17 research outputs found
Monitorización de sistemas con Bluemix
No full textHoy en día todos los sistemas informáticos generan grandes cantidades de información en los
llamados registros o logs de funcionamiento e incidencias, que debido al gran tráfico de datos y al
siempre creciente número de dispositivos llegan a alcanzar tamaños de varios Gigabytes, o incluso
Teras. El análisis de estos archivos resulta muy útil, y muchas veces esencial, de cara a detectar
comportamientos anómalos en el sistema o accesos no autorizados, prever posibles fallos futuros
o simplemente estudiar el rendimiento del equipo. Sin embargo, el abultado volumen de datos
previamente mencionado, así como la falta de una estructura clara y común en estos registros,
hacen del análisis de logs una tarea tediosa y complicada.
Entre las nuevas tecnologías que están creciendo últimamente se encuentran las herramientas
Big Data y la computación en la nube. Las técnicas de Big Data son aquellas que se emplean
en el análisis y tratamiento de grandes conjuntos de datos desestructurados que no puede ser
manejados con herramientas convencionales, y son cada vez más relevantes debido al exponencial
aumento de información digital. Por otra parte, la computación en la nube se encarga de ofrecer
servicios a través de Internet, que pueden ser usados de manera fácil y transparente por los
usuarios. Una de estas plataformas en la nube es IBM Bluemix, que ofrece herramientas y
servicios para desarrolladores.
El objetivo de este TFG ha sido desarrollar una aplicación de análisis de logs que sea fácil
e intuitiva de utilizar para un usuario sin conocimientos avanzados en informática, y que al
estar basada en tecnologías en la nube de Bluemix no haga necesario poseer un equipo con unos
requisitos especializados.
Para el desarrollo de la aplicación antes mencionada ha sido necesario tener conocimiento
acerca del análisis de logs y las técnicas y herramientas ya existentes, así como sobre las posibilidades
de la computación en la nube, especialmente del entorno IBM Bluemix, que es el que
ha sido utilizado. Para las distintas partes de la aplicación, dedicadas a la recolección, parseado,
almacenamiento, filtrado y visualización de la información, se han aprendido y utilizado diversas
tecnologías (DB2, Spring, Hadoop, etc) y lenguajes (Java, JavaScript, HTML, etc).Nowadays every computer system generates huge information quantities as operations and
incidences logs, that due to the great data traffic and the always growing number of devices can
reach sizes of several Gigabytes, and even Terabytes. The analysis of these files is really useful,
and sometimes even essential, when we want to detect anomalous behaviours in the system or
unauthorized accesses, prevent possible future failures or simply study the performance of a
machine. Nevertheless, the previously mentioned large volume of data, among the lack of a clear
and common log structure, make log analysis a tedious and complicated task.
Among the numerous technologies emerging these days we can find Big Data and Cloud
Computing tools. Big Data techniques are those used for the analysis and treatment of huge
unestructured data sets that can not be handled by conventional tools, and are becoming more
relevant every day as the amount of digital information increases. On the other hand, Cloud
Computing offers services through the Internet, that can be used easily and transparently by the
users. One of these cloud platforms is IBM Bluemix, that offers tools and services for developers.
The goal of this Bachelor Thesis was to develop a log analysis application, intuitive and easy
to use for an user without advanced computer knowledge, and since it is based in Bluemix cloud
technologies, it does not require to be run in a machine with any special requirements.
For the development of the aforementioned application, knowledge about Log Analysis, and
the techniques and tools used for it, was needed, and also about the possibilities of Cloud
Computing, specially the IBM Bluemix environment that was used. For the application different
parts, dedicated to gather, parse, store, filter and show the information, multiple technologies
were learnt and used (DB2, Spring, Hadoop, etc), as well as different programming languages
(Java, JavaScript, HTML, etc)
Tecnología software de tiempo real y su implementación en protocolos de ciclo cerrado para neurociencia experimental
No full textTesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 21-02-202
Real-Time software technology and its use in experimental neuroscience
No full textMáster Universitario en Investigación e Innovación en Tecnologías de la Información y las ComunicacionesDebido a las complicadas dinámicas no lineales de los sistemas neuronales así como a la incapacidad
existente a la hora de observar más de unas pocas de las señales que participan en
dichas dinámicas de forma simultánea, el estudio de estos sistemas es muy complejo. Además,
el paradigma tradicional de trabajo es el de estímulo-respuesta, en el cual se registra el comportamiento
del sistema al responder a ciertos estímulos de entrada y se estudian estos resultados a
posteriori, lo que impide caracterizar completamente la dinámica de su funcionamiento. Las tecnologías
de ciclo cerrado permiten superar estas adversidades mediante la observación, el control
y la interacción bidireccional con estos elementos neuronales. Sin embargo, la implementación
de este tipo de tecnologías no es tan sencilla debido a que en muchos casos la detección y estimulación
del sistema biológico debe hacerse de acuerdo a ciertas restricciones temporales precisas.
