A two-dimensional finite element model of front surface current flow in cells under non-uniform, concentrated illumination

A two-dimensional finite element model of current flow in the front surface of a PV cell is presented. In order to validate this model we perform an experimental test. Later, particular attention is paid to the effects of non-uniform illumination in the finger direction which is typical in a linear concentrator system. Fill factor, open circuit voltage and efficiency are shown to decrease with increasing degree of non-uniform illumination. It is shown that these detrimental effects can be mitigated significantly by reoptimization of the number of front surface metallization fingers to suit the degree of non-uniformity. The behavior of current flow in the front surface of a cell operating at open circuit voltage under non-uniform illumination is discussed in detail.

Research on intermediate band solar cells and development of experimental techniques for their characterization under concentrated illumination

Abstract This work is a contribution to the research and development of the intermediate band solar cell (IBSC), a high efficiency photovoltaic concept that features the advantages of both low and high bandgap solar cells. The resemblance with a low bandgap solar cell comes from the fact that the IBSC hosts an electronic energy band -the intermediate band (IB)- within the semiconductor bandgap. This IB allows the collection of sub-bandgap energy photons by means of two-step photon absorption processes, from the valence band (VB) to the IB and from there to the conduction band (CB). The exploitation of these low energy photons implies a more efficient use of the solar spectrum. The resemblance of the IBSC with a high bandgap solar cell is related to the preservation of the voltage: the open-circuit voltage (VOC) of an IBSC is not limited by any of the sub-bandgaps (involving the IB), but only by the fundamental bandgap (defined from the VB to the CB). Nevertheless, the presence of the IB allows new paths for electronic recombination and the performance of the IBSC is degraded at 1 sun operation conditions. A theoretical argument is presented regarding the need for the use of concentrated illumination in order to circumvent the degradation of the voltage derived from the increase in the recombi¬nation. This theory is supported by the experimental verification carried out with our novel characterization technique consisting of the acquisition of photogenerated current (IL)-VOC pairs under low temperature and concentrated light. Besides, at this stage of the IBSC research, several new IB materials are being engineered and our novel character¬ization tool can be very useful to provide feedback on their capability to perform as real IBSCs, verifying or disregarding the fulfillment of the “voltage preservation” principle. An analytical model has also been developed to assess the potential of quantum-dot (QD)-IBSCs. It is based on the calculation of band alignment of III-V alloyed heterojunc-tions, the estimation of the confined energy levels in a QD and the calculation of the de¬tailed balance efficiency. Several potentially useful QD materials have been identified, such as InAs/AlxGa1-xAs, InAs/GaxIn1-xP, InAs1-yNy/AlAsxSb1-x or InAs1-zNz/Alx[GayIn1-y]1-xP. Finally, a model for the analysis of the series resistance of a concentrator solar cell has also been developed to design and fabricate IBSCs adapted to 1,000 suns. Resumen Este trabajo contribuye a la investigación y al desarrollo de la célula solar de banda intermedia (IBSC), un concepto fotovoltaico de alta eficiencia que auna las ventajas de una célula solar de bajo y de alto gap. La IBSC se parece a una célula solar de bajo gap (o banda prohibida) en que la IBSC alberga una banda de energía -la banda intermedia (IB)-en el seno de la banda prohibida. Esta IB permite colectar fotones de energía inferior a la banda prohibida por medio de procesos de absorción de fotones en dos pasos, de la banda de valencia (VB) a la IB y de allí a la banda de conducción (CB). El aprovechamiento de estos fotones de baja energía conlleva un empleo más eficiente del espectro solar. La semejanza antre la IBSC y una célula solar de alto gap está relacionada con la preservación del voltaje: la tensión de circuito abierto (Vbc) de una IBSC no está limitada por ninguna de las fracciones en las que la IB divide a la banda prohibida, sino que está únicamente limitada por el ancho de banda fundamental del semiconductor (definido entre VB y CB). No obstante, la presencia de la IB posibilita nuevos caminos de recombinación electrónica, lo cual degrada el rendimiento de la IBSC a 1 sol. Este trabajo argumenta de forma teórica la necesidad de emplear luz concentrada para evitar compensar el aumento de la recom¬binación de la IBSC y evitar la degradación del voltage. Lo anterior se ha verificado experimentalmente por medio de nuestra novedosa técnica de caracterización consistente en la adquisicin de pares de corriente fotogenerada (IL)-VOG en concentración y a baja temperatura. En esta etapa de la investigación, se están desarrollando nuevos materiales de IB y nuestra herramienta de caracterizacin está siendo empleada para realimentar el proceso de fabricación, comprobando si los materiales tienen capacidad para operar como verdaderas IBSCs por medio de la verificación del principio de preservación del voltaje. También se ha desarrollado un modelo analítico para evaluar el potencial de IBSCs de puntos cuánticos. Dicho modelo está basado en el cálculo del alineamiento de bandas de energía en heterouniones de aleaciones de materiales III-V, en la estimación de la energía de los niveles confinados en un QD y en el cálculo de la eficiencia de balance detallado. Este modelo ha permitido identificar varios materiales de QDs potencialmente útiles como InAs/AlxGai_xAs, InAs/GaxIni_xP, InAsi_yNy/AlAsxSbi_x ó InAsi_zNz/Alx[GayIni_y]i_xP. Finalmente, también se ha desarrollado un modelado teórico para el análisis de la resistencia serie de una célula solar de concentración. Gracias a dicho modelo se han diseñado y fabricado IBSCs adaptadas a 1.000 soles.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Non-uniform target illumination in deflagration regime : thermal smoothing

