Person:
Risco Martín, José Luis

First Name

José Luis

Last Name

Risco Martín

URI

https://hdl.handle.net/20.500.14352/78655

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Informática

Department

Arquitectura de Computadores y Automática

Area

Arquitectura y Tecnología de Computadores

Identifiers

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Search Results

Now showing 1 - 10 of 25

Grammatical Evolutionary Techniques for Prompt Migraine Prediction
(2016) Pagán Ortiz, Josué; Risco Martín, José Luis; José M. Moya; Ayala Rodrigo, José Luis
The migraine disease is a chronic headache presenting symptomatic crisis that causes high economic costs to the national health services, and impacts negatively on the quality of life of the patients. Even if some patients can feel unspecific symptoms before the onset of the migraine, these only happen randomly and cannot predict the crisis precisely. In our work, we have proved how migraine crisis can be predicted with high accuracy from the physiological variables of the patients, acquired by a non-intrusive Wireless Body Sensor Network. In this paper, we derive alternative models for migraine prediction using Grammatical Evolution techniques. We obtain prediction horizons around 20 minutes, which are sufficient to advance the drug intake and avoid the symptomatic crisis. The robustness of the models with respect to sensor failures has also been tackled to allow the practical implementation in the ambulatory monitoring platform. The achieved models are non linear mathematical expressions with low computing overhead during the run-time execution in the wearable devices.
Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities.
(Energy Procedia, 6th International conference on sustainability in energy and buildings, 2014) Arroba, Patricia; Risco Martín, José Luis; Zapater Sancho, Marina; Moya, José Manuel; Ayala Rodrigo, José Luis; Olcoz Herrero, Katzalin
As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simultaneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.
Project number: 172
Integración de los servicios para.TI@UCM en una plataforma de e-learning similar al Campus Virtual
(2014) Sánchez-Elez Martín, Marcos; Risco Martín, José Luis; Pardines Lence, María Inmaculada; Garnica Alcázar, Antonio Óscar; Miñana Ropero, María Guadalupe; Gómez Pérez, José Ignacio; Olcoz Herrero, Katzalin; Chaver Martínez, Daniel Ángel; Castro Rodríguez, Fernando; Sáez Alcaide, Juan Carlos; Igual Peña, Francisco Daniel
La integración de los servicios para.TI@UCM en nuestra Universidad hace plantearnos nuevas metodologías docentes y de evaluación en el proceso de enseñanza-aprendizaje. Este proyecto surge como continuación del proyecto PIMCD UCM 138 (2013) titulado “Uso de los servicios para.TI@UCM para integrar tareas docentes y fomentar el aprendizaje activo y colaborativo de los alumnos” desarrollado por este mismo grupo de profesores. Como resultado de este proyecto se han elaborado una serie de tutoriales sobre el uso de las aplicaciones de Google en el ámbito de las tareas docentes como herramientas útiles para fomentar el aprendizaje de los alumnos. Partiendo del nuevo marco docente creado en el PIMCD UCM 138 (2013) donde tanto el material docente como las actividades propuestas a los alumnos se desarrollan en la nube, el objetivo de este nuevo proyecto es conseguir integrar todas las aplicaciones necesarias para un desarrollo completo de la actividad docente en la nube (para.TI@UCM), tanto las propietarias de Google como las desarrolladas por terceros. Nuestro objetivo es intentar crear una plataforma de e-learning similar al Campus Virtual. Para realizar esta tarea será necesario realizar un estudio, por un lado, de las funcionalidades que ofrece el Campus Virtual, y por otro, de cuáles de estas funcionalidades están disponibles en los recursos para.TI@UCM. El siguiente paso sería plantear cómo se pueden implementar las funcionalidades buscadas y no encontradas en para.TI@UCM usando como base las aplicaciones de Google.
Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data
(Sensors, 2015) Pagán, Josué; De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco Martín, José Luis; Vivancos Mora, J.; Moya, José M.; Ayala Rodrigo, José Luis
Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.
ART-GCS: an adaptive real-time multi-agent ground control station
(2019 Spring Simulation Conference (SpringSim), 2019) Bonache Seco, Juan; López Orozco, José Antonio; Besada Portas, Eva; Risco Martín, José Luis
Ground Control Stations (GCS) are essential tools to monitor and command real-world complex missions involving Unmanned Vehicles (UVs). As the number and types of UVs in the mission grows, implementing a robust and adaptable GCS, capable of simplifying and reducing operator' interactions and mental workloads, becomes an engineering challenge. To address it, this paper presents a new Adaptive-Real-Time (ART)-GCS that 1) allows to monitor and control a runtime changing number of heterogeneous UVs, 2) adapt its GUI to the mission requirements and operators workload to minimize their fatigue and stress, and 3) provide support to experiments with actual and simulated UVs. To show its benefits in real-world missions, this paper presents a field experiment where, for safety reasons, a simulated unmanned aerial vehicle has to find an oil-spill that must be enclosed by a containment boom dragged by two real unmanned surface vehicles.
