MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION

Home environments are changing as more technological devices are used to improve daily life. The growing demand for high technology in our homes means that robot integration will soon arrive. Home devices are evolving in a connected paradigm in which data flows to perform efficient home task management. Heterogeneous home robots connected in a network can establish a workflow that complements their capabilities and so increases performance within a mission execution. This work addresses the definition and requirements of a robot-group mission in the home context. The proposed solution relies on a network of smart resources, which are defined as cyber-physical systems that provide high-level service execution. Firstly, control middleware architecture is introduced as the execution base for the Smart resources. Next, the Smart resource topology and its integration within a robotic platform are addressed. Services supplied by Smart resources manage their execution through a robot behavior architecture. Robot behavior execution is hierarchically organized through a mission definition that can be established as an individual or collective approach. Environment model and interaction tasks characterize the operation capabilities of each robot within a mission. Mission goal achievement in a heterogeneous group is enhanced through the complement of the interaction capabilities of each robot. To offer a clearer explanation, a full use case is presented in which two robots cooperate to execute a mission and the previously detailed steps are evaluated. Finally, some of the obtained results are discussed as conclusions and future works is introduced.Los entornos domésticos se encuentran sometidos a un proceso de cambio gracias al empleo de dispositivos tecnológicos que mejoran la calidad de vida de las personas. La creciente demanda de alta tecnología en los hogares señala una próxima incorporación de la robótica de servicio. Los dispositivos domésticos están evolucionando hacia un paradigma de conexión en el cual la información fluye para ofrecer una gestión más eficiente. En este entorno, robots heterogéneos conectados a la red pueden establecer un flujo de trabajo que ofreciendo nuevas soluciones y incrementando la eficiencia en la ejecución de tareas. Este trabajo aborda la definición y los requisitos necesarios para la ejecución de misiones en grupos de robots heterogéneos en entornos domésticos. La solución propuesta se apoya en una red de Smart resources, que son definidos como sistemas ciber-físicos que proporcionan servicios de alto nivel. En primer lugar, se presenta la arquitectura del middleware de control en la cual se basa la ejecución de los Smart resources. A continuación se detalla la topología de los Smart resources, así como su integración en plataformas robóticas. Los servicios proporcionados por los Smart resources gestionan su ejecución mediante una arquitectura de comportamientos para robots. La ejecución de estos comportamientos se organiza de forma jerárquica mediante la definición de una misión con un objetivo establecido de forma individual o colectiva a un grupo de robots. Dentro de una misión, las tareas de modelado e interacción con el entorno define las capacidades de operación de los robots dentro de una misión. Mediante la integración de un grupo heterogéneo de robots sus diversas capacidades son complementadas para el logro un objetivo común. A fin de caracterizar esta propuesta, los mecanismos presentados en este documento se evaluarán en detalle a lo largo de una serie experimentos en los cuales un grupo de robots heterogéneos ejecutan una misión colaborativa para alcanzar un objetivo común. Finalmente, los resultados serán discutidos a modo de conclusiones dando lugar el establecimiento de un trabajo futuro.Els entorns domèstics es troben sotmesos a un procés de canvi gràcies a l'ocupació de dispositius tecnològics que milloren la qualitat de vida de les persones. La creixent demanda d'alta tecnologia a les llars assenyala una propera incorporació de la robòtica de servei. Els dispositius domèstics estan evolucionant cap a un paradigma de connexió en el qual la informació flueix per oferir una gestió més eficient. En aquest entorn, robots heterogenis connectats a la xarxa poden establir un flux de treball que ofereix noves solucions i incrementant l'eficiència en l'execució de tasques. Aquest treball aborda la definició i els requisits necessaris per a l'execució de missions en grups de robots heterogenis en entorns domèstics. La solució proposada es recolza en una xarxa de Smart resources, que són definits com a sistemes ciber-físics que proporcionen serveis d'alt nivell. En primer lloc, es presenta l'arquitectura del middleware de control en la qual es basa l'execució dels Smart resources. A continuació es detalla la tipologia dels Smart resources, així com la seva integració en plataformes robòtiques. Els serveis proporcionats pels Smart resources gestionen la seva execució mitjançant una arquitectura de comportaments per a robots. L'execució d'aquests comportaments s'organitza de forma jeràrquica mitjançant la definició d'una missió amb un objectiu establert de forma individual o col·lectiva a un grup de robots. Dins d'una missió, les tasques de modelatge i interacció amb l'entorn defineix les capacitats d'operació dels robots dins d'una missió. Mitjançant la integració d'un grup heterogeni de robots seves diverses capacitats són complementades per a l'assoliment un objectiu comú. Per tal de caracteritzar aquesta proposta, els mecanismes presentats en aquest document s'avaluaran en detall mitjançant d'una sèrie experiments en els quals un grup de robots heterogenis executen una missió col·laborativa per aconseguir un objectiu comú. Finalment, els resultats seran discutits a manera de conclusions donant lloc a l'establiment d'un treball futur.Munera Sánchez, E. (2017). MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88404TESI

