Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant

An improved bit parallel exact maximum clique algorithm

This paper describes new improvements for BB-MaxClique (San Segundo et al. in Comput Oper Resour 38(2):571–581, 2011 ), a leading maximum clique algorithm which uses bit strings to efficiently compute basic operations during search by bit masking. Improvements include a recently described recoloring strategy in Tomita et al. (Proceedings of the 4th International Workshop on Algorithms and Computation. Lecture Notes in Computer Science, vol 5942. Springer, Berlin, pp 191–203, 2010 ), which is now integrated in the bit string framework, as well as different optimization strategies for fast bit scanning. Reported results over DIMACS and random graphs show that the new variants improve over previous BB-MaxClique for a vast majority of cases. It is also established that recoloring is mainly useful for graphs with high densities.

A dual-band priority assignment algorithm for dynamic QoS resource management

Future high-quality consumer electronics will contain a number of applications running in a highly dynamic environment, and their execution will need to be efficiently arbitrated by the underlying platform software. The multimedia applications that currently execute in such similar contexts face frequent run-time variations in their resource demands, originated by the greedy nature of the multimedia processing itself. Changes in resource demands are triggered by numerous reasons (e.g. a switch in the input media compression format). Such situations require real-time adaptation mechanisms to adjust the system operation to the new requirements, and this must be done seamlessly to satisfy the user experience. One solution for efficiently managing application execution is to apply quality of service resource management techniques, based on assigning and enforcing resource contracts to applications. Most resource management solutions provide temporal isolation by enforcing resource assignments and avoiding any resource overruns. However, this has a clear limitation over the cost-effective resource usage. This paper presents a simple priority assignment scheme based on uniform priority bands to allow that greedy multimedia tasks incur in safe overruns that increase resource usage and do not threaten the timely execution of non-overrunning tasks. Experimental results show that the proposed priority assignment scheme in combination with a resource accounting mechanism preserves timely multimedia execution and delivery, achieves a higher cost-effective processor usage, and guarantees the execution isolation of non-overrunning tasks.

Metaheuristic approaches for MWT and MWPT problems

It is known that the Minimum Weight Triangulation problem is NP-hard. Also the complexity of the Minimum Weight Pseudo-Triangulation problem is unknown, yet it is suspected to be also NP-hard. Therefore we focused on the development of approximate algorithms to find high quality triangulations and pseudo-triangulations of minimum weight. In this work we propose two metaheuristics to solve these problems: Ant Colony Optimization (ACO) and Simulated Annealing (SA). For the experimental study we have created a set of instances for MWT and MWPT problems, since no reference to benchmarks for these problems were found in the literature. Through experimental evaluation, we assess the applicability of the ACO and SA metaheuristics for MWT and MWPT problems. These results are compared with those obtained from the application of deterministic algorithms for the same problems (Delaunay Triangulation for MWT and a Greedy algorithm respectively for MWT and MWPT).

Simulating accepting networks of evolutionary processors with filtered connections by accepting evolutionary P systems

In this work, we propose a variant of P system based on the rewriting of string-objects by means of evolutionary rules. The membrane structure of such a P system seems to be a very natural tool for simulating the filters in accepting networks of evolutionary processors with filtered connections. We discuss an informal construction supporting this simulation. A detailed proof is to be considered in an extended version of this work.

Caracterización y optimización de un sistema de sonido profesional

El presente proyecto tiene como objeto caracterizar y optimizar un equipo de sonido profesional, entendiendo por “caracterizar” el determinar los atributos particulares de cada uno de los componentes integrados en el sistema, y entendiendo por “optimizar” el hallar la mejor manera de obtener una respuesta plana para todo el rango de frecuencias, libre de distorsión, y en la mayor área posible. El sistema de sonido utilizado pertenece a un grupo musical de directo, por lo que se instala y se configura en cada concierto en función de las características del recinto, sea cerrado o al aire libre. Con independencia de estas particularidades, el sistema completo se divide en dos formaciones, L y R (lado izquierdo y lado derecho del escenario), por lo que cada formación se compone de un procesador digital de la señal, cuatro etapas de amplificación, un sistema line array de ocho unidades, y un conjunto de ocho altavoces de subgraves. Para llevar a cabo el objetivo planteado, se ha dividido el proyecto en las fases que a continuación se describen. En primer lugar, se han realizado, en la cámara anecoica de la EUITT, las medidas que permiten obtener las características de cada uno de los elementos que componen el sistema. Estas medidas se han almacenado en formato ASCII. En segundo lugar, se ha diseñado una interfaz gráfica que permite, utilizando las medidas almacenadas, caracterizar tanto la respuesta individual de cada elemento de la cadena del sistema de sonido como la respuesta combinada de una unidad line array y una unidad de subgraves. La interfaz es interactiva, y tiene además la capacidad de entregar automáticamente los valores de configuración que permiten la optimización del conjunto. Esto es, obtener alineamiento en el rango de frecuencias compartido por ambas unidades. Las medidas realizadas en la cámara anecoica se han utilizado igualmente para modelar el sistema line array al completo y poder realizar simulaciones en campo libre utilizando programas de predicción acústica. Se ha experimentado con los valores de configuración que permiten el alineamiento de los elementos individuales y obtenidos a través de la interfaz desarrollada, para comprobar la validez de los mismos con la formación line array y subgraves al completo. Por otro lado, se han analizado los métodos de optimización de sistemas propuestos por profesionales reconocidos del medio con el objetivo de aplicarlos en un evento real. En la preparación y montaje del evento, se han aplicado los valores de configuración proporcionados por la interfaz, y se ha comprobado la validez de los mismos realizando medidas in situ según los criterios propuestos en los métodos de optimización estudiados. ABSTRACT. This project aims to characterize and optimize a professional sound system. Characterize must be understood as determining the particular attributes of each component integrated in the system; optimize must be understood as finding the best way to get a flat response for all the frequency range, distortion free, in the largest possible area. The sound system under test belongs to a live musical group, so it is setup and configured on each concert depending on the characteristics of the enclosure, whether it’s indoor or outdoor. Apart from these features, the whole system is divided into two clusters, L and R (left and right side of the stage), so that each one is provided with a digital signal processor, four amplification stages, an eight-units line array system, and a set of eight subwoofers . To accomplish the stated objective, the project has been divided into the steps described below. To begin with, measures have been realized in the anechoic chamber of EUITT, which make possible obtaining the characteristics of each of the elements of the system. These measures have been stored in ASCII format. Then, a graphical interface has been designed that allow, using the stored measurements and from graphics, to characterize both the individual response of each element of the string sound system and the combined response of the several elements. The interface is interactive, and also has the ability to automatically deliver the configuration settings that allow the whole optimization. That means to get alignment in the frequency range shared by a line array unit and a subwoofer unit. The measurements made in the anechoic chamber have also been used to model the complete line array system and to perform free-field simulations using acoustical prediction programs. Simulations have been done with the configuration settings that allow the individual elements alignment (provided by the graphical interface developed), in order to check their validity with the full line array and subwoofer systems. On the other hand, analysis about the optimization methods, proposed by renowned professionals of the field, has been made in order to apply them in a real concert. In the setup and assembly of the event, configuration settings provided by the interface have been applied. Their validity has been proved by making measures on-site according to the criteria set in the studied optimization methods.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Analysis of large-scale digital optical neural networks by Feynman diagrams

A new method to study large scale neural networks is presented in this paper. The basis is the use of Feynman- like diagrams. These diagrams allow the analysis of collective and cooperative phenomena with a similar methodology to the employed in the Many Body Problem. The proposed method is applied to a very simple structure composed by an string of neurons with interaction among them. It is shown that a new behavior appears at the end of the row. This behavior is different to the initial dynamics of a single cell. When a feedback is present, as in the case of the hippocampus, this situation becomes more complex with a whole set of new frequencies, different from the proper frequencies of the individual neurons. Application to an optical neural network is reported.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

MIA - a free and open source software for gray scale medical image analysis

Background Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development. Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don’t provide an clear approach when one wants to shape a new command line tool from a prototype shell script. Results The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code. Conclusion In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed.