Thermal smoothing in the plasma ablated from a laser target under weakly nonuniform irradiation is analyzed, assuming absorption at nc and a deflagration regime (conduction restricted to a thin quasisteady layer next to the target). Magnetic generation effects are included and found to be weak. Differences from results available in the literature are explained; the importance of the character of the underdense flow at uniform irradiation is emphasized.

Non uniform target illumination in deflagration regime : refractive smoothing

Refractive smoothing of weak non-uniformities in the illumination of laser targets is analyzed, assuming absorption at the critical density and restricting conduction to a thin layer, and using results from thermal smoothing, which is uncoupled from the refraction. Magnetic effects are included. Non-uniformity wavelengths comparable to the thickness of the conduction layer are considered; efficient smoothing exists at both short and long wavelengths in this range. Thermal focusing could make the ablated plasma unstable.

SMS freeforms for illumination

SMS 3D (simultaneous multiple surfaces in their three-dimensional version) is a well-known design method comprising two freeform surfaces that allow the perfect coupling of two wavefronts with another two. The design algorithm provides a collection of line pairs on both surfaces (called SMS spines), whose three-dimensional shape seems arbitrary at first sight. This paper shows that the shapes of the spines are partially governed by applying the étendue conservation theorem to the biparametric bundle of rays linking the paired spines, which is one lesser known étendue invariants found by Poincaré. The resulting formulae for the spines in three-dimensional space happen to coincide with the conventional étendue formulas of two-dimensional geometry, like for instance, the Hottel formula.

Calculation of dielectrophoretic potential generated under illumination patterns on x- and z-cut LiNbO3 : application to photovoltaic particle trapping

The dielectrophoretic potential generated near the surface of a z-cut LiNbO3 by photovoltaic charge transport has been calculated for first time. The procedure and results are compared with the ones corresponding to x-cut. Diferences in the position, sharpness and time evolution are reported, and their implication on particle trapping are discussed.

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms.

