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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Now showing 1 - 10 of 24

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.
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.
Courseware management tool for engineering education
(6th World Multiconference on Systemics, Cybernetics and Informatics, Vol II, Proceedings: Concepts and Applications of Systemics, Cybernetics and Informatics I, 2002) López Orozco, José Antonio; Risco Martín, José Luis; Andrés Toro, Bonifacio de; Cruz García, Jesús Manuel de la
An courseware management tool has been designed and constructed in order to use as support in the education. The objective of this tool has been to build a template web to offer any subject. A simple database generation allows show the most important theoretical concepts, development exercises and practices, and the resolution of theoretical and practical tests. It includes exercises of checking and reinforcement, and guided practices. Theoretical, practical and problem tests allow the teacher to realize a follow-up of the student. The tool is reinforced with statistical study of these tests, discussion forums and direct communication between student and teacher for resolution of doubts so the pupil acquires the necessary foundations to deal subjects of higher courses. The courseware management tool has been applied for subjects of Control Systems in the new plans of study of the Universidad Complutense de Madrid (Electronic Engineering and Physical Sciences studies).
An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devices
(2017) Pagán Ortiz, Josué; Fallahzadeh, Ramin ; Ghasemzadeh, Hassan ; Moya, Jose M.; Risco Martín, José Luis; Ayala Rodrigo, José Luis
Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with limited battery autonomy. Several researchers focus their efforts in reducing the energy consumption of these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption of monitoring devices for the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of several sources of energy consumption: the running code for prediction, and the sensors for data acquisition. As a result of our methodology, we are able to improve the energy consumption of the computing process up to 90% with a minimal impact on accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.
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.
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.
EA-based ASV trajectory planner for detecting cyanobacterial blooms in freshwater
(2023) Carazo Barbero, Gonzalo; Besada Portas, Eva; Risco Martín, José Luis; López Orozco, José Antonio
Cyanobacterial Blooms (CBs) constitute a relevant ecological and public health problem since they often produce toxic metabolites that endanger the lives of many species, and they prevent human water consumption and recreational use. To determine the locations of CBs in lentic water bodies, we present a new planner based on Evolutionary Algorithms (EAs) that optimizes the trajectory of an Autonomous Surface Vehicle (ASV) equipped with a probe capable of detecting CBs. The planner 1) exploits the information provided by a particle transport simulator that determines the CB distribution from the water currents and the inherent CB behavior (in particular, its biological growth and vertical displacements) and 2) is supported by an EA that optimizes the mission duration, the ASV trajectory length, and the contributions of each simulated particle to the predicted cyanobacterial concentration along the ASV trajectory. The planner also ensures the trajectory feasibility from the ASV, probe, and water body perspective; and refines the trajectory shape by increasing the number of the decision variables during the iteration of an EA supported by usual NSGA-II operations. The results over different scenarios show that the planner determines overall good solutions that adapt the ASV trajectory to the evolution of CB distribution.
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.
Runtime data center temperature prediction using Grammatical Evolution techniques
(Applied soft computing, 2016) Zapater, Marina; Risco Martín, José Luis; Arroba, Patricia; Ayala Rodrigo, José Luis; Moya, José M.; Hermida Correa, Román
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.