Sistemas de Gestión de la Calidad. Metodología para implementar proyectos de mejora continua para la reducción de los defectos de construcción en edificación de viviendas

La mejora continua debería estar presente siempre en las empresas, dispongan o no de sistemas de gestión. Sin embargo, su aplicación en el sector de la construcción es especialmente difícil debido a las características particulares del mismo. Por este motivo se plantea el objetivo principal de esta Tesis Doctoral: “Establecer una metodología de trabajo que permita a las empresas constructoras implantar proyectos de mejora continua para incrementar la calidad de las viviendas entregadas a los usuarios”. En la investigación llevada a cabo se han inspeccionado 818 viviendas, recogiendo un total de 82.550 incidencias, las cuales se han analizado aplicando cuatro de las siete herramientas estadísticas básicas de la mejora continua (Hoja de recogida de datos, Estratificación, Histograma y Diagrama de Pareto), concluyendo que los tres oficios que concentran el 80% de los defectos, en los que convendría actuar para reducir de manera significativa los fallos de construcción en la fase de pre-entrega, son: Carpintería de Madera, Revestimientos Cerámicos e Instalación de Electricidad. De entre estos tres oficios se ha seleccionado el de Revestimientos Cerámicos para poner en práctica un proyecto de mejora continua. Analizando los datos relativos a este oficio se elabora un listado de 25 defectos tipo en los que se pueden agrupar todas las incidencias detectadas. Aplicando de nuevo las cuatro herramientas básicas de la calidad se destacan los 10 defectos tipo con mayor impacto en volumen de incidencias y en coste de reparación, para focalizar los esfuerzos de mejora. Con esta información se elabora un documento de criterios técnicos para la ejecución de los Revestimientos Cerámicos que se implanta, en parte, en varias obras para tratar de reducir los defectos detectados en las viviendas antes de la entrega a sus propietarios, y se definen unos Índices de Calidad para medir los resultados. Se toman datos de nuevo a 6 y 20 meses desde la implantación del protocolo, se analizan y se calculan los resultados del proyecto de mejora, concluyendo que se está avanzando positivamente. En base a toda la información recogida a lo largo del proceso de la investigación y de la experiencia del proyecto de mejora implantado, se presenta una propuesta de metodología para implementar proyectos de mejora, así como la documentación recomendada para su puesta en práctica, además de la documentación técnica específica para la prevención de los defectos de construcción en Revestimientos Cerámicos incluyendo las fichas de control para la recepción de materiales, control de ejecución y control de recepción del revestimiento terminado. ABSTRACT Continuous improvement should always be a core value in firms of all kinds, whether or not they implement management systems. Nevertheless, its application in the construction sector seems especially difficult due to its inherent intricacies and complexity. The study of this phenomenon is the main aim of the hereby presented PhD dissertation "Establishment of a working methodology that allows construction (related) firms to carry out projects of continuous improvement in order to increase the quality of housing upon delivery to the client". In the present research 818 housing units have been inspected, collecting a total of 82550 incidence entries, which have been analyzed by means of 4 out of the 7 basic statistical tools of continuous improvement: Data collection sheets, stratification, histogram, and Pareto diagram. The data shows that the 3 main trades where special actions should be taken in order to significantly reduce construction defects are: carpentry, ceramic cladding, and electricity systems. These trades combined account for the 80% of the total defects detected during the inspections. Among the mentioned works, ceramic tiling is selected as a continuous improvement case study project. Analysing data relative to this specific trade, a list of 25 defect types is developed. These types gather all detected defects under this group. Further applying the four statistical tools referred to above, the 10 most significant events are highlighted as to clearly determine the improvement measures. These events have the most impact on both volume of defects and reparation costs. This information is then put together in a document of technical criteria for the correct execution of ceramic tiling that is implemented in various ongoing projects under construction as to minimize the defects prior to the final delivery to the client. Also, a series of Quality Index are defined as criteria for execution suitability. 6 to 20 months after the implementation of this control protocol, the same process is repeated with the purpose of comparing results. It is concluded that a positive evolution takes place. Based both on the information collected throughout the research process and the experience of the case study, the dissertation proposes a methodology to successfully implement improvement projects along with reference documentation and specific technical documents for the prevention of construction defects in ceramic tiling, including (i) material reception control sheets, (ii) an execution control sheet, and a sheet relative to the (iii) control of the finished cladding.

