938 resultados para System-based
Resumo:
In Europe, Cardiovascular Diseases (CVD) are the leading source of death, causing 45% of all deceases. Besides, Heart Failure, the paradigm of CVD, mainly affects people older than 65. In the current aging society, the European MyHeart Project was created, whose mission is to empower citizens to fight CVD by leading a preventive lifestyle and being able to be diagnosed at an early stage. This paper presents the development of a Heart Failure Management System, based on daily monitoring of Vital Body Signals, with wearable and mobile technologies, for the continuous assessment of this chronic disease. The System makes use of the latest technologies for monitoring heart condition, both with wearable garments (e.g. for measuring ECG and Respiration); and portable devices (such as Weight Scale and Blood Pressure Cuff) both with Bluetooth capabilities
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A novel HCPV nonimaging concentrator concept with high concentration (>500×) is presented. It uses the combination of a commercial concentration GaInP∕GaInAs∕Ge 3J cell and a concentration Back‐Point‐Contact (BPC) concentration silicon cell for efficient spectral utilization, and external confinement techniques for recovering the 3J cell′s reflection. The primary optical element (POE) is a flat Fresnel lens and the secondary optical element (SOE) is a free‐form RXI‐type concentrator with a band‐pass filter embedded it, both POE and SOE performing Köhler integration to produce light homogenization. The band‐pass filter sends the IR photons in the 900–1200 nm band to the silicon cell. Computer simulations predict that four‐terminal terminal designs could achieve ∼46% added cell efficiencies using commercial 39% 3J and 26% Si cells. A first proof‐of concept receiver prototype has been manufactured using a simpler optical architecture (with a lower concentration, ∼ 100× and lower simulated added efficiency), and experimental measurements have shown up to 39.8% 4J receiver efficiency using a 3J with peak efficiency of 36.9%
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Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.
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A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.
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sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approach
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Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.
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This article presents a solution to the problem of strong authentication, portable and expandable using a combination of Java technology and storage of X.509 digital certificate in Java cards to access services offered by an institution, in this case, the technology of the University of Panama, ensuring the authenticity, confidentiality, integrity and non repudiation.
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This paper addresses initial efforts to develop a navigation system for ground vehicles supported by visual feedback from a mini aerial vehicle. A visual-based algorithm computes the ground vehicle pose in the world frame, as well as possible obstacles within the ground vehicle pathway. Relying on that information, a navigation and obstacle avoidance system is used to re-plan the ground vehicle trajectory, ensuring an optimal detour. Finally, some experiments are presented employing a unmanned ground vehicle (UGV) and a low cost mini unmanned aerial vehicle (UAV).
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The objective of this paper is to design a path following control system for a car-like mobile robot using classical linear control techniques, so that it adapts on-line to varying conditions during the trajectory following task. The main advantages of the proposed control structure is that well known linear control theory can be applied in calculating the PID controllers to full control requirements, while at the same time it is exible to be applied in non-linear changing conditions of the path following task. For this purpose the Frenet frame kinematic model of the robot is linearised at a varying working point that is calculated as a function of the actual velocity, the path curvature and kinematic parameters of the robot, yielding a transfer function that varies during the trajectory. The proposed controller is formed by a combination of an adaptive PID and a feed-forward controller, which varies accordingly with the working conditions and compensates the non-linearity of the system. The good features and exibility of the proposed control structure have been demonstrated through realistic simulations that include both kinematics and dynamics of the car-like robot.