Optimal laser intensity profiles for a uniform target illumination in direct-drive inertial confinement fusion

A numerical method providing the optimal laser intensity profiles for a direct-drive inertial confinement fusion scheme has been developed. The method provides an alternative approach to phase-space optimization studies, which can prove computationally expensive. The method applies to a generic irradiation configuration characterized by an arbitrary number NB of laser beams provided that they irradiate the whole target surface, and thus goes beyond previous analyses limited to symmetric configurations. The calculated laser intensity profiles optimize the illumination of a spherical target. This paper focuses on description of the method, which uses two steps: first, the target irradiation is calculated for initial trial laser intensities, and then in a second step the optimal laser intensities are obtained by correcting the trial intensities using the calculated illumination. A limited number of example applications to direct drive on the Laser MegaJoule (LMJ) are described.

Optoelectronic tweezers under arbitrary illumination patterns: theoretical simulations and comparison to experiment

Photovoltaic tweezers are a promising tool to place and move particles on the surface of a photovoltaic material in a controlled way. To exploit this new technique it is necessary to accurately know the electric field created by a specific illumination on the surface of the crystal and above it. This paper describes a numerical algorithm to obtain this electric field generated by several relevant light patterns, and uses them to calculate the electrophoretic potential acting over neutral, polarizable particles in the proximity of the crystal. The results are compared to experiments carried out in LiNbO3 with good overall agreement.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

A numerical study of Bi-periodic binary diffraction gratings for solar cell applications

In this paper, a numerical study is made of simple bi-periodic binary diffraction gratings for solar cell applications. The gratings consist of hexagonal arrays of elliptical towers and wells etched directly into the solar cell substrate. The gratings are applied to two distinct solar cell technologies: a quantum dot intermediate band solar cell (QD-IBSC) and a crystalline silicon solar cell (SSC). In each case, the expected photocurrent increase due to the presence of the grating is calculated assuming AM1.5D illumination. For each technology, the grating period, well/tower depth and well/tower radii are optimised to maximise the photocurrent. The optimum parameters are presented. Results are presented for QD-IBSCs with a range of quantum dot layers and for SSCs with a range of thicknesses. For the QD-IBSC, it is found that the optimised grating leads to an absorption enhancement above that calculated for an ideally Lambertian scatterer for cells with less than 70 quantum dot layers. In a QD-IBSC with 50 quantum dot layers equipped with the optimum grating, the weak intermediate band to conduction band transition absorbs roughly half the photons in the corresponding sub-range of the AM1.5D spectrum. For the SSC, it is found that the optimised grating leads to an absorption enhancement above that calculated for an ideally Lambertian scatterer for cells with thicknesses of 10 ?m or greater. A 20um thick SSC equipped with the optimised grating leads to an absorption enhancement above that of a 200um thick SSC equipped with a planar back reflector.

Un tutorial sobre blogs. El abecé del universo blog

Esta exposición pretende ser una introducción al estudio de un amplio, complejo y dinámico conjunto de nociones, técnicas y prácticas sociales, que gira en torno a la blogosfera, “un vigoroso subespacio de comunicación en Internet”, tal como lo denomina Sáez Vacas en esta misma revista. El objetivo no es tanto ser exhaustivo en el tratamiento, como dar a conocer al lector los distintos conceptos y fenómenos involucrados en la génesis de este peculiar universo, cuyo origen podemos situar en un metafórico Blog Bang. Hablaremos de los blogs (weblogs o bitácoras), su origen, caracterización, clasificación y cuantificación, de la tecnología que los rodea y de conceptos relacionados, tales como los wikis, el socialware, la blogocultura y la web semántica. This essay is designed as an introduction to the study of a broad, complex and dynamic set of notions, techniques and social practices revolving around the blogosphere –“an intense communication subspace on the Internet”, as defined by Saéz Vacas in this magazine. The aim of this article is not to exhaustively cover the topic, but rather, to introduce the reader to the different concepts and phenomena involved in the genesis of this peculiar universe, whose origin lies in the metaphoric Blog Bang. We will touch on blogs (weblogs and bitcores), their origin, nature, classification and quantification, the technology that surrounds them, and other related concepts like wikis, socialware, blogculture and web semantics.