Near-Field Directionality Beyond the Dipole Approximation: Electric Quadrupole and Higher-Order Multipole Angular Spectra

Within the context of spin-related optical phenomena, the near-field directionality is generally understood from the quantum spin Hall effect of light, according to which the transverse spin of surface or guided modes is locked to the propagation direction. So far, most previous works have been focused on the spin properties of circularly polarized dipolar sources. However, in near-field optics, higher-order multipole sources (e.g., quadrupole, octupole, and so on) might become relevant, so a more in-depth formulation would be highly valuable. Building on the angular spectrum representation, we provide a general, analytical, and ready-to-use treatment in order to address the near-field directionality of any multipole field, particularizing to the electric quadrupole case. Besides underpinning and upgrading the current framework on spin-dependent directionality, our results may open up new perspectives for engineering light-matter coupling at the nanoscale.Comment: 7 pages, 2 figures. Supplemental Material (19 pages). Supplemental tools (calculator of angular spectra and animation) available at https://doi.org/10.5281/zenodo.267790

Detección automática de temperatura de personas en ambientes climatizados

Premio extraordinario de Trabajo Fin de Máster curso 2016-2017. Máster en Energías Renovables DistribuidasEl presente trabajo de fin de máster presenta un sistema para la segmentación automática de la frente de una persona para la recolección de temperaturas e interpretación de las mismas, que podrán ser tratados y estudiados en posteriores trabajos, como por ejemplo su inclusión en una unidad de control de climatización. La temperatura de la frente es conocida por estar altamente correlada con la temperatura corporal de una persona. El sistema de adquisición tiene la característica de ser no intrusivo y no influir ni modificar el comportamiento cotidiano de las actividades diarias de las personas. El sistema propuesto ha sido desarrollado para ser invariante en la medida de lo posible a diferentes cambios en la morfología de la cara de una persona, en la posición de la cabeza, y en la aparición de objetos externos. El método utilizado en el sistema propuesto ha sido optimizado en términos de tiempo de procesamiento. Además el presente sistema puede ser utilizado en otras aplicaciones en términos de tiempo real.This work presents a method for automatic segmentation of the person's forehead to obtain temperatures from this area and to analyze the data which could be used in future works. For instance, including this information inside of an air control unit. Temperature at the forehead is known to be highly correlated to the internal body temperature. Furthermore, the system has the feature to not be intrusive and not changing the daily habits of the people. The proposed system has been developed to be invariant to different changes in relation to the shape and position of the human face, and even in presence of external objects. The present method has been optimized in terms of time processing. Thus, it can be used in applications with real-time constraints. Keywords

Automatic system for pavement crack detection and classification

Las carreteras son un tipo de elemento urbanístico utilizado por millones de personas a diario, y su estado en condiciones óptimas favorece la disminución de la tasa de accidentes de tráfico. El estado de la superficie del asfalto se ve alterado por un amplio abanico de defectos, y en concreto, las grietas cobran un interés especial debido a que su tratamiento en fases tempranas pueden suponer un ahorro en el coste de reparación y tratamiento del defecto en etapas posteriores, así como evitar la aparición de defectos derivados de ellas. Por este motivo, el mantenimiento de los pavimentos juega un papel fundamental tanto en la seguridad de los usuarios de este tipo de vías, como en términos económicos. Sin embargo, a pesar de la importancia que tiene, existen millones de kilómetros que necesitan ser inspeccionados, y esta labor se realiza en la mayoría de los casos de forma manual mediante la inspección visual supervisada por expertos, siendo una tarea ineficiente en el tiempo. Por ello, esta Tesis Doctoral presenta un sistema para la detección y clasificación automática de defectos de grietas en pavimentos. Para ello, se aplican métodos de procesamiento de imágenes a las capturas tomadas de la superficie de los asfaltos para la extracción de características y su posterior optimización y representación a un nuevo espacio de atributos interpretables por una persona. Posteriormente estas características son utilizadas por un ensemble de modelos compuesto por varios algoritmos de aprendizaje automático, para realizar la clasificación de las grietas en sus tipos más comunes: grietas de tipo malla o cocodrilo, grietas longitudinales y grietas transversales. De acuerdo con los experimentos realizados y los resultados obtenidos, el sistema tiene la capacidad de trabajar en sistemas computacionales de recursos limitados, siendo susceptible de emplearse con restricciones de tiempo real, además de proporcionar mejores resultados frente a las propuestas existentes en la literatura científica. Esto hace posible que el sistema se pueda colocar en diferentes vehículos no especializados para la recolección y clasificación de los defectos en el mismo lugar donde ocurren, aliviando así la tareas llevadas a cabo por los expertos.Roads are a type of urban element used by millions of people every day, and the optimal surface condition contributes to the reduction of the rate of traffic accidents. The condition of the asphalt surface is affected by a wide range of defects, and particularly, cracks have a special interest because their treatment in early stages represent savings in the repairing costs. As this prevents the appearance of defects derived from them rather than treating the defects in later stages. For this reason, pavement maintenance is fundamental in economic terms and the safety of the users. Millions of kilometers need to be examined, however, this work is mostly done manually through visual inspection supervised by experts, which is a time inefficient task. For this reason, this Doctoral Thesis presents a system for the automatic detection and classification of cracking defects in pavements. For this purpose, image processing methods are applied to asphalt surface images extracting features of the cracks, reducing the number of features, and representing the cracks in a new interpretable space of attributes. These new attributes are used by an ensemble model composed of different automatic learning algorithms classifying the cracks into their most common types: mesh or alligator cracks, longitudinal cracks, and transverse cracks. According to the experiment results, the proposed system can work in computer systems with limited resources and could be used with real-time constraints. Also, the proposed methodology provides more accurate results compared to the existing proposals in the scientific literature. These features enable the system to be placed in non-specialized vehicles collecting and classifying the defects, in the same place where they occur, and simplifying the tasks carried out by experts

FPGA-Based Bee Counter System

The bee counter described in this paper is a device that is installed at the entrance of the hive and forces the bees to pass through one of its twenty passageways. Each passageway has an LED at the top, which emits infrared light, and at the bottom a double photodiode integrated in the same package, which generates electrical pulses when the bee passes through the passageway and cuts the light beam. The pulses are monitored by an FPGA that counts the number of bees entering and leaving the passageway, each of which has its own control unit, implemented in the FPGA, achieving a correct and independent interpretation of the temporal relationship between the two pulses. Furthermore, the sampling frequency of the pulses and the small distance between the photodiodes, because they are in the same encapsulation, make it possible to detect the input or output of bees moving very close to each other, with a minimum distance of approximately 1 mm. In addition, the fact that each passageway has its own control unit makes it possible to detect anomalous conditions due to a failure in the LED or photodiodes, or anomalies caused even by the bees as the propolis. For these cases, a timer associated to each passageway in the FPGA has been included, which starts a timer when one of the two photodiodes does not detect a light beam. The counter has two working modes: connected to a host or in stand-alone mode, in which it periodically sends the bee count. The counter has an UART of ABR (Automatic Baud Rate) type where it receives the AT commands sent by the host to request the input and output counts and the status of the passageways. The answer data corresponding to the command are also sent to the host through the UART. The FPGA description has been performed in VHDL and customizable so that it can be implemented for any number of passageways on any FPGA. The system was evaluated in three hives from August 2nd to September 23rd, 2020, during the end of the summer season and the previous results are also shown in this wor

Non-Invasive Forehead Segmentation in Thermographic Imaging

The temperature of the forehead is known to be highly correlated with the internal body temperature. This area is widely used in thermal comfort systems, lie-detection systems, etc. However, there is a lack of tools to achieve the segmentation of the forehead using thermographic images and non-intrusive methods. In fact, this is usually segmented manually. This work proposes a simple and novel method to segment the forehead region and to extract the average temperature from this area solving this lack of non-user interaction tools. Our method is invariant to the position of the face, and other different morphologies even with the presence of external objects. The results provide an accuracy of 90% compared to the manual segmentation using the coefficient of Jaccard as a metric of similitude. Moreover, due to the simplicity of the proposed method, it can work with real-time constraints at 83 frames per second in embedded systems with low computational resources. Finally, a new dataset of thermal face images is presented, which includes some features which are difficult to find in other sets, such as glasses, beards, moustaches, breathing masks, and different neck rotations and flexions

Data communication optimization for the evaluation of multivariate conditions in distributed scenarios

The current technological landscape is characterized by the massive and efficient interconnection of heterogeneous devices. Sensor networks (SNs) are key elements of this paradigm; they support the local loop, the collection and early manipulation of information. Among the applications of SNs, event detection is a well-explored topic in which strategies such as collaboration, self-organization, and others have been developed in depth. In this topic, the simplest and also most used event concept approach is the threshold-based event, which is usually integrated as part of the local sensor process. This paper addresses a different perspective by discussing the evaluation of multivariate Boolean conditions with distributed variables. We propose a new algorithm (Data Retaining Algorithm for Condition Evaluation, DRACE) that reduces packet traffic while preserving time accuracy in event calculation on an adaptive approach. To facilitate understanding of DRACE, a case study is presented in the context of a logical simile titled The Problem of a Proper Defense. The algorithm supports parameters that affects the compromise between accuracy and traffic savings. To analyze its performance, 9000 executions of the algorithm have been performed. 9 configurations tested on a repository of 1000 triads of signals randomly generated. Focusing on the most accurate configuration, 99% of executions are error-free, and the number of packets is reduced by 40% on average, being between 30 and 50% in 68% of cases