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Esta tesis propone un sistema biométrico de geometría de mano orientado a entornos sin contacto junto con un sistema de detección de estrés capaz de decir qué grado de estrés tiene una determinada persona en base a señales fisiológicas Con respecto al sistema biométrico, esta tesis contribuye con el diseño y la implementación de un sistema biométrico de geometría de mano, donde la adquisición se realiza sin ningún tipo de contacto, y el patrón del usuario se crea considerando únicamente datos del propio individuo. Además, esta tesis propone un algoritmo de segmentación multiescala para solucionar los problemas que conlleva la adquisición de manos en entornos reales. Por otro lado, respecto a la extracción de características y su posterior comparación esta tesis tiene una contribución específica, proponiendo esquemas adecuados para llevar a cabo tales tareas con un coste computacional bajo pero con una alta precisión en el reconocimiento de personas. Por último, este sistema es evaluado acorde a la norma estándar ISO/IEC 19795 considerando seis bases de datos públicas. En relación al método de detección de estrés, esta tesis propone un sistema basado en dos señales fisiológicas, concretamente la tasa cardiaca y la conductancia de la piel, así como la creación de un innovador patrón de estrés que recoge el comportamiento de ambas señales bajo las situaciones de estrés y no-estrés. Además, este sistema está basado en lógica difusa para decidir el grado de estrés de un individuo. En general, este sistema es capaz de detectar estrés de forma precisa y en tiempo real, proporcionando una solución adecuada para sistemas biométricos actuales, donde la aplicación del sistema de detección de estrés es directa para evitar situaciónes donde los individuos sean forzados a proporcionar sus datos biométricos. Finalmente, esta tesis incluye un estudio de aceptabilidad del usuario, donde se evalúa cuál es la aceptación del usuario con respecto a la técnica biométrica propuesta por un total de 250 usuarios. Además se incluye un prototipo implementado en un dispositivo móvil y su evaluación. ABSTRACT: This thesis proposes a hand biometric system oriented to unconstrained and contactless scenarios together with a stress detection method able to elucidate to what extent an individual is under stress based on physiological signals. Concerning the biometric system, this thesis contributes with the design and implementation of a hand-based biometric system, where the acquisition is carried out without contact and the template is created only requiring information from a single individual. In addition, this thesis proposes an algorithm based on multiscale aggregation in order to tackle with the problem of segmentation in real unconstrained environments. Furthermore, feature extraction and matching are also a specific contributions of this thesis, providing adequate schemes to carry out both actions with low computational cost but with certain recognition accuracy. Finally, this system is evaluated according to international standard ISO/IEC 19795 considering six public databases. In relation to the stress detection method, this thesis proposes a system based on two physiological signals, namely heart rate and galvanic skin response, with the creation of an innovative stress detection template which gathers the behaviour of both physiological signals under both stressing and non-stressing situations. Besides, this system is based on fuzzy logic to elucidate the level of stress of an individual. As an overview, this system is able to detect stress accurately and in real-time, providing an adequate solution for current biometric systems, where the application of a stress detection system is direct to avoid situations where individuals are forced to provide the biometric data. Finally, this thesis includes a user acceptability evaluation, where the acceptance of the proposed biometric technique is assessed by a total of 250 individuals. In addition, this thesis includes a mobile implementation prototype and its evaluation.
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Sensing systems in living bodies offer a large variety of possible different configurations and philosophies able to be emulated in artificial sensing systems. Motion detection is one of the areas where different animals adopt different solutions and, in most of the cases, these solutions reflect a very sophisticated form. One of them, the mammalian visual system, presents several advantages with respect to the artificial ones. The main objective of this paper is to present a system, based on this biological structure, able to detect motion, its sense and its characteristics. The configuration adopted responds to the internal structure of the mammalian retina, where just five types of cells arranged in five layers are able to differentiate a large number of characteristics of the image impinging onto it. Its main advantage is that the detection of these properties is based purely on its hardware. A simple unit, based in a previous optical logic cell employed in optical computing, is the basis for emulating the different behaviors of the biological neurons. No software is present and, in this way, no possible interference from outside affects to the final behavior. This type of structure is able to work, once the internal configuration is implemented, without any further attention. Different possibilities are present in the architecture to be presented: detection of motion, of its direction and intensity. Moreover, some other characteristics, as symmetry may be obtained.
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This paper presents an Ontology-Based multi-technology platform designed to avoid some issues of Building Automation Systems. The platform allows the integration of several building automation protocols, eases the development and implementation of different kinds of services and allows sharing information related to the infrastructure and facilities within a building. The system has been implemented and tested in the Energy Efficiency Research Facility at CeDInt-UPM.
