Person: Pagán Ortiz, Josué
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First Name
Josué
Last Name
Pagán Ortiz
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
Identifiers
13 results
Search Results
Now showing 1 - 10 of 13
- Project number: 290
Item Arquitecturas dinámica de redes inalámbricas en banda libre para la ejemplificación de conceptos de transmisión en aplicaciones de “Internet Of Things”(2016) Ayala Rodrigo, José Luis; Pagán Ortiz, Josué; Zapater Sancho, Marina; Del Barrio García, Alberto; Hermida Correa, RománEste proyecto de innovación educativa propone una metodología práctica y un equipamiento novedoso para la docencia de la asignatura de Redes y Servicios de Telecomunicación II impartida en tercer curso del Grado en Ingeniería Electrónica de Comunicaciones. Mediante la incorporación de elementos prácticos a la docencia como los recogidos en este proyecto, se pretende ahondar en los conceptos de uso espectral, acceso a un canal compartido, enrutamiento, topología de red, relación consumo vs. potencia de transmisión, etc. desde una perspectiva práctica que facilite el aprendizaje y despierte la curiosidad del alumnado. Para ello, se propondrá un despliegue de nodos inalámbricos, y un entorno de programación de éstos, que permita la evaluación de los contenidos antes descritos. Item 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 LuisWearable 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.Item Power-awareness and smart-resource management in embedded computing systems(2015) Santambrogio, Marco Domenico; Ayala Rodrigo, José Luis; Campanoni, Simone; Cattaneo, Riccardo; Durelli, Gianluca Carlo; Ferroni, Matteo; Nacci, Alessandro; Pagán Ortiz, Josué; Zapater, Marina; Vallejo, MónicaResources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960's. Therefore this is the time for a fresh approach to the way systems are designed and used.Item Método para determinar el nivel de activación del sistema trigémino-vascular(2016) Gago Veiga, Ana Beatriz; Sobrado Sanz, Mónica; Vivancos Mora, José Aurelio; Pagán Ortiz, Josué; De Orbe Izquierdo, María Irene; Ayala Rodrigo, José Luis; Fundación para la Investigación Biomédica del Hospital Universitario La PrincesaThe present invention describes a method for determining in real time the degree of activation of the trigeminovascular system. In particular, the invention can be applied in the field of medical devices capable of determining the activation index of the trigeminovascular system, mainly on the basis of the use of biomedical signals of hemodynamic character. The method establishes objective criteria for determining the degree of activation and is described as the result of the application of modelling and data fusion techniques. The method is also based on another type of signals, such as ambient signals, in order to improve statistically in real time the degree of activation determined.Item Robust modeling for information acquisition in biophysical and critical scenarios(2019) Pagán Ortiz, Josué; Ayala Rodrigo, José Luis; Risco Martín, José Luis; Moya Fernández, José ManuelThe era of information and Big Data is an environment where multiple devices, always connected, generate huge volumes of information (paradigm of the Internet of Things). This paradigm is present in different areas: the Smart Cities, sport tracking, lifestyle, or health. The goal of this thesis is the development and implementation of a Robust predictive modeling methodology using low cost wearable devices in biophysical and critical scenarios. In this manuscript we present a multilevel architecture that covers from the on-node data processing, up to the data management in Data Centers. The methodology applies energy aware optimization techniques at each level of the network. And the decision system makes use of data from different sources leading to expert decision system...Item Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios(Future Generation Computer Systems-The International Journal Of Escience, 2018) Pagán Ortiz, Josué; Zapater, Marina; Ayala Rodrigo, José LuisThe Internet of Things (IoT) holds big promises for healthcare, especially in proactive personal eHealth. Prediction of symptomatic crises in chronic diseases in the IoT scenario leads to the deployment of ambulatory monitoring systems. These systems place a major concern in the amount of data to be processed and the intelligent management of the energy consumption. The huge amount of data generated for these systems require high computing capabilities only available in Data Centers. This paper presents a real case of prediction in the eHealth scenario, devoted to neurological disorders. The presented case study focuses on the migraine headache, a disease that affects around 15% of the European population. This paper extrapolates results from real data and simulations in a study where migraine patients are monitored using an unobtrusive Wireless Body Sensor Network. Low-power techniques are applied in monitorization nodes. Techniques such us: on-node signal processing and radio policies to make node’s autonomy longer and save energy, have been applied. Workload balancing policies are carried out in the coordinator nodes and Data Centers to reduce the computational burden in these facilities and minimize its energy consumption. Our results draw average savings of € 288 million in this eHealth scenario applied only to 2% of European migraine sufferers; in addition to savings of € 1272 million due to the benefits of the migraine prediction.Item Monitorización ambulatoria no invasiva de variables biométricas en pacientes con migraña: ¿es posible predecir una crisis?(2014) Gago Veiga, Ana Beatriz; De Orbe Izquierdo, María Irene; Sobrado Sanz, Mónica; Pagán Ortiz, Josué; Carreras Rodríguez, María Teresa; Ayala Rodrigo, José Luis; Vivancos Mora, José Aurelio; ElsevierObjetivos: Definir parámetros de normalidad biométricos en pa-cientes con migraña para estimar su modificación en periodo de crisis sintomática vs periodos asintomáticos utilizando un sistema de monitorización inalámbrica no invasiva.Material y métodos: Estudio piloto. Observacional, prospectivo, longitudinal y comparativo antes-después. Pacientes seleccionados con diagnóstico de migraña con y sin aura (según ICHD-3 con, al menos, 2 migrañas/semana). Se monitorizaron durante 2 semanas, mediante un dispositivo basado en la plataforma inalámbrica de sensores PLUX-Wireless Biosignals, diversas variables biométricas (entre otras, sudoración, temperatura (Tª) y frecuencia cardíaca (FC).Resultados: Cinco pacientes monitorizados con aura (n = 3), sin aura (n = 2). Edad (24-57) años, 80% mujeres. Pacientes sin trata-miento preventivo (n = 4) y con beta-bloqueante (n = 1). Los análisis preliminares de los datos, expresados en media, relejan varia-ciones en la Tª (oC) y en la sudoración (%) entre: Las 5h-1h antes del aura/dolor: [(-0,4)-(+2,0)] oC y [(–1,7)-(+71,7)]%; 1h antes e inicio del aura/dolor [(-0,7) -(+1,7)] oC y [(-26,4)-(+16,2)]%; y el comienzo del aura e inicio del dolor [(-0,7)-(+0,6)] ºC y [(-1,1)- (9,9)]%. Las variaciones de FC resultan positivas y negativas en igual magnitud, haciendo no significativa esta variable para la predicción.Conclusiones: La variación observada en nuestro estudio, entre el periodo sintomático y asintomático en las variables biométricas de temperatura y sudoración puede relejar la afectación disautonómica del paciente migrañoso y la manera en que ésta se afecta. La secuencia temporal de los acontecimientos observados, abre la posibilidad de llegar a predecir una crisis.Item 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 LuisThe 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.Item Grammatical Evolutionary Techniques for Prompt Migraine Prediction(2016) Pagán Ortiz, Josué; Risco Martín, José Luis; José M. Moya; Ayala Rodrigo, José LuisThe 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.Item Advanced migraine prediction simulation system(2017) Pagán Ortiz, Josué; Moya Fernández, José Manuel; Risco Martín, José Luis; Ayala Rodrigo, José LuisIn 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.