas, el filtro espacial de 9x9 resultó ser el de tamaño de ventana óptimo. Los umbrales fueron seleccionados a partir del carácter bimodal de los histogramas. Los patrones binarios obtenidos mostraron áreas húmedas (blancas) y secas (negras) que permitieron su análisis. Todos los parámetros obtenidos mostraron diferencias significativas entre ambos conjuntos de patrones espaciales. Mientras los exponentes fractales aportan información sobre las características de llenado del patrón de humedad, los coeficientes prefractales representan propiedades del suelo investigado. La lagunaridad fractal fue el mejor discriminador entre los patrones de humedad aparente del suelo. ABSTRACT From last century, digital image processing and analysis was converted in a powerful tool to investigate soil properties at multiple resolutions, however, the best final procedure in these works not yet exist. The main problem to study vertical drainage from the moisture distribution, on a vertisol profile, is searching for suitable methods using these procedures. Our aim was to design a digital image processing methodology and its analysis to characterize the moisture content distribution of a vertisol profile. In this research, twelve soil pits were excavated on a bare Mazic Pellic Vertisol, six of them in May 13/2011 and the rest in May 19/2011 after a moderate rainfall event. Digital RGB images were taken from each vertisol pit using a Kodak™ camera selecting a size of 1600x945 pixels. Each soil image was processed to homogenized brightness and then a spatial filter with several window sizes was applied to select the optimum one. The RGB image obtained were divided in each matrix color selecting the best thresholds for each one, maximum and minimum, to be applied and get a digital binary pattern. This one was analyzed by estimating two fractal scaling exponents: box counting dimension (DBC

Evaluating the reinforcement of inorganic fullerene-like nanoparticles in thermoplastic matrices by depth-sensing indentation

The reinforcing effect of inorganic fullerene-like tungsten disulfide (IF-WS2) nanoparticles in two different polymer matrices, isotactic polypropylene (iPP) and polyphenylene sulfide (PPS), has been investigated by means of dynamic depth-sensing indentation. The hardness and elastic modulus enhancement upon filler addition is analyzed in terms of two main contributions: changes in the polymer matrix nanostructure and intrinsic properties of the filler including matrix-particle load transfer. It is found that the latter mainly determines the overall mechanical improvement, whereas the nanostructural changes induced in the polymer matrix only contribute to a minor extent. Important differences are suggested between the mechanisms of deformation in the two nanocomposites, resulting in a moderate mechanical enhancement in case of iPP (20% for a filler loading of 1%), and a remarkable hardness increase in case of PPS (60% for the same filler content). The nature of the polymer amorphous phase, whether in the glassy or rubbery state, seems to play here an important role. Finally, nanoindentation and dynamic mechanical analysis measurements are compared and discussed in terms of the different directionality of the stresses applied.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Estudio e implementación de estrategias de detección de objetos móviles mediante métodos basados en el uso de Local Binary Patterns

El objetivo de este Trabajo de Fin de Grado es el estudio e implementación de estrategias de detección de objetos móviles basadas en el uso de Local Binary Patterns. Para ello, en primer lugar se han analizado los métodos de substracción de fondos basados en información de textura propuestos durante los últimos años. Como resultado de este análisis, se ha implementado una estrategia basada en Local Binary Patterns a la que posteriormente se le han añadido las mejoras que se han considerado más apropiadas, las cuales están destinadas tanto a reducir el coste computacional de la estrategia como a mejorar la calidad de los resultados obtenidos. Por último se ha realizado un estudio comparativo entre las estrategias implementadas y otros métodos populares empleados para la substracción de fondos, como los basados en el uso de mezclas de gaussianas.

Efficient information reconciliation for Continuous-Variable QKD using Non-Binary Low-Density Parity-Check Codes

We present here an information reconciliation method and demonstrate for the first time that it can achieve efficiencies close to 0.98. This method is based on the belief propagation decoding of non-binary LDPC codes over finite (Galois) fields. In particular, for convenience and faster decoding we only consider power-of-two Galois fields.

New definitions of margin for multi-class Boosting algorithms

La familia de algoritmos de Boosting son un tipo de técnicas de clasificación y regresión que han demostrado ser muy eficaces en problemas de Visión Computacional. Tal es el caso de los problemas de detección, de seguimiento o bien de reconocimiento de caras, personas, objetos deformables y acciones. El primer y más popular algoritmo de Boosting, AdaBoost, fue concebido para problemas binarios. Desde entonces, muchas han sido las propuestas que han aparecido con objeto de trasladarlo a otros dominios más generales: multiclase, multilabel, con costes, etc. Nuestro interés se centra en extender AdaBoost al terreno de la clasificación multiclase, considerándolo como un primer paso para posteriores ampliaciones. En la presente tesis proponemos dos algoritmos de Boosting para problemas multiclase basados en nuevas derivaciones del concepto margen. El primero de ellos, PIBoost, está concebido para abordar el problema descomponiéndolo en subproblemas binarios. Por un lado, usamos una codificación vectorial para representar etiquetas y, por otro, utilizamos la función de pérdida exponencial multiclase para evaluar las respuestas. Esta codificación produce un conjunto de valores margen que conllevan un rango de penalizaciones en caso de fallo y recompensas en caso de acierto. La optimización iterativa del modelo genera un proceso de Boosting asimétrico cuyos costes dependen del número de etiquetas separadas por cada clasificador débil. De este modo nuestro algoritmo de Boosting tiene en cuenta el desbalanceo debido a las clases a la hora de construir el clasificador. El resultado es un método bien fundamentado que extiende de manera canónica al AdaBoost original. El segundo algoritmo propuesto, BAdaCost, está concebido para problemas multiclase dotados de una matriz de costes. Motivados por los escasos trabajos dedicados a generalizar AdaBoost al terreno multiclase con costes, hemos propuesto un nuevo concepto de margen que, a su vez, permite derivar una función de pérdida adecuada para evaluar costes. Consideramos nuestro algoritmo como la extensión más canónica de AdaBoost para este tipo de problemas, ya que generaliza a los algoritmos SAMME, Cost-Sensitive AdaBoost y PIBoost. Por otro lado, sugerimos un simple procedimiento para calcular matrices de coste adecuadas para mejorar el rendimiento de Boosting a la hora de abordar problemas estándar y problemas con datos desbalanceados. Una serie de experimentos nos sirven para demostrar la efectividad de ambos métodos frente a otros conocidos algoritmos de Boosting multiclase en sus respectivas áreas. En dichos experimentos se usan bases de datos de referencia en el área de Machine Learning, en primer lugar para minimizar errores y en segundo lugar para minimizar costes. Además, hemos podido aplicar BAdaCost con éxito a un proceso de segmentación, un caso particular de problema con datos desbalanceados. Concluimos justificando el horizonte de futuro que encierra el marco de trabajo que presentamos, tanto por su aplicabilidad como por su flexibilidad teórica. Abstract The family of Boosting algorithms represents a type of classification and regression approach that has shown to be very effective in Computer Vision problems. Such is the case of detection, tracking and recognition of faces, people, deformable objects and actions. The first and most popular algorithm, AdaBoost, was introduced in the context of binary classification. Since then, many works have been proposed to extend it to the more general multi-class, multi-label, costsensitive, etc... domains. Our interest is centered in extending AdaBoost to two problems in the multi-class field, considering it a first step for upcoming generalizations. In this dissertation we propose two Boosting algorithms for multi-class classification based on new generalizations of the concept of margin. The first of them, PIBoost, is conceived to tackle the multi-class problem by solving many binary sub-problems. We use a vectorial codification to represent class labels and a multi-class exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of penalties for failures and rewards for successes. The stagewise optimization of this model introduces an asymmetric Boosting procedure whose costs depend on the number of classes separated by each weak-learner. In this way the Boosting procedure takes into account class imbalances when building the ensemble. The resulting algorithm is a well grounded method that canonically extends the original AdaBoost. The second algorithm proposed, BAdaCost, is conceived for multi-class problems endowed with a cost matrix. Motivated by the few cost-sensitive extensions of AdaBoost to the multi-class field, we propose a new margin that, in turn, yields a new loss function appropriate for evaluating costs. Since BAdaCost generalizes SAMME, Cost-Sensitive AdaBoost and PIBoost algorithms, we consider our algorithm as a canonical extension of AdaBoost to this kind of problems. We additionally suggest a simple procedure to compute cost matrices that improve the performance of Boosting in standard and unbalanced problems. A set of experiments is carried out to demonstrate the effectiveness of both methods against other relevant Boosting algorithms in their respective areas. In the experiments we resort to benchmark data sets used in the Machine Learning community, firstly for minimizing classification errors and secondly for minimizing costs. In addition, we successfully applied BAdaCost to a segmentation task, a particular problem in presence of imbalanced data. We conclude the thesis justifying the horizon of future improvements encompassed in our framework, due to its applicability and theoretical flexibility.