Experiences on the characterization of parallel applications in embedded systems with Extrae/Paraver

Cutting-edge functionalities in embedded systems require the use of parallel architectures to meet their performance requirements. This imposes the introduction of a new layer in the software stacks of embedded systems: the parallel programming model. Unfortunately, the tools used to analyze embedded systems fall short to characterize the performance of parallel applications at a parallel programming model level, and correlate this with information about non-functional requirements such as real-time, energy, memory usage, etc. HPC tools, like Extrae, are designed with that level of abstraction in mind, but their main focus is on performance evaluation. Overall, providing insightful information about the performance of parallel embedded applications at the parallel programming model level, and relate it to the non-functional requirements, is of paramount importance to fully exploit the performance capabilities of parallel embedded architectures. This paper contributes to the state-of-the-art of analysis tools for embedded systems by: (1) analyzing the particular constraints of embedded systems compared to HPC systems (e.g., static setting, restricted memory, limited drivers) to support HPC analysis tools; (2) porting Extrae, a powerful tracing tool from the HPC domain, to the GR740 platform, a SoC used in the space domain; and (3) augmenting Extrae with new features needed to correlate the parallel execution with the following non-functional requirements: energy, temperature and memory usage. Finally, the paper presents the usefulness of Extrae to characterize OpenMP applications and its non-functional requirements, evaluating different aspects of the applications running in the GR740.This work has been partially funded from the HP4S (High Performance Parallel Payload Processing for Space) project under the ESA-ESTEC ITI contract № 4000124124/18/NL/CRS.Peer ReviewedPostprint (author's final draft

A reliability-based particle filter for humanoid robot self-localization in Robocup Standard Platform League

This paper deals with the problem of humanoid robot localization and proposes a new method for position estimation that has been developed for the RoboCup Standard Platform League environment. Firstly, a complete vision system has been implemented in the Nao robot platform that enables the detection of relevant field markers. The detection of field markers provides some estimation of distances for the current robot position. To reduce errors in these distance measurements, extrinsic and intrinsic camera calibration procedures have been developed and described. To validate the localization algorithm, experiments covering many of the typical situations that arise during RoboCup games have been developed: ranging from degradation in position estimation to total loss of position (due to falls, ‘kidnapped robot’, or penalization). The self-localization method developed is based on the classical particle filter algorithm. The main contribution of this work is a new particle selection strategy. Our approach reduces the CPU computing time required for each iteration and so eases the limited resource availability problem that is common in robot platforms such as Nao. The experimental results show the quality of the new algorithm in terms of localization and CPU time consumption.This work has been supported by the Spanish Science and Innovation Ministry (MICINN) under the CICYT project COBAMI: DPI2011-28507-C02-01/02. The responsibility for the content remains with the authors.Munera Sánchez, E.; Muñoz Alcobendas, M.; Blanes Noguera, F.; Benet Gilabert, G.; Simó Ten, JE. (2013). A reliability-based particle filter for humanoid robot self-localization in Robocup Standard Platform League. Sensors. 13(11):14954-14983. https://doi.org/10.3390/s131114954S1495414983131

Integration of Mobile Robot Navigation on a Control Kernel Middleware based system