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In this paper, an architecture based on a scalable and flexible set of Evolvable Processing arrays is presented. FPGA-native Dynamic Partial Reconfiguration (DPR) is used for evolution, which is done intrinsically, letting the system to adapt autonomously to variable run-time conditions, including the presence of transient and permanent faults. The architecture supports different modes of operation, namely: independent, parallel, cascaded or bypass mode. These modes of operation can be used during evolution time or during normal operation. The evolvability of the architecture is combined with fault-tolerance techniques, to enhance the platform with self-healing features, making it suitable for applications which require both high adaptability and reliability. Experimental results show that such a system may benefit from accelerated evolution times, increased performance and improved dependability, mainly by increasing fault tolerance for transient and permanent faults, as well as providing some fault identification possibilities. The evolvable HW array shown is tailored for window-based image processing applications.
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This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.
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EPICS (Experimental Physics and Industrial Control System) lies in a set of software tools and applications which provide a software infrastructure for building distributed data acquisition and control systems. Currently there is an increase in use of such systems in large Physics experiments like ITER, ESS, and FREIA. In these experiments, advanced data acquisition systems using FPGA-based technology like FlexRIO are more frequently been used. The particular case of ITER (International Thermonuclear Experimental Reactor), the instrumentation and control system is supported by CCS (CODAC Core System), based on RHEL (Red Hat Enterprise Linux) operating system, and by the plant design specifications in which every CCS element is defined either hardware, firmware or software. In this degree final project the methodology proposed in Implementation of Intelligent Data Acquisition Systems for Fusion Experiments using EPICS and FlexRIO Technology Sanz et al. [1] is used. The final objective is to provide a document describing the fulfilled process and the source code of the data acquisition system accomplished. The use of the proposed methodology leads to have two diferent stages. The first one consists of the hardware modelling with graphic design tools like LabVIEWFPGA which later will be implemented in the FlexRIO device. In the next stage the design cycle is completed creating an EPICS controller that manages the device using a generic device support layer named NDS (Nominal Device Support). This layer integrates the data acquisition system developed into CCS (Control, data access and communication Core System) as an EPICS interface to the system. The use of FlexRIO technology drives the use of LabVIEW and LabVIEW FPGA respectively. RESUMEN. EPICS (Experimental Physics and Industrial Control System) es un conjunto de herramientas software utilizadas para el desarrollo e implementación de sistemas de adquisición de datos y control distribuidos. Cada vez es más utilizado para entornos de experimentación física a gran escala como ITER, ESS y FREIA entre otros. En estos experimentos se están empezando a utilizar sistemas de adquisición de datos avanzados que usan tecnología basada en FPGA como FlexRIO. En el caso particular de ITER, el sistema de instrumentación y control adoptado se basa en el uso de la herramienta CCS (CODAC Core System) basado en el sistema operativo RHEL (Red Hat) y en las especificaciones del diseño del sistema de planta, en la cual define todos los elementos integrantes del CCS, tanto software como firmware y hardware. En este proyecto utiliza la metodología propuesta para la implementación de sistemas de adquisición de datos inteligente basada en EPICS y FlexRIO. Se desea generar una serie de ejemplos que cubran dicho ciclo de diseño completo y que serían propuestos como casos de uso de dichas tecnologías. Se proporcionará un documento en el que se describa el trabajo realizado así como el código fuente del sistema de adquisición. La metodología adoptada consta de dos etapas diferenciadas. En la primera de ellas se modela el hardware y se sintetiza en el dispositivo FlexRIO utilizando LabVIEW FPGA. Posteriormente se completa el ciclo de diseño creando un controlador EPICS que maneja cada dispositivo creado utilizando una capa software genérica de manejo de dispositivos que se denomina NDS (Nominal Device Support). Esta capa integra la solución en CCS realizando la interfaz con la capa EPICS del sistema. El uso de la tecnología FlexRIO conlleva el uso del lenguaje de programación y descripción hardware LabVIEW y LabVIEW FPGA respectivamente.