Differential elimination by differential specialization of Sylvester style matrices

Differential resultant formulas are defined, for a system $\cP$ of $n$ ordinary Laurent differential polynomials in $n-1$ differential variables. These are determinants of coefficient matrices of an extended system of polynomials obtained from $\cP$ through derivations and multiplications by Laurent monomials. To start, through derivations, a system $\ps(\cP)$ of $L$ polynomials in $L-1$ algebraic variables is obtained, which is non sparse in the order of derivation. This enables the use of existing formulas for the computation of algebraic resultants, of the multivariate sparse algebraic polynomials in $\ps(\cP)$, to obtain polynomials in the differential elimination ideal generated by $\cP$. The formulas obtained are multiples of the sparse differential resultant defined by Li, Yuan and Gao, and provide order and degree bounds in terms of mixed volumes in the generic case.

Analysis of single and binary phases in cerium doped sodium bismuth titanate -Na0.5Bi0.5TiO3 materials

The pure and cerium doped sodium bismuth titanate inorganic powders were synthesized by solid state reaction method. The presence of rhombohedral phase was observed in cerium doped NBT compounds. At 1200 ºC, the 5% of cerium doped NBT compound forms single perovskite phase. The samples of x = 0.10 and 0.15 were heat treated to 1350 ºC, the binary phases with cerium and bismuth oxides were observed. The X-ray diffraction, fourier transform infrared spectroscopy, reflectance spectra, differential thermal analysis and thermo gravimetric analysis were used to analyze the various properties of samples. Moreover, the effects of cerium doping and calcining temperature on NBT samples were investigated. In this work we present our recent results on the synthesis and characterization of Ce doped sodium bismuth titanate materials.

Binary metal oxides for composite ultrafiltration membranes

A new ultrafiltration membrane was developed by the incorporation of binary metal oxides inside polyethersulfone. Physico-chemical characterization of the binary metal oxides demonstrated that the presence of Ti in the TiO2?ZrO2 system results in an increase of the size of the oxides, and also their dispersity. The crystalline phases of the synthesized binary metal oxides were identified as srilankite and zirconium titanium oxide. The effect of the addition of ZrO2 can be expressed in terms of the inhibition of crystal growth of anocrystalline TiO2 during the synthesis process. For photocatalytic applications the band gap of the synthesized semiconductors was determined, confirming a gradual increase (blue shift) in the band gap as the amount of Zr loading increases. Distinct distributions of binary metal oxides were found along the permeation axis for the synthesized membranes. Particles with Ti are more uniformly dispersed throughout the membrane cross-section. The physico-chemical characterization of membranes showed a strong correlation between some key membrane properties and the spatial particle distribution in the membrane structure. The proximity of metal oxide fillers to the membrane surface determines the hydrophilicity and porosity of modified membranes. Membranes incorporating binary metal oxides were found to be promising candidates for wastewater treatment by ultrafiltration, considering the observed improvement influx and anti-fouling properties of doped membranes. Multi-run fouling tests of doped membranes confirmed the stability of permeation through membranes embedded with binary TiO2?ZrO2 particles.

Efectos territoriales de las denominaciones de origen protegidas de aceite de oliva en España

La implementación de indicaciones geográficas, generan distintos efectos socio-económicos, tecnológicos y ambientales, no sólo a nivel de los productores directamente involucrados, sino también en el marco del sistema agroalimentario local (SIAL). Entre estos últimos, se destaca la organización colectiva de los productores y el desarrollo de una gobernanza, a partir del conjunto de actividades desarrolladas por el Consejo Regulador (CR), en torno al control de calidad y la promoción del producto. El concepto de gobernanza territorial implica llevar a cabo procesos de organización colectiva en red en los que tienen lugar procesos de coordinación multi-nivel entre los agentes, las empresas y las instituciones locales, en un contexto de asimetría en la información y de la existencia de numerosos decisores. El objetivo de esta Tesis Doctoral es realizar un aporte metodológico para el estudio de la gobernanza territorial en sistemas agroalimentarios localizados, a partir de la construcción de indicadores (de calidad, innovación, efectos económicos y prácticas ambientales) y el análisis de las redes de colaboración en materia de difusión del conocimiento e innovaciones técnicas, organizativas y comerciales en la Comarca de Sierra Mágina. Para responder el objetivo se aplica análisis de redes sociales y se construyen indicadores sintéticos para valorizar los efectos. Se realizan encuestas a la totalidad de almazaras presentes en la Comarca de Sierra Mágina, tanto a las que poseen el signo de calidad como a las que no cuentan con dicha marca territorial y entrevistas a testigos privilegiados. En las mismas se consulta sobre las actividades y proyectos que realizan en forma conjunta con otras almazaras e instituciones, así como los actores o referentes a quiénes consultan en temas relacionados con la calidad en la producción olivícola y la obtención de aceite, así como aquellos actores a quienes consultan en aspectos referidos a la Comercialización y gestión. A partir de la información recabada, se obtienen cuatro indicadores sintéticos de efectos de la implantación de una DOP en las almazaras adheridas, referidos a la adopción de innovaciones productivas y organizativas, a la calidad en producto y procesos, a las rentas de diferenciación y a las prácticas ambientales. Asimismo se generan las matrices bi-narias y valuadas para las redes de colaboración en la difusión del conocimiento y se calculan los indicadores particulares. Esto nos permite estudiar las características de las redes que se generan (tamaño, conectividad), el papel del CR en la gobernanza territorial y de otras instituciones del medio, así como identificar actores centrales y grupos en el proceso de difusión de conocimientos. Los resultados muestran que los SIAL pueden beneficiarse de la organización interprofesional y de cooperación interinstitucional a escala local que se producen tras la implantación de una DOP. Se desarrolla un proceso de articulación y un fuerte intercambio de conocimientos entre actores ligados al propio proceso de control de la calidad diferencial. Pero, junto con ello, se desarrollan actividades de formación y de promoción del patrimonio comarcal y se consolidan redes locales de innovación y colaboración mutua en distintas actividades, tanto entre actores de la propia cadena de producción, como entre éstos y las instituciones del medio. Estas actividades, junto con la presencia de un entramado institucional denso, permiten el desarrollo de una gobernanza territorial alrededor del sistema agroalimentario del aceite de oliva. ABSTRACT The implementation of geographical indications has different socio-economic, technological and environmental effects not only for the producers involved but also for the local agro food system (LAFS). Among the latter, we can highlight the collective organization of producers and the development of territorial governance through a series of tasks developed by the Regulatory Board (RB) as regards quality control and product marketing. The concept of territorial governance involves processes of collective organization in networks, where processes of multi-level coordination among agents, enterprises and local institutions take place within a context of asymmetry of information and numerous decision makers. The objective of this Doctoral Thesis is to contribute with methodological tools for the study of territorial governance in local agro food systems by constructing indicators (of quality, innovation, economic effects and environmental practices) and by analyzing networks of collaboration in terms of dissemination of technical, organizational and commercial innovation and knowledge in Comarca de Sierra Mágina. To achieve that objective, social network analysis is applied and synthetic indicators are elaborated to valorize the effects. Interviews to key actors and surveys to every oil mill in Comarca de Sierra Mágina are carried out, not only in those oil mills that host a label of quality but also in the ones that do not hold it. These interviews inquire about tasks and projects performed together with other oil mills and institutions and about actors or referents consulted in topics related to quality in olive production and oil extraction as well as in management and marketing. From all the information collected, four synthetic indicators of the effects of establishing Protected Designations of Origen (PDOs) in the associate oil mills are obtained, in terms of adoption of productive and organizational innovations, process and product quality, income differentiation and environmental practices. In the same way, binary and valorized matrixes are generated for those networks collaborating in the dissemination of knowledge and specific indicators are calculated. This allows to study networks (size, connectivity), the role of RB in territorial governance and other institutions involved and to identify main actors and groups in the process of knowledge dissemination. The results show that the LAFS can benefit from inter-professional organizations and inter- institutional cooperation at local levels by implementing PDOs. A process of articulation and strong exchange of knowledge is developed among those actors involved in controlling differential quality. But, at the same time, activities for the formation and promotion of the “comarca” patrimony are developed and local networks of innovation and mutual collaboration are built for different activities, not only among actors in the production process itself, but between these actors and local institutions as well. These tasks, together with the existence of a dense spatial entrepreneurial network, allow the development of territorial governance on the olive oil agro food system.