Advanced migraine prediction simulation system
(2017) Pagán Ortiz, Josué; Moya Fernández, José Manuel; Risco Martín, José Luis; Ayala Rodrigo, José Luis
In the Internet of Things (IoT) era, there is growing interest in wireless monitoring sensors for detection, classification and prediction of health symptoms. The prediction of symptoms in chronic diseases such as migraines brings new hope to improve patients' lives. The prediction of a migraine symptomatic event through monitoring hemodynamic variables has been previously demonstrated in our earlier work. In this paper, a simulation-based approach for a real-time migraine prediction system is described. The simulation has been implemented using the specifications of the formal description language Discrete EVent Systems (DEVS). The simulation system is a proof of concept that is ready for testing in a real-world ambulatory monitoring environment. The results obtained encourage developing a hardware/software (HW/SW) co-simulation system that incorporates Hardware-in-the-Loop (HIL) components as prior step to the expensive and slow hardware implementation of a complete migraine prediction device. When such a system is used in a real-time setting, it can simulate failures in sensors and trigger alarms for active patient response.
Una herramienta para gestión de cuestionarios
(II Jornada Campus Virtual UCM: cómo integrar investigación y docencia en el CV-UCM, 2005) López Orozco, José Antonio; Risco Martín, José Luis; Fernández-Valmayor Crespo, Alfredo; Fernández-Pampillón Cesteros, Ana María; Merino Granizo, Jorge
Se está desarrollando una herramienta de autor para la creación y gestión de cuestionarios de forma sencilla y fácil. El profesor con esta herramienta sólo tendrá que escribir las preguntas y respuestas de modo semejante a como lo hace en el procesador de textos MS-Word. Así se facilita el uso de formularios a aquellos profesores menos familiarizados con las nuevas tecnologías. Pero para cualquier usuario será un elemento muy útil, puesto que permite la creación de cuestionarios en formato WYSIWYG1 y dispondrá de los cuestionarios en un formato claro y cómodo de leer, corregir y mantener. La herramienta, a su vez, es independiente de la plataforma docente que se utilice para su visualización final. Generará código cualquier plataforma en la que se pueda importar las preguntas en un formato conocido como es texto, html, base de datos, etc. Como ejemplo se generará código para WebCT, pero se podrán construir librerías que permitan la generación de código en otras plataformas sin prejuicio de todo lo diseñado para formatos anteriores ni del funcionamiento de la herramienta construida.
A unified cloud-enabled discrete event parallel and distributed simulation architecture
(Simulation modelling practice and theory, 2022) Risco Martín, José Luis; Henares Vilaboa, Kevin; Mittal, Saurabh; Almendras Aruzamen, Luis Fernando; Olcoz Herrero, Katzalin
Cloud infrastructure provides rapid resource provision for on-demand computational require-ments. Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M & S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M & S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M & S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M & S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M & S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of 15.95x and 1.84x, respectively.
Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments
(Sensors, 2012) Zapater, Marina; Sanchez, Cesar; Ayala Rodrigo, José Luis; Moya, Jose M.; Risco Martín, José Luis
Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.
A Real-Time Framework for a DEVS-based MigrainePrediction Simulator System
(2016) Pagán Ortiz, Josué; Risco Martín, José Luis; Moya Fernández, José Manuel; Ayala Rodrigo, José Luis
The migraine disease is one of the most disabling neurological diseases that negatively impacts on the quality of life and on the cost of the public health services. The prediction of a migraine symptomatic event through monitorization of hemodynamic variables has been previously demonstrated in our previous works. In this paper, a first approach for the development of a simulator for a real time migraine prediction system is shown. The simulator has been implemented using a formal description language and validated using Grammatical Evolutionary models. The results encourage to develop real time techniques to trigger accurate alarms and real time repairing techniques of disrupted signals. All these problems will be faced in our future work by HW/SW co-simulation and including Hardware In the Loop components, in order to simulate failures in sensors or trigger alarms.