Esta capacidad del sistema para ejecutar tareas y responder a eventos externos (síncronos o
asíncronos) en una determinada franja de tiempo es lo que se conoce como funcionamiento en
"tiempo real".
Los ordenadores personales actuales poseen la suficiente potencia de procesamiento como
para cumplir con los requisitos de tiempo real, sin embargo debido al funcionamieno de los
planificadores de los sistemas operativos de propósito general (Windows, Linux, MacOS), que
no puede ser controlado por el usuario, no existe manera de asegurar que un proceso de tiempo
real se ejecutará sin interrupciones y cumpliendo con las restricciones temporales. Por otra
parte, las implementaciones en hardware pueden cumplir con dichas restricciones temporales
pero son menos programables. Por ello, existen también los llamados sistemas operativos de
tiempo real (RTOS). Sin embargo, esta tecnología es a menudo difícil de instalar, configurar y
manejar. Estas dificultades relativas a los RTOS provocan que muchos equipos y laboratorios
dedicados a la neurociencia no vean viable invertir tiempo y esfuerzo en dominar esta tecnología
para realizar experimentos de ciclo cerrado.
En este trabajo se realiza una comparativa cuantitativa de las herramientas para tiempo
real RTAI, Xenomai y Preempt-RT, de acuerdo a su rendimiento así como su usabilidad y accesibilidad,
en la que se compara sus valores de latencia y la variabilidad (jitter) de estos. La
comparativa se lleva a cabo en el contexto del uso de la tecnología de tiempo real en neurociencia
experimental. Además se ha desarrollado una librería de modelos neuronales y sinápticos
en tiempo real para su uso en circuitos híbridos, con neuronas vivas y modelos artificiales, y
experimentos de ciclo cerrado. El correcto funcionamiento de dicha librería ha sido probado
mediante su integración en circuitos híbridos, tanto con neuronas vivas como electrónicas, así
como con el manejo de un motor de pasos para la estimulación mecánica.Due to the complicated non linear dynamics of neuronal systems, as to the existing inability
to observe simultaneously more than a few signals of the ones involved in said dynamics, the
study of these systems is quite complex. Moreover, traditionally the working paradigm is the
stimulus-response one, where the system behaviour is recorded while it responds to certain input
stimuli and the results are studied afterwards, thus preventing the complete characterization of
the behavioural dynamics. Closed-loop technologies allow to overcome these difficulties through
online observation, control and bidirectional interaction with these neural elements. Nevertheless,
implementing this kind of technologies is not an easy task because in many cases the
detection and stimulation must be done within some precise temporal boundaries. This ability
of the system to complete tasks and respond to external events (synchronous and asynchronous)
within a determined time slot is known as "real-time" performance.
Actual computers have enough processing power and speed to comply with real-time requirements,
but due to the general purpose operating systems (Windows, Linux, MacOS) schedulers’
behaviour, which can not be controlled by the user, there is no way to ensure that a real-time
process will be run without interruptions and respecting the temporal restrictions. On the other
hand, hardware implementations can fullfil such boundaries, but are also less programmable.
For this reason the real-time operating systems (RTOS) exist. However, this technology is often
difficult to install, configure and use. This RTOS-related complications provoke that many neuroscience
researching teams and laboratories do not consider feasible to spend time and effort
to implement this tools for closed-loop experiments.
