Patient-Rated Outcomes of Lumbar Fusion in Patients with Degenerative Disease of the Lumbar Spine: Does Age Matter?

STUDY DESIGN Single centre retrospective study of prospectively collected data, nested within the Eurospine Spine Tango data acquisition system. OBJECTIVE The aim of this study was to assess the patient-rated outcome and complication rates associated with lumbar fusion procedures in three different age groups. SUMMARY OF BACKGROUND DATA There is a general reluctance to consider spinal fusion procedures in elderly patients due to the increased likelihood of complications. METHODS Before and at 3, 12, and 24 months after surgery, patients completed the multidimensional Core Outcome Measures Index (COMI). At the 3-, 12-, and 24-month follow-ups they also rated the Global Treatment Outcome (GTO) and their satisfaction with care. Patients were divided into three age groups: younger (≥50y < 65y; n = 317), older (≥65y < 80y; n = 350), and geriatric (≥ 80y; n = 40). RESULTS 707 consecutive patients were included. The preoperative comorbidity status differed significantly (p < 0.0001) between the age groups, with the highest scores in the geriatric group. Medical complications during surgery were lower in the younger age group (7%) than in the older (13.4%; p = 0.006) and geriatric groups (17.5%; p = 0.007); surgical complications tended to be higher in the elderly group (younger, 6.3%; older, 6.0%; geriatric, 15.0%; p = 0.09). There were no significant group differences (p > 0.05) for the scores on any of the COMI domains, GTO, or patient-rated satisfaction at either 3-, 12-, and 24-months follow-up. CONCLUSIONS Despite greater comorbidity and complication rates in geriatric patients, the patient-rated outcome was as good in the elderly as it was in younger age groups up to two years after surgery. These data indicate that geriatric age needs careful consideration of associated risks but is not per se a contraindication for fusion for lumbar degenerative disease. LEVEL OF EVIDENCE 4.

Standardization of data collection for the Lifetime Surveillance of Astronaut Health (LSAH)

Problems due to the lack of data standardization and data management have lead to work inefficiencies for the staff working with the vision data for the Lifetime Surveillance of Astronaut Health. Data has been collected over 50 years in a variety of manners and then entered into a software. The lack of communication between the electronic health record (EHR) form designer, epidemiologists, and optometrists has led to some level to confusion on the capability of the EHR system and how its forms can be designed to fit all the needs of the relevant parties. EHR form customizations or form redesigns were found to be critical for using NASA's EHR system in the most beneficial way for its patients, optometrists, and epidemiologists. In order to implement a protocol, data being collected was examined to find the differences in data collection methods. Changes were implemented through the establishment of a process improvement team (PIT). Based on the findings of the PIT, suggestions have been made to improve the current EHR system. If the suggestions are implemented correctly, this will not only improve efficiency of the staff at NASA and its contractors, but set guidelines for changes in other forms such as the vision exam forms. Because NASA is at the forefront of such research and health surveillance the impact of this management change could have a drastic improvement on the collection of and adaptability of the EHR. Accurate data collection from this 50+ year study is ongoing and is going to help current and future generations understand the implications of space flight on human health. It is imperative that the vast amount of information is documented correctly.^

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

A set of canonical problems in sloshing. Part 0: Experimental setup and data processing

In a series of attempts to research and document relevant sloshing type phenomena, a series of experiments have been conducted. The aim of this paper is to describe the setup and data processing of such experiments. A sloshing tank is subjected to angular motion. As a result pressure registers are obtained at several locations, together with the motion data, torque and a collection of image and video information. The experimental rig and the data acquisition systems are described. Useful information for experimental sloshing research practitioners is provided. This information is related to the liquids used in the experiments, the dying techniques, tank building processes, synchronization of acquisition systems, etc. A new procedure for reconstructing experimental data, that takes into account experimental uncertainties, is presented. This procedure is based on a least squares spline approximation of the data. Based on a deterministic approach to the first sloshing wave impact event in a sloshing experiment, an uncertainty analysis procedure of the associated first pressure peak value is described.

