Diferencias en la distancia de lanzamiento y velocidad de balón según el puesto específico en jugadores de balonmano sub-18

El presente estudio ha analizado las diferencias entre puestos específicos ofensivos en la distancia lanzamiento con balón medicinal pesado y liviano y en la velocidad de lanzamiento con y sin oposición en jugadores en formación. Para ello, cincuenta y ocho jugadores realizaron pruebas de progresiva especificidad: lanzamiento con balón medicinal pesado (LBMP) y ligero (LBML), velocidad de lanzamiento sin (VL) y con oposición (VLO). VLO fue menor a VL en todos los puestos específicos, con diferencias significativas en los jugadores laterales (p<0,01) y pivotes (p<0,05), constatándose una influencia negativa de la oposición en la velocidad de lanzamiento. Igualmente, se constataron diferencias significativas (p<0,001) entre puestos específicos en LBMP (F4, 53=17,012), LBML (F4, 53=37,433), VL (F4, 53=25,183) y VLO (F4, 53=17,091), lo cual ratifica que el puesto específico podría ser determinante en la distancia de lanzamiento con balón medicinal y en la velocidad de lanzamiento en jugadores de balonmano en etapas formativas.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Crop row detection in maize fields inspired on the human visual perception

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.

Implementación de una herramienta de productividad para el desarrollo y despliegue de aplicaciones

En la actualidad se está viviendo el auge del Cloud Computing (Computación en la Nube) y cada vez son más las empresas importantes en el sector de las Tecnologías de la Información que apuestan con fuerza por estos servicios. Por un lado, algunas ofrecen servicios, como Amazon y su sistema IaaS (Infrastructure as a Service) Amazon Web Services (AWS); por otro, algunas los utilizan, como ocurre en el caso de este proyecto, en el que Telefonica I+D hace uso de los servicios proporcionados por AWS para sus proyectos. Debido a este crecimiento en el uso de las aplicaciones distribuidas es importante tener en cuenta el papel que desempeñan los desarrolladores y administradores de sistemas que han de trabajar y mantener todas las máquinas remotas de uno o varios proyectos desde una única máquina local. El ayudar a realizar estas tareas de la forma más cómoda y automática posible es el objetivo principal de este proyecto. En concreto, el objetivo de este proyecto es el diseño y la implementación de una solución software que ayude a la productividad en el desarrollo y despliegue de aplicaciones en un conjunto de máquinas remotas desde una única máquina local, teniendo como base una prueba de concepto realizada anteriormente que prueba las funcionalidades más básicas de las librerías utilizadas para el desarrollo de la herramienta. A lo largo de este proyecto se han estudiado las diferentes alternativas que se encuentran en el mercado que ofrecen al menos parte de la soluci6n a los problemas abordados, pese a que los requisitos de la empresa indicaban que la herramienta debía implementarse de forma completa. Se estudió a fondo después la prueba de concepto de la que se partía para, con los conocimientos adquiridos sobre el tema, mejorarla cumpliendo los objetivos marcados. Tras el desarrollo y la implementaci6n completa de la herramienta se proponen posibles caminos a seguir en el futuro. ---ABSTRACT---Nowadays we are experiencing the rise of Cloud Computing and every day more and more important IT companies are betting hard for this kind of services. On one hand, some of these companies offer services such as Amazon IaaS (Infrastructure as a Service) system Amazon Web Services (AWS); on the other hand, some of them use these services, as in the case of this project, in which Telefonica I+D uses the services provided by AWS in their projects. Due this growth in the use of distributed applications it is important to consider the developers and system administrators' roles, who have to work and do the maintenance of all the remote machines from one or several projects from a single local machine. The main goal of this project is to help with these tasks making them as comfortable and automatically as possible. Specifically, the goal of this project is the design and implementation of a software solution that helps to achieve a better productivity in the development of applications on a set of remote machines from a single local machine, based on a proof of concept developed before, in which the basic functionality of the libraries used in this tool were tested. Throughout this project the different alternatives on the market that offer at least part of the solution to the problem addressed have been studied, although according to the requirements of the company, the tool should be implemented from scratch. After that, the basic proof of concept was thoroughly studied and improved with the knowledge acquired on the subject, fulfilling the marked goals. Once the development and full implementation of the tool is done, some ways of improvement for the future are suggested.

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.

Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers

The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect