Integrated analysis of error detection and recovery

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms

Investigation Robot on Cables

International audienceThe Decontamination and Decommissioning (D&D) Operations take place in hostile environments to the human intervention. In this context, robotic systems are a solution to achieve these operations while minimizing human intervention. Among these operations, the monitoring of lengthwise plant has to be performed in places where the radioactive contaminations make hostile environments. These monitoring operations need to measure several physics parameters in several locations in a precise and safe manner. These repetitive tasks can be made by robots that are simple, small sized and easily adaptable. In this scope, a robotic system on cables has been designed to control and monitor the inside of lengthwise nuclear facilities. In lengthwise installations, the using of cables allows to increase the covered zone of the robots which is an essential quality in such places. Unlike cable-driven parallel robots used in workspaces of 2D or 3D controlled by redundant tensioning motors, the current robot moves in a 1D workspace. Thus a tractors cable controlled by one tensioning motor is used to drive the robot on carrier cables. In this paper, we will describe the first results of this work by bringing out the innovation of our system. Then, we will discuss the perspective to upgrade the actual system whose applications could be perfectly imagined outside the nuclear D&D industry

Detección automática y temprana del deterioro de pacientes en unidades de cuidados intensivos: desafíos tecnológicos y soluciones

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Automatic and early detection of the deterioration of patients in Intensive and Intermediate Care Units: technological challenges and solutions

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Automatic and early detection of the deterioration of patients in Intensive and Intermediate Care Units: technological challenges and solutions

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

Detección automática y temprana del deterioro de pacientes en unidades de cuidados intensivos: desafíos tecnológicos y soluciones

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.En las unidades de cuidados intensivos e intermedios de centros de salud, muchos sensores están conectados a los pacientes para medir datos fisiológicos de alta frecuencia. Para analizar el estado de un paciente, el personal médico requiere información presentada de manera apropiada y de fácil acceso. Como la mayoría del equipamiento médico no admite la extracción de datos digitales en formatos conocidos, el personal médico completa formularios manualmente. Esta metodología es propensa a errores humanos debido al gran volumen de información, con orígenes y complejidad variable. La detección automática y en tiempo real de cambios en los parámetros, basados en reglas médicas conocidas, permitirá evitar estos errores y, además, detectar el deterioro de forma temprana. En este artículo, proponemos una arquitectura de alto nivel del sistema, un sistema embebido que extrae la señal del electrocardiograma de una salida analógica de un monitor médico, y una infraestructura Big Data de tiempo real que integra productos Software Libre. Creemos que los resultados experimentales, obtenidos con un prototipo, demuestran la viabilidad de las técnicas y tecnologías utilizadas, dejando sólidas bases para la construcción de un sistema confiable para uso médico, y capaz de escalar para soportar un número creciente de pacientes y datos capturados.Facultad de Informátic

A user-friendly platform for yeast two-hybrid library screening using next generation sequencing

Yeast two-hybrid (Y2H) is a well-established genetics-based system that uses yeast to selectively display binary protein-protein interactions (PPIs). To meet the current need to unravel complex PPI networks, several adaptations have been made to establish mediumto high-throughput Y2H screening platforms, with several having successfully incorporated the use of the next-generation sequencing (NGS) technology to increase the scale and sensitivity of the method. However, these have been to date mainly restricted to the use of fully annotated custom-made open reading frame (ORF) libraries and subject to complex downstream data processing. Here, a streamlined Y2H library screening strategy, based on integration of Y2H with NGS, called Y2H-seq, was developed, which allows efficient and reliable screening of Y2H cDNA libraries. To generate proof of concept, the method was applied to screen for interaction partners of two key components of the jasmonate signaling machinery in the model plant Arabidopsis thaliana, resulting in the identification of several previously reported as well as hitherto unknown interactors. Our Y2H-seq method offers a user-friendly, specific and sensitive screening method that allows identification of PPIs without prior knowledge of the organism's ORFs, thereby extending the method to organisms of which the genome has not entirely been annotated yet. The quantitative NGS readout allows to increase genome coverage, thereby overcoming some of the bottlenecks of current Y2H technologies, which will further strengthen the value of the Y2H technology as a discovery platform