Silicon Photomultipliers (SiPM) as novel photodetectors for PET

Next generation PET scanners should fulfill very high requirements in terms of spatial, energy and timing resolution. Modern scanner performances are inherently limited by the use of standard photomultiplier tubes. The use of Silicon Photomultipliers (SiPMs) is proposed for the construction of a 4D-PET module of 4.8×4.8 cm2 aimed to replace the standard PMT based PET block detector. The module will be based on a LYSO continuous crystal read on two faces by Silicon Photomultipliers. A high granularity detection surface made by SiPM matrices of 1.5 mm pitch will be used for the x–y photon hit position determination with submillimetric accuracy, while a low granularity surface constituted by 16 mm2 SiPM pixels will provide the fast timing information (t) that will be used to implement the Time of Flight technique (TOF). The spatial information collected by the two detector layers will be combined in order to measure the Depth of Interaction (DOI) of each event (z). The use of large area multi-pixel Silicon Photomultiplier (SiPM) detectors requires the development of a multichannel Data Acquisition system (DAQ) as well as of a dedicated front-end in order not to degrade the intrinsic detector capabilities and to manage many channels. The paper describes the progress made on the development of the proof of principle module under construction at the University of Pisa.

NetCDF based data archiving system applied to ITER Fast Plant System Control prototype

EURATOM/CIEMAT and Technical University of Madrid (UPM) have been involved in the development of a FPSC [1] (Fast Plant System Control) prototype for ITER, based on PXIe (PCI eXtensions for Instrumentation). One of the main focuses of this project has been data acquisition and all the related issues, including scientific data archiving. Additionally, a new data archiving solution has been developed to demonstrate the obtainable performances and possible bottlenecks of scientific data archiving in Fast Plant System Control. The presented system implements a fault tolerant architecture over a GEthernet network where FPSC data are reliably archived on remote, while remaining accessible to be redistributed, within the duration of a pulse. The storing service is supported by a clustering solution to guaranty scalability, so that FPSC management and configuration may be simplified, and a unique view of all archived data provided. All the involved components have been integrated under EPICS [2] (Experimental Physics and Industrial Control System), implementing in each case the necessary extensions, state machines and configuration process variables. The prototyped solution is based on the NetCDF-4 [3] and [4] (Network Common Data Format) file format in order to incorporate important features, such as scientific data models support, huge size files management, platform independent codification, or single-writer/multiple-readers concurrency. In this contribution, a complete description of the above mentioned solution is presented, together with the most relevant results of the tests performed, while focusing in the benefits and limitations of the applied technologies.

Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices

New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile. However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation. For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task. Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments. The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

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.