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-07593-8_55This paper introduces how a mobile robot can perform navigation tasks by taking the advantages of implementing a control kernel middleware (CKM) based system. Smart resources are also included into the topology of the system for improving the distribution of computational load of the needed tasks. The CKM and the smart resources are both highly recon gurable, even on execution time, and they also implement.lt detection mechanisms and QoS policies. By combining of these capabilities, the system can be dinamically adapted to the requirements of its tasks. Furthermore, this solution is suitable for most type of robots, including those which are provided of a low computational power because of the distribution of load, the bene ts of exploiting the smart resources capabilities, and the dynamic performance of the system.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-002-02.Munera Sánchez, E.; Muñoz Alcobendas, M.; Posadas-Yagüe, J.; Poza-Lujan, J.; Blanes Noguera, F. (2014). Integration of Mobile Robot Navigation on a Control Kernel Middleware based system. En Distributed Computing and Artificial Intelligence, 11th International Conference. Springer Advances in Intelligent Systems and Computing Volume 290. 477-484. https://doi.org/10.1007/978-3-319-07593-8_55S477484Rock (Robot Constrution Toolkit), http://www.rock-robotics.org/Albertos, P., Crespo, A., Simó, J.: Control kernel: A key concept in embedded control systems. In: 4th IFAC Symposium on Mechatronic Systems (2006)Bruyninckx, H., Soetens, P., Koninckx, B.: The Real-Time Motion Control Core of the Orocos Project. In: IEEE International Conference on Robotics and Automation, pp. 2766–2771 (2003)De Souza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 237–267 (2002)Fitzpatrick, P., Metta, G., Natale, L.: Towards long-lived robot genes. Robotics and Autonomous Systems (2008)Mohamed, N., Al-Jaroodi, J., Jawhar, I.: Middleware for robotics: A survey. In: 2008 IEEE Conference on Robotics, Automation and Mechatronics, pp. 736–742. IEEE (2008)Montemerlo, M., Roy, N., Thrun, S.: Perspectives on standardization in mobile robot programming: The carnegie mellon navigation (carmen) toolkit. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 3, pp. 2436–2441. IEEE (2003)Muñoz, M., Munera, E., Blanes, J.F., Simo, J.E., Benet, G.: Event driven middleware for distributed system control. XXXIV Jornadas de Automatica, 8 (2013)Muñoz, M., Munera, E., Blanes, J.F., Simó, J.E.: A hierarchical hybrid architecture for mission-oriented robot control. In: Armada, M.A., Sanfeliu, A., Ferre, M. (eds.) First Iberian Robotics Conference of ROBOT 2013. AISC, vol. 252, pp. 363–380. Springer, Heidelberg (2014)Sánchez, E.M., Alcobendas, M.M., Noguera, J.F.B., Gilabert, G.B., Ten, J.E.S.: A reliability-based particle filter for humanoid robot self-localization in RoboCup Standard Platform League. Sensors (Basel, Switzerland) 13(11), 14954–14983 (2013)Poza-Luján, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E.: Relationship between Quality of Control and Quality of Service in Mobile Robot Navigation. In: Omatu, S., De Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 557–564. Springer, Heidelberg (2012)Proetzsch, M., Luksch, T., Berns, K.: Development of complex robotic systems using the behavior-based control architecture iB2C. Robotics and Autonomous Systems 58(1), 46–67 (2010)Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: An open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3 (2009)Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation-mobile robot navigation with uncertainty in dynamic environments. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, vol. 1, pp. 35–40. IEEE (1999)Nicolau, V., Muñoz, M., Simó, J.: KertrolBot Platform: SiDiReLi: Distributed System with Limited Resources. Technical report, Institute of Control Systems and Industrial Computing - Polytechnic University of Valencia, Valencia, Spain (2011

Integrating Smart Resources in ROS-based systems to distribute services

[EN] Mobile robots execute complexes tasks that involve the management of several embedded sensors and actuators. Therefore, in many cases, a robot is characterized as an intelligent distributed system formed with a central unit, which manages the on-board embedded devices and distributes the tasks execution. Embedded devices are also evolving to more complex systems. These systems are developed not only for executing simple tasks but also for offering some advanced mechanisms. Thus, complex data processing, adaptive execution, or fault-tolerance routines are some common system features. The Smart Resource topology has been developed in order to manage these embedded systems. This topology offers high-level routines that rely on a certain physical hardware execution. Therefore, Smart Resources are defined as distributed services providers, which operates within some context and quality requirements. Provided services can adapt its execution in order accomplish the set requirements and maximize the system performance. How to improve the versatility of the Smart Resources by making their services compatibles with the Robot Operating System (ROS) is addressed along this work. This solution integrates all the execution mechanisms provided by ROS with the service distribution, adaptive execution, and fault-tolerance routines offered by the Smart Resources. This integration is tested through a set of experiments using the Turtlebot robot platform and a simulated version of it. In both approaches ROS mechanisms are used to access the Smart Resource Services. Finally, obtained results are used to characterize the performance of this proposal.Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): UPV-PAID-FPI-2013.Munera-Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2017). Integrating Smart Resources in ROS-based systems to distribute services. Advances in Distributed Computing and Artificial Intelligence Journal. 6(1):13-19. https://doi.org/10.14201/ADCAIJ2017611319S13196

Optimizations on semantic environment management: an application for humanoid robot home assistance