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

On the classification of binary shifts of finite commutant index

We provide a complete classification up to conjugacy of the binary shifts of finite commutant index on the hyperfinite II1, factor. There is a natural correspondence between the conjugacy classes of these shifts and polynomials over GF(2) satisfying a certain duality condition.

Extremely sensitive, background-free gene detection using binary probes and beta replicase.

We have developed a specific and sensitive nucleic acid amplification assay that is suitable for routine gene detection. The assay is based on a novel molecular genetic strategy in which two different RNA probes are hybridized to adjacent positions on a target nucleic acid and then ligated to form an amplifiable reporter RNA. The reporter RNA is then replicated up to a hundred billion-fold in a 30-min isothermal reaction that signals the presence of the target. The assay can detect fewer than 100 nucleic acid molecules; it provides quantitative results over a wide range of target concentrations and it employs a universal format that can detect any infectious agent.

Skeletal matrices, muci, and the origin of invertebrate calcification.

The sudden appearance of calcified skeletons among many different invertebrate taxa at the Precambrian-Cambrian transition may have required minor reorganization of preexisting secretory functions. In particular, features of the skeletal organic matrix responsible for regulating crystal growth by inhibition may be derived from mucous epithelial excretions. The latter would have prevented spontaneous calcium carbonate overcrusting of soft tissues exposed to the highly supersaturated Late Proterozoic ocean [Knoll, A. H., Fairchild, I. J. & Swett, K. (1993) Palaios 8, 512-525], a putative function for which we propose the term "anticalcification." We tested this hypothesis by comparing the serological properties of skeletal water-soluble matrices and mucous excretions of three invertebrates--the scleractinian coral Galaxea fascicularis and the bivalve molluscs Mytilus edulis and Mercenaria mercenaria. Crossreactivities recorded between muci and skeletal water-soluble matrices suggest that these different secretory products have a high degree of homology. Furthermore, freshly extracted muci of Mytilus were found to inhibit calcium carbonate precipitation in solution.