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

Sistema de instrumentação e controle para células a combustível

Este trabajo presenta un estudio sobre el funcionamiento y aplicaciones de las células de combustible de membrana tipo PEM, o de intercambio de protones, alimentadas con hidrógeno puro y oxigeno obtenido de aire comprimido. Una vez evaluado el proceso de dichas células y las variables que intervienen en el mismo, como presión, humedad y temperatura, se presenta una variedad de métodos para la instrumentación de tales variables así como métodos y sistemas para la estabilidad y control de las mismas, en torno a los valores óptimos para una mayor eficacia en el proceso. Tomando como variable principal a controlar la temperatura del proceso, y exponiendo los valores concretos en torno a 80 grados centígrados entre los que debe situarse, es realizado un modelo del proceso de calentamiento y evolución de la temperatura en función de la potencia del calentador resistivo en el dominio de la frecuencia compleja, y a su vez implementado un sistema de medición mediante sensores termopar de tipo K de respuesta casi lineal. La señal medida por los sensores es amplificada de manera diferencial mediante amplificadores de instrumentación INA2126, y es desarrollado un algoritmo de corrección de error de unión fría (error producido por la inclusión de nuevos metales del conector en el efecto termopar). Son incluidos los datos de test referentes al sistema de medición de temperatura , incluyendo las desviaciones o error respecto a los valores ideales de medida. Para la adquisición de datos y implementación de algoritmos de control, es utilizado un PC con el software Labview de National Instruments, que permite una programación intuitiva, versátil y visual, y poder realizar interfaces de usuario gráficas simples. La conexión entre el hardware de instrumentación y control de la célula y el PC se realiza mediante un interface de adquisición de datos USB NI 6800 que cuenta con un amplio número de salidas y entradas analógicas. Una vez digitalizadas las muestras de la señal medida, y corregido el error de unión fría anteriormente apuntado, es implementado en dicho software un controlador de tipo PID ( proporcional-integral-derivativo) , que se presenta como uno de los métodos más adecuados por su simplicidad de programación y su eficacia para el control de este tipo de variables. Para la evaluación del comportamiento del sistema son expuestas simulaciones mediante el software Matlab y Simulink determinando por tanto las mejores estrategias para desarrollar el control PID, así como los posibles resultados del proceso. En cuanto al sistema de calentamiento de los fluidos, es empleado un elemento resistor calentador, cuya potencia es controlada mediante un circuito electrónico compuesto por un detector de cruce por cero de la onda AC de alimentación y un sistema formado por un elemento TRIAC y su circuito de accionamiento. De manera análoga se expone el sistema de instrumentación para la presión de los gases en el circuito, variable que oscila en valores próximos a 3 atmosferas, para ello es empleado un sensor de presión con salida en corriente mediante bucle 4-20 mA, y un convertidor simple corriente a tensión para la entrada al sistema de adquisición de datos. Consecuentemente se presenta el esquema y componentes necesarios para la canalización, calentamiento y humidificación de los gases empleados en el proceso así como la situación de los sensores y actuadores. Por último el trabajo expone la relación de algoritmos desarrollados y un apéndice con información relativa al software Labview. ABTRACT This document presents a study about the operation and applications of PEM fuel cells (Proton exchange membrane fuel cells), fed with pure hydrogen and oxygen obtained from compressed air. Having evaluated the process of these cells and the variables involved on it, such as pressure, humidity and temperature, there is a variety of methods for implementing their control and to set up them around optimal values for greater efficiency in the process. Taking as primary process variable the temperature, and exposing its correct values around 80 degrees centigrade, between which must be placed, is carried out a model of the heating process and the temperature evolution related with the resistive heater power on the complex frequency domain, and is implemented a measuring system with thermocouple sensor type K performing a almost linear response. The differential signal measured by the sensor is amplified through INA2126 instrumentation amplifiers, and is developed a cold junction error correction algorithm (error produced by the inclusion of additional metals of connectors on the thermocouple effect). Data from the test concerning the temperature measurement system are included , including deviations or error regarding the ideal values of measurement. For data acquisition and implementation of control algorithms, is used a PC with LabVIEW software from National Instruments, which makes programming intuitive, versatile, visual, and useful to perform simple user interfaces. The connection between the instrumentation and control hardware of the cell and the PC interface is via a USB data acquisition NI 6800 that has a large number of analog inputs and outputs. Once stored the samples of the measured signal, and correct the error noted above junction, is implemented a software controller PID (proportional-integral-derivative), which is presented as one of the best methods for their programming simplicity and effectiveness for the control of such variables. To evaluate the performance of the system are presented simulations using Matlab and Simulink software thereby determining the best strategies to develop PID control, and possible outcomes of the process. As fluid heating system, is employed a heater resistor element whose power is controlled by an electronic circuit comprising a zero crossing detector of the AC power wave and a system consisting of a Triac and its drive circuit. As made with temperature variable it is developed an instrumentation system for gas pressure in the circuit, variable ranging in values around 3 atmospheres, it is employed a pressure sensor with a current output via 4-20 mA loop, and a single current to voltage converter to adequate the input to the data acquisition system. Consequently is developed the scheme and components needed for circulation, heating and humidification of the gases used in the process as well as the location of sensors and actuators. Finally the document presents the list of algorithms and an appendix with information about Labview software.