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This article introduces some optimization mechanisms focused on environment management, object recognition, and environment interaction. Although the generality of the presented system, this work will be focused on its application on home assistance humanoid robots. For this purpose, a generic environment formalization procedure for semantic scenery description is introduced. As the main contribution of this work, some techniques for a more efficient use of the environment knowledge are proposed. That way, the application of an areabased discrimination mechanism will avoid to process large amounts of data, useless in the current context, improving the object recognition, and characterizing the available interactions in the current area. Finally, the formalized description, and the optimization procedure, will be tested and verified on a specific home scenario using a humanoid robotThis work has been supported by the Spanish Science and Innovation Ministry MICINN under the CICYT project COBAMI: DPI2011-28507-C02-01/02. The responsibility for the content remains with the authors.Munera Sánchez, E.; Posadas-Yagüe, J.; Poza-Lujan, J.; Blanes Noguera, F.; Simó Ten, JE. (2014). Optimizations on semantic environment management: an application for humanoid robot home assistance. En 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE. 720-725. doi:10.1109/HUMANOIDS.2014.7041442S72072

Robot Behavior Architecture Based on Smart Resource Service Execution

[EN] Robot behavior definition aims to classify and specify the robot tasks execution. Behavior architecture design is crucial for proper robot operation performance. According to this, this work aims to establish a robot behavior architecture based on distributed intelligent services. Therefore, behavior definition is set in a high-level delegating the task execution to distributed services provided by network abstractions characterized as Smart Resources. In order to provide a mechanism to measure the performance of this architecture, an evaluation mechanisms based on a service performance composition is introduced. In order to test this proposal it is designed a real use case implementing the proposed robot behavior architecture on a real navigation task.Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT 866 project M2C2: Codiseño de sistemas de control con criticidad mixta basado en 867 misiones TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): 868 UPV-PAID-FPI-2013.Munera-Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2017). Robot Behavior Architecture Based on Smart Resource Service Execution. International Journal of Soft Computing And Artificial Intelligence (Online). 5(1):55-60. http://hdl.handle.net/10251/152272S55605

Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client s requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client s requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.This work has been supported by the Spanish Science and Innovation Ministry MICINN under the CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and the Programme for Research and Development PAID of the Polytechnic University of Valencia: UPV-PAID-FPI-2013. The responsibility for the content remains with the authors.Munera Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors. 15(8):18080-18101. https://doi.org/10.3390/s150818080S1808018101158Gupta, R. A., & Mo-Yuen Chow. (2010). Networked Control System: Overview and Research Trends. IEEE Transactions on Industrial Electronics, 57(7), 2527-2535. doi:10.1109/tie.2009.2035462Morales, R., Badesa, F. J., García-Aracil, N., Perez-Vidal, C., & Sabater, J. M. (2012). Distributed Smart Device for Monitoring, Control and Management of Electric Loads in Domotic Environments. Sensors, 12(5), 5212-5224. doi:10.3390/s120505212Zhang, Z. (2012). Microsoft Kinect Sensor and Its Effect. IEEE Multimedia, 19(2), 4-10. doi:10.1109/mmul.2012.24Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., Martínez-Sánchez, J., & Arias, P. (2013). 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Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(4), 453-464. doi:10.1016/j.riai.2013.08.002Castrillón-Santan, M., Lorenzo-Navarro, J., & Hernández-Sosa, D. (2014). Conteo de personas con un sensor RGBD comercial. Revista Iberoamericana de Automática e Informática Industrial RIAI, 11(3), 348-357. doi:10.1016/j.riai.2014.05.006Vogel, A., Kerherve, B., von Bochmann, G., & Gecsei, J. (1995). Distributed multimedia and QOS: a survey. IEEE Multimedia, 2(2), 10-19. doi:10.1109/93.388195Eugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078Aurrecoechea, C., Campbell, A. T., & Hauw, L. (1998). A survey of QoS architectures. Multimedia Systems, 6(3), 138-151. doi:10.1007/s005300050083Xu, W., Zhou, Z., Pham, D. T., Liu, Q., Ji, C., & Meng, W. (2012). Quality of service in manufacturing networks: a service framework and its implementation. The International Journal of Advanced Manufacturing Technology, 63(9-12), 1227-1237. doi:10.1007/s00170-012-3965-yKang, W., Son, S. H., & Stankovic, J. A. (2012). Design, Implementation, and Evaluation of a QoS-Aware Real-Time Embedded Database. IEEE Transactions on Computers, 61(1), 45-59. doi:10.1109/tc.2010.240Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., Simarro, R., & Benet, G. (2015). Distributed Sensor Architecture for Intelligent Control that Supports Quality of Control and Quality of Service. Sensors, 15(3), 4700-4733. doi:10.3390/s150304700Manzoor, A., Truong, H.-L., & Dustdar, S. (2014). Quality of Context: models and applications for context-aware systems in pervasive environments. The Knowledge Engineering Review, 29(2), 154-170. doi:10.1017/s0269888914000034Cardellini, V., Casalicchio, E., Grassi, V., Iannucci, S., Presti, F. L., & Mirandola, R. (2012). MOSES: A Framework for QoS Driven Runtime Adaptation of Service-Oriented Systems. IEEE Transactions on Software Engineering, 38(5), 1138-1159. doi:10.1109/tse.2011.68Nogueira, L., Pinho, L. M., & Coelho, J. (2012). A feedback-based decentralised coordination model for distributed open real-time systems. Journal of Systems and Software, 85(9), 2145-2159. doi:10.1016/j.jss.2012.04.033del-Hoyo, R., Martín-del-Brío, B., Medrano, N., & Fernández-Navajas, J. (2009). Computational intelligence tools for next generation quality of service management. Neurocomputing, 72(16-18), 3631-3639. doi:10.1016/j.neucom.2009.01.016Tian, Y.-C., Jiang, X., Levy, D. C., & Agrawala, A. (2012). Local Adjustment and Global Adaptation of Control Periods for QoC Management of Control Systems. IEEE Transactions on Control Systems Technology, 20(3), 846-854. doi:10.1109/tcst.2011.2141133Vilalta, R., & Drissi, Y. (2002). Artificial Intelligence Review, 18(2), 77-95. doi:10.1023/a:1019956318069Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018Yélamos, I., Escudero, G., Graells, M., & Puigjaner, L. (2009). Performance assessment of a novel fault diagnosis system based on support vector machines. Computers & Chemical Engineering, 33(1), 244-255. doi:10.1016/j.compchemeng.2008.08.008Zhang, X., Qiu, D., & Chen, F. (2015). Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis. Neurocomputing, 149, 641-651. doi:10.1016/j.neucom.2014.08.010Iplikci, S. (2010). Support vector machines based neuro-fuzzy control of nonlinear systems. Neurocomputing, 73(10-12), 2097-2107. doi:10.1016/j.neucom.2010.02.008Ferrari, P., Flammini, A., & Sisinni, E. (2011). New Architecture for a Wireless Smart Sensor Based on a Software-Defined Radio. IEEE Transactions on Instrumentation and Measurement, 60(6), 2133-2141. doi:10.1109/tim.2011.2117090Munera Sánchez, E., Muñoz Alcobendas, M., Blanes Noguera, J., Benet Gilabert, G., & Simó Ten, J. (2013). A Reliability-Based Particle Filter for Humanoid Robot Self-Localization in RoboCup Standard Platform League. Sensors, 13(11), 14954-14983. doi:10.3390/s131114954Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641-647. doi:10.1109/34.295913JIMÉNEZ-GARCÍA, J.-L., BASELGA-MASIA, D., POZA-LUJÁN, J.-L., MUNERA, E., POSADAS-YAGÜE, J.-L., & SIMÓ-TEN, J.-E. (2014). Smart device definition and application on embedded system: performance and optimi-zation on a RGBD sensor. ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 3(8), 46. doi:10.14201/adcaij2014384655Feng-Li Lian, Moyne, J., & Tilbury, D. (2002). Network design consideration for distributed control systems. 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Smart device definition and application on embedded system: performance and optimi-zation on a RGBD sensor

[EN] Embedded control systems usually are characterized by its limitations in terms of computational power and memory. Although this systems must deal with perpection and actuation signal adaptation and calculate control actions ensuring its reliability and providing a certain degree of fault tolerance. The allocation of these tasks between some different embedded nodes conforming a distributed control system allows to solve many of these issues. For that reason is proposed the application of smart devices aims to perform the data processing tasks related with the perception and actuation and offer a simple interface to be configured by other nodes in order to share processed information and raise QoS based alarms. In this work is introduced the procedure of implementing a smart device as a sensor as an embedded node in a distributed control system. In order to analyze its benefits an application based on a RGBD sensor implemented as a smart device is proposed.This work has been supported by the coordinated project COBAMI: Mission-based Hierarchical Control. Education and Science Department, Spanish Government. CICYT: MICINN:DPI2011-28507-C02-01/02 and project “Real time distributed control systems” of the Support Program for Research and Development 2012 UPV (PAID-06-12)Jimenez-Garcia, J.; Baselga-Masia, D.; Munera Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó-Ten, J. (2014). Smart device definition and application on embedded system: performance and optimi-zation on a RGBD sensor. ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal. 3(8):46-55. https://doi.org/10.14201/ADCAIJ2014384655S46553

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group