In this work a quantitative comparison between the real-time solution RTAI, Xenomai and
Preempt-RT is carried out, focusing on their performance, usability and accessibility, by comparing
their latency values and jitter. The comparison done in the context of real-time software
technology usage in experimental neuroscience. Furthermore, a real-time neuron and synapse
model library was developed for its use in hybrid circuits and closed-loop experiments. To validate
the correct functioning of said library it was used in hybrid circuits, with both electronic
and living neurons, and to control a stepper motor for mechanical stimulation
Reduced order modelling of combustion using convolutional neural network
No full textIt is well known that CFD simulations of a complex combustion system, such as Moderate or Intense Low-oxygen Dilution (MILD) combustion, requires consid- erable computational resources. This precludes various applications including the use of CFD in real time control systems. The idea of a reduced order model (ROM) was born from the desire to overcome this obstacle. A ROM, if properly instructed, returns the output of a requested CFD simulation in extremely short time. This one is an ideal mechanism with two basic gears: the input size reduction technique and the interpolation method. This project proposes a study on the applicability of convolutional neural network (CNN) as a dimensionality reduction technique. The code written for this purpose will be presented in detail, as well as pre and post processing. A sensibility analysis will be carry out to find out which parame- ters to adjust and how in order to achieve the optimum. Finally, the network will be compared in its peculiarity and its results with Principal Component Analysis (PCA), the technique used by the BURN group of Libre University of Bruxelles for the same purpose. Moreover with the desire to improve, we went further by trying to overcome the limits dictated by the rules of a legitimate comparation between PCA and CNN. Lastly, the author considers necessary to provide the theoretical basis in order to enrich and support what has just been described. Therefore, you will also find introductions / insights on MILD combustion, CFD of a combustion system, neural networks and the aspects related to them
Erratum to “Systematic versus on-demand early palliative care: A randomised clinical trial assessing quality of care and treatment aggressiveness near the end of life” [Eur J Cancer (2016) 69 (110–118)] (S095980491632487X)(10.1016/j.ejca.2016.10.004)
No full textThe publisher regrets that the collaborators for this paper were not listed as such within the author details of the published paper. The collaborators were published in the Acknowledgements and are as follows: Alberto Farolfi, Silvia Ruscelli, Martina Valgiusti, Sara Pini, Marina Faedi, Department of Medical Oncology, IRST IRCCS, Meldola; Angela Ragazzini, Unit of Biostatistics and Clinical Trials, IRST IRCCS, Meldola; Cristina Pittureri and Elena Amaducci, Palliative Care and Hospice Unit, AUSL Romagna, Cesena; Irene Guglieri, Psychooncology Service, Veneto Institute of Oncology IOV – IRCCS, Padua; Francesca Bergamo, Sara Lonardi, Department of Clinical and Experimental Oncology, Medical Oncology 1, Veneto Institute of Oncology IOV – IRCCS, Padua; Camilla Di Nunzio, Medical Oncology Unit, Oncology–Hematology Department, Guglielmo da Saliceto Hospital, Piacenza; Monica Bosco, Palliative Care Unit, Oncology–Hematology Department, Guglielmo da Saliceto Hospital, Piacenza; Barbara Bocci, Medical Oncology Unit, San Paolo Hospital, Milan; Alfina Bramanti and Chiara Gandini, Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia; Angela Buonadonna, Medical Oncology Unit, Aviano National Cancer Institute, Aviano; Alessandro Comandone, Medical Oncology Unit, Presidio Humanitas Gradenigo, Turin; Sonia Zoccali, Coordinamento Cure Palliative (supported by F.I.L.E., Leniterapia Italian Foundatio), Florence; Maria Simona Pino, Medical Oncology Unit, Oncology Department, S. Maria Annunziata Hospital, Florence; Davide Dalu, Palliative Care Unit, Oncology Department, L. Sacco Hospital, Milan; Pietro Sozzi, Oncology Unit, Ospedale degli Infermi, Ponderano; Alberto Gozza, Medical Oncology, Department of Medicine, E.O. Galliera Hospitals, Genoa; Monica Giordano and Carla Longhi, Oncology Unit, Sant'Anna