Meter data management for smart monitoring power networks

The electrical power distribution and commercialization scenario is evolving worldwide, and electricity companies, faced with the challenge of new information requirements, are demanding IT solutions to deal with the smart monitoring of power networks. Two main challenges arise from data management and smart monitoring of power networks: real-time data acquisition and big data processing over short time periods. We present a solution in the form of a system architecture that conveys real time issues and has the capacity for big data management.

Generating descriptions that summarize geospatial and temporal data

Effective data summarization methods that use AI techniques can help humans understand large sets of data. In this paper, we describe a knowledge-based method for automatically generating summaries of geospatial and temporal data, i.e. data with geographical and temporal references. The method is useful for summarizing data streams, such as GPS traces and traffic information, that are becoming more prevalent with the increasing use of sensors in computing devices. The method presented here is an initial architecture for our ongoing research in this domain. In this paper we describe the data representations we have designed for our method, our implementations of components to perform data abstraction and natural language generation. We also discuss evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.

Sistemas multiespectrales e hiperespectrales para la observación del territorio : análisis y aplicación a la prospección de hidrocarburos.

La observación de la Tierra es una herramienta de gran utilidad en la actualidad para el estudio de los fenómenos que se dan en la misma. La observación se puede realizar a distintas escalas y por distintos métodos dependiendo del propósito. El actual Trabajo Final de Grado persigue exponer la observación del territorio mediante técnicas de Teledetección, o Detección Remota, y su aplicación en la exploración de hidrocarburos. Desde la Segunda Guerra Mundial el capturar imágenes aéreas de regiones de la Tierra estaba restringido a usos cartográficos en el sentido estricto. Desde aquellos tiempos, hasta ahora, ha acontecido una serie de avances científicos que permiten deducir características intrínsecas de la Tierra mediante mecanismos complejos que no apreciamos a simple vista, pero que, están configurados mediante determinados parámetros geométricos y electrónicos, que permiten generar series temporales de fenómenos físicos que se dan en la Tierra. Hoy en día se puede afirmar que el aprovechamiento del espectro electromagnético está en un punto máximo. Se ha pasado del análisis de la región del espectro visible al análisis del espectro en su totalidad. Esto supone el desarrollo de nuevos algoritmos, técnicas y procesos para extraer la mayor cantidad de información acerca de la interacción de la materia con la radiación electromagnética. La información que generan los sistemas de captura va a servir para la aplicación directa e indirecta de métodos de prospección de hidrocarburos. Las técnicas utilizadas en detección por sensores remotos, aplicadas en campañas geofísicas, son utilizadas para minimizar costes y maximizar resultados en investigaciones de campo. La predicción de anomalías en la zona de estudio depende del analista, quien diseña, calcula y evalúa las variaciones de la energía electromagnética reflejada o emitida por la superficie terrestre. Para dicha predicción se revisarán distintos programas espaciales, se evaluará la bondad de registro y diferenciación espectral mediante el uso de distintas clasificaciones (supervisadas y no supervisadas). Por su influencia directa sobre las observaciones realizadas, se realiza un estudio de la corrección atmosférica; se programan distintos modelos de corrección atmosférica para imágenes multiespectrales y se evalúan los métodos de corrección atmosférica en datos hiperespectrales. Se obtendrá temperatura de la zona de interés utilizando los sensores TM-4, ASTER y OLI, así como un Modelo Digital del Terreno generado por el par estereoscópico capturado por el sensor ASTER. Una vez aplicados estos procedimientos se aplicarán los métodos directos e indirectos, para la localización de zonas probablemente afectadas por la influencia de hidrocarburos y localización directa de hidrocarburos mediante teledetección hiperespectral. Para el método indirecto se utilizan imágenes capturadas por los sensores ETM+ y ASTER. Para el método directo se usan las imágenes capturadas por el sensor Hyperion. ABSTRACT The observation of the Earth is a wonderful tool for studying the different kind of phenomena that occur on its surface. The observation could be done by different scales and by different techniques depending on the information of interest. This Graduate Thesis is intended to expose the territory observation by remote sensing acquiring data systems and the analysis that can be developed to get information of interest. Since Second World War taking aerials photographs of scene was restricted only to a cartographic sense. From these days to nowadays, it have been developed many scientific advances that make capable the interpretation of the surface behavior trough complex systems that are configure by specific geometric and electronic parameters that make possible acquiring time series of the phenomena that manifest on the earth’s surface. Today it is possible to affirm that the exploitation of the electromagnetic spectrum is on a maxim value. In the past, analysis of the electromagnetic spectrum was carry in a narrow part of it, today it is possible to study entire. This implicates the development of new algorithms, process and techniques for the extraction of information about the interaction of matter with electromagnetic radiation. The information that has been acquired by remote sensing sensors is going to be a helpful tool for the exploration of hydrocarbon through direct and vicarious methods. The techniques applied in remote sensing, especially in geophysical campaigns, are employed to minimize costs and maximize results of ground-based geologic investigations. Forecasting of anomalies in the region of interest depends directly on the expertise data analyst who designs, computes and evaluates variations in the electromagnetic energy reflected or emanated from the earth’s surface. For an optimal prediction a review of the capture system take place; assess of the goodness in data acquisition and spectral separability, is carried out by mean of supervised and unsupervised classifications. Due to the direct influence of the atmosphere in the register data, a study of the minimization of its influence has been done; a script has been programed for the atmospheric correction in multispectral data; also, a review of hyperspectral atmospheric correction is conducted. Temperature of the region of interest is computed using the images captured by TM-4, ASTER and OLI, in addition to a Digital Terrain Model generated by a pair of stereo images taken by ASTER sensor. Once these procedures have finished, direct and vicarious methods are applied in order to find altered zones influenced by hydrocarbons, as well as pinpoint directly hydrocarbon presence by mean of hyperspectral remote sensing. For this purpose ETM+ and ASTER sensors are used to apply the vicarious method and Hyperion images are used to apply the direct method.