An algorithm based on fuzzy ordinal classification to predict students’ academic performance

Predicting students’ performance in distance courses is a very relevant task to help teachers identify students who need reinforcement or extension activities. Nevertheless, identifying the student’s progress is highly complicated due to the large number of students and the lack of direct interaction. Artificial intelligence algorithms contribute to overcoming this problem by automatically analyzing the features and interactions of each student with the e-learning platform. The main limitations of the previous proposals are that they do not consider a ranking between the different marks obtained by students and the most accurate models are usually black boxes without comprehensibility. This paper proposes to use an optimized ordinal classification algorithm, FlexNSLVOrd, that performs a prediction of student’s performance in four ranking classes (Withdrawn < Fail < Pass < Distinction) by generating highly understandable models. The experimental study uses the OULA dataset and compares 10 state-of-the-art methods on 7 different courses and 3 classical classification metrics. The results, validated with statistical analysis, show that FlexNSLVOrd has higher performance than the other models and achieves significant differences with the rest of the proposals. In addition, the interpretability of FlexNSLVOrd is compared with other rule-based models, and simpler and more representative rules are obtained

3D reconstruction system and multiobject local tracking algorithm designed for billiards

The use of virtual reality or augmented reality systems in billiards sports are useful tools for pure entertainment or improving the player’s skills. Depending on the purpose of these systems, tracking algorithms based on computer vision must be used. These algorithms are especially useful in systems aiming to reconstruct the trajectories followed by the balls after a strike. However, depending on the billiard modality, the problem of tracking multiple small identical objects, such as balls, is a complex task. In addition, when an amateur or nontop professional player uses low-frame-rate and low-resolution devices, problems such as blurred balls, blurred contours, or fuzzy edges, among others, arise. These effects have a negative impact on ball-tracking accuracy and reconstruction quality. Thus, this work proposes two contributions. The first contribution is a new tracking algorithm called “multiobject local tracking (MOLT)”. This algorithm can track balls with high precision and accuracy even with motion blur caused by low-resolution and low-frame-rate devices. Moreover, the proposed MOLT algorithm is compared with nine tracking methods and four different metrics, outperforming the rest of the methods in the majority of the cases and providing a robust solution. The second contribution is a whole system to track (using the MOLT algorithm) and reconstruct the movements of the balls on a billiard table in a 3D virtual world using computer vision. The proposed system covers all steps from image capture to 3D reconstruction. The 3D reconstruction results have been qualitatively evaluated by different users through a series of questionnaires, obtaining an overall score of 7.6 (out of 10), which indicates that the system is a promising and useful tool for training. Finally, both the MOLT algorithm and the reconstruction system are tested in three billiard modalities: blackball, carom billiards, and snooker

Benefits of ensemble models in road pavement cracking classification

The maintenance of road pavements is an essential task to prevent major deterioration and to reduce accident rates. In this task, the detection and classification of different types of cracks on the roads is usually considered. However, in most cases, these tasks are not fully automated and they need to be supervised by an expert to make repair decisions. This work focuses on the automatic classification of the most common types of cracks: longitudinal cracks, transverse cracks, and alligator cracks. Our proposal combines, first, computer vision techniques for crack segmentation and second, an ensemble model (composed of different rule-based algorithms) for the classification. This approach achieves an average precision and recall values greater than 94% for three analyzed data sets improving the results in comparison to other approaches

Optimización de ataques a redes complejas mediante un algoritmo de colonias de abejas artificiales

En los últimos años, ha crecido el interés en formular como un problema de optimización la tarea de concebir ataques efectivos que causen el máximo daño sobre redes complejas. En este caso, los ataques se modelan como un proceso de eliminación de k vértices del grafo que representa la red. En este trabajo, seguimos esta línea de investigación presentando un problema de optimización que concierne la selección de los nodos a eliminar con el objetivo de minimizar el máximo valor de intermediación en el grafo residual. La intermediación es una medida de centralidad bien conocida que evalúa la importancia de los nodos de la red de acuerdo a su participación en los caminos más cortos. La relevancia de este indicador dentro de la tecnología actual disponible para el análisis de redes nos ha llevado a plantear esta técnica para planificar ataques efectivos sobre redes. Además, para abordar el problema de optimización, proponemos un algoritmo de colonias de abejas artificiales, que es una técnica de inteligencia colectiva inspirada en el comportamiento de las abejas cuando realizan la búsqueda de comida. Nuestra propuesta explota el conocimiento útil sobre el problema que se obtiene de la exploración de las fuentes de comida, aplicando una destrucción parcial de las soluciones escogidas y una reconstrucción heurística de las mismas. Mediante el análisis experimental de los resultados mostramos el buen comportamiento del algoritmo propuesto, con respecto a métodos de la literatura que pueden adoptarse para enfrentarse con el problema, tal como el método de ataque secuencial basado en centralidad