Hospital, Como; Cristina Autelitano, Palliative Care Unit, Arcispedale S. Maria Nuova – IRCCS, Reggio Emilia; Teresa Gamucci, Oncology Unit, SS Trinità Hospital Sora, ASL Frosinone, Frosinone; Cataldo Mastromauro, Oncology Unit, ULSS 12 Veneziana, Venice; Rodolfo Scognamiglio, Hospice Nazareth, Mestre; Daniela Degiovanni, Palliative Care Unit, Casale Monferrato, ASL Alessandria; Federica Negri, Medical Oncology Unit, Istituti Ospitalieri, Cremona; Augusto Caraceni, Palliative Care, Pain Therapy and Rehabilitation Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan; and Luigi Montanari, Palliative Care Unit Ravenna, AUSL Romagna, Italy. The publisher would like to apologise for any inconvenience caused
Erratum to “Systematic versus on-demand early palliative care: A randomised clinical trial assessing quality of care and treatment aggressiveness near the end of life” [Eur J Cancer 69 (2016) 110–118]
No full textThe publisher regrets that the collaborators for this paper were not listed as such within the author details of the published paper. The collaborators were published in the Acknowledgements and are as follows: Alberto Farolfi, Silvia Ruscelli, Martina Valgiusti, Sara Pini, Marina Faedi, Department of Medical Oncology, IRST IRCCS, Meldola; Angela Ragazzini, Unit of Biostatistics and Clinical Trials, IRST IRCCS, Meldola; Cristina Pittureri and Elena Amaducci, Palliative Care and Hospice Unit, AUSL Romagna, Cesena; Irene Guglieri, Psychooncology Service, Veneto Institute of Oncology IOV – IRCCS, Padua; Francesca Bergamo, Sara Lonardi, Department of Clinical and Experimental Oncology, Medical Oncology 1, Veneto Institute of Oncology IOV – IRCCS, Padua; Camilla Di Nunzio, Medical Oncology Unit, Oncology–Hematology Department, Guglielmo da Saliceto Hospital, Piacenza; Monica Bosco, Palliative Care Unit, Oncology–Hematology Department, Guglielmo da Saliceto Hospital, Piacenza; Barbara Bocci, Medical Oncology Unit, San Paolo Hospital, Milan; Alfina Bramanti and Chiara Gandini, Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia; Angela Buonadonna, Medical Oncology Unit, Aviano National Cancer Institute, Aviano; Alessandro Comandone, Medical Oncology Unit, Presidio Humanitas Gradenigo, Turin; Sonia Zoccali, Coordinamento Cure Palliative (supported by F.I.L.E., Leniterapia Italian Foundatio), Florence; Maria Simona Pino, Medical Oncology Unit, Oncology Department, S. Maria Annunziata Hospital, Florence; Davide Dalu, Palliative Care Unit, Oncology Department, L. Sacco Hospital, Milan; Pietro Sozzi, Oncology Unit, Ospedale degli Infermi, Ponderano; Alberto Gozza, Medical Oncology, Department of Medicine, E.O. Galliera Hospitals, Genoa; Monica Giordano and Carla Longhi, Oncology Unit, Sant'Anna Hospital, Como; Cristina Autelitano, Palliative Care Unit, Arcispedale S. Maria Nuova – IRCCS, Reggio Emilia; Teresa Gamucci, Oncology Unit, SS Trinità Hospital Sora, ASL Frosinone, Frosinone; Cataldo Mastromauro, Oncology Unit, ULSS 12 Veneziana, Venice; Rodolfo Scognamiglio, Hospice Nazareth, Mestre; Daniela Degiovanni, Palliative Care Unit, Casale Monferrato, ASL Alessandria; Federica Negri, Medical Oncology Unit, Istituti Ospitalieri, Cremona; Augusto Caraceni, Palliative Care, Pain Therapy and Rehabilitation Department, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan; and Luigi Montanari, Palliative Care Unit Ravenna, AUSL Romagna, Italy. The publisher would like to apologise for any inconvenience caused
Automatic synapse parameter exploration for the interaction of living neurons and models
No full textSocioeconomic status as a risk factor for dementia death:individual participant meta-analysis of 86 508 men and women from the UK
No full textLife-course socioeconomic factors may have a role in dementia aetiology but there is a current paucity of studies. Meta-analyses of individual participant data would considerably strengthen this evidence base
RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience
No full textClosed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience
Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons
No full textBiohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goalThis research was supported by grants AEI/FEDER
PID2021-122347NB-100, PGC2018-095895-B-I00, and PID2020-
114867RB-I00 (funded by MCIN/AEI/10.13039/501100011033 and
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