Robust control of underactuated wheeled mobile manipulators using GPI disturbance observers

This article describes the design of a linear observer–linear controller-based robust output feedback scheme for output reference trajectory tracking tasks in the case of nonlinear, multivariable, nonholonomic underactuated mobile manipulators. The proposed linear feedback scheme is based on the use of a classical linear feedback controller and suitably extended, high-gain, linear Generalized Proportional Integral (GPI) observers, thus aiding the linear feedback controllers to provide an accurate simultaneous estimation of each flat output associated phase variables and of the exogenous and perturbation inputs. This information is used in the proposed feedback controller in (a) approximate, yet close, cancelations, as lumped unstructured time-varying terms, of the influence of the highly coupled nonlinearities, and (b) the devising of proper linear output feedback control laws based on the approximate estimates of the string of phase variables associated with the flat outputs simultaneously provided by the disturbance observers. Simulations reveal the effectiveness of the proposed approach.

Effect of magnetic field on current colection on bare tether in LEO

An eiectrodynamic Tether is a long thin conductive string deployed from a spacecraft. A part of the ED tether near one end, which is rendered positive by the Electromotive force (EMF)along the tether, collects electrons from the ambient plasma. In the frame of reference moving with theter, ions flow toward the tether, get deflected near the tether by its high positive potential and create a wake. Due to the asymmetry of plasma distribution and the weak but significant Geomagnetic field, the conventional probe theory becomes almost inapplicable. Computational work for the prediction of current collection is thus necessiated.. In this paper, we analyze effects of magnetic field on velocity distribution funtion at a point that is far from the tether, and discuss a new way to treat electrons at computational boundary. Three cases with different magnetic field are simulated and compiled so as to provide a part of the pre-flight prediction of the space experiment by NASA ProSEDS, which is planned September 2002.

Temporal task allocation in periodic environments. An approach based on synchronization

In this paper, we study a robot swarm that has to perform task allocation in an environment that features periodic properties. In this environment, tasks appear in different areas following periodic temporal patterns. The swarm has to reallocate its workforce periodically, performing a temporal task allocation that must be synchronized with the environment to be effective. We tackle temporal task allocation using methods and concepts that we borrow from the signal processing literature. In particular, we propose a distributed temporal task allocation algorithm that synchronizes robots of the swarm with the environment and with each other. In this algorithm, robots use only local information and a simple visual communication protocol based on light blinking. Our results show that a robot swarm that uses the proposed temporal task allocation algorithm performs considerably more tasks than a swarm that uses a greedy algorithm.

Simulación numérica computacional de fenómenos dinámicos

En este proyecto se trata la simulación numérica de un fenómeno dinámico, basado en el comportamiento de una onda transmitida a lo largo de una cuerda elástica de un instrumento musical, cuyos extremos se encuentran anclados. El fenómeno físico, se desarrolla utilizando una ecuación en derivadas parciales hiperbólicas con variables espacial y temporal, acompañada por unas condiciones de contorno tipo Dirichlet en los extremos y por más condiciones iniciales que dan comienzo al proceso. Posteriormente se han generado algoritmos para el método numérico empleado (Diferencias finitas centrales y progresivas) y la programación del problema aproximado con su consistencia, estabilidad y convergencia, obteniéndose unos resultados acordes con la solución analítica del problema matemático. La programación y salida de resultados se ha realizado con Visual Studio 8.0. y la programación de objetos con Visual Basic .Net In this project the topic is the numerical simulation of a dynamic phenomenon, based on the behavior of a transmitted wave along an elastic string of a musical instrument, whose ends are anchored. The physical phenomenon is developed using a hyperbolic partial differential equation with spatial and temporal variables, accompanied by a Dirichlet boundary conditions at the ends and more initial conditions that start the process. Subsequently generated algorithms for the numerical method used (central and forward finite differences) and the programming of the approximate problem with consistency, stability and convergence, yielding results in line with the analytical solution of the mathematical problem. Programming and output results has been made with Visual Studio 8.0. and object programming with Visual Basic. Net