Hardware timestamping for image acquisition system based on FlexRIO and IEEE 1588 v2 Standard

Current fusion devices consist of multiple diagnostics and hundreds or even thousands of signals. This situation forces on multiple occasions to use distributed data acquisition systems as the best approach. In this type of distributed systems, one of the most important issues is the synchronization between signals, so that it is possible to have a temporal correlation as accurate as possible between the acquired samples of all channels. In last decades, many fusion devices use different types of video cameras to provide inside views of the vessel during operations and to monitor plasma behavior. The synchronization between each video frame and the rest of the different signals acquired from any other diagnostics is essential in order to know correctly the plasma evolution, since it is possible to analyze jointly all the information having accurate knowledge of their temporal correlation. The developed system described in this paper allows timestamping image frames in a real-time acquisition and processing system using 1588 clock distribution. The system has been implemented using FPGA based devices together with a 1588 synchronized timing card (see Fig.1). The solution is based on a previous system [1] that allows image acquisition and real-time image processing based on PXIe technology. This architecture is fully compatible with the ITER Fast Controllers [2] and offers integration with EPICS to control and monitor the entire system. However, this set-up is not able to timestamp the frames acquired since the frame grabber module does not present any type of timing input (IRIG-B, GPS, PTP). To solve this lack, an IEEE1588 PXI timing device its used to provide an accurate way to synchronize distributed data acquisition systems using the Precision Time Protocol (PTP) IEEE 1588 2008 standard. This local timing device can be connected to a master clock device for global synchronization. The timing device has a buffer timestamp for each PXI trigger line and requires tha- a software application assigns each frame the corresponding timestamp. The previous action is critical and cannot be achieved if the frame rate is high. To solve this problem, it has been designed a solution that distributes the clock from the IEEE 1588 timing card to all FlexRIO devices [3]. This solution uses two PXI trigger lines that provide the capacity to assign timestamps to every frame acquired and register events by hardware in a deterministic way. The system provides a solution for timestamping frames to synchronize them with the rest of the different signals.