884 resultados para cloud-based applications
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These slides present several 3-D reconstruction methods to obtain the geometric structure of a scene that is viewed by multiple cameras. We focus on the combination of the geometric modeling in the image formation process with the use of standard optimization tools to estimate the characteristic parameters that describe the geometry of the 3-D scene. In particular, linear, non-linear and robust methods to estimate the monocular and epipolar geometry are introduced as cornerstones to generate 3-D reconstructions with multiple cameras. Some examples of systems that use this constructive strategy are Bundler, PhotoSynth, VideoSurfing, etc., which are able to obtain 3-D reconstructions with several hundreds or thousands of cameras. En esta presentación se tratan varios métodos de reconstrucción 3-D para la obtención de la estructura geométrica de una escena que es visualizada por varias cámaras. Se enfatiza la combinación de modelado geométrico del proceso de formación de la imagen con el uso de herramientas estándar de optimización para estimar los parámetros característicos que describen la geometría de la escena 3-D. En concreto, se presentan métodos de estimación lineales, no lineales y robustos de las geometrías monocular y epipolar como punto de partida para generar reconstrucciones con tres o más cámaras. Algunos ejemplos de sistemas que utilizan este enfoque constructivo son Bundler, PhotoSynth, VideoSurfing, etc., los cuales, en la práctica pueden llegar a reconstruir una escena con varios cientos o miles de cámaras.
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In this work, a fiber-based optical powering (or power-by-light) system capable of providing more than 1 W is developed. The prototype was used in order to power a shunt regulator for controlling the activation and deactivation of solar panels in satellites. The work involves the manufacture of a light receiver (a GaAs multiple photovoltaic converter (MPC)), a power conditioning block, and a regulator and the implementation and characterization of the whole system. The MPC, with an active area of just 3.1 mm2, was able to supply 1 W at 5 V with an efficiency of 30%. The maximum measured device efficiency was over 40% at an input power (Pin) of 0.5 W. Open circuit voltage over 7 V was measured for Pin over 0.5 W. A system optoelectronic efficiency (including the optical fiber, connectors, and MPC) of 27% was measured at an output power (Pout) of 1 W. At Pout = 0.2 W, the efficiency was as high as 36%. The power conditioning block and the regulator were successfully powered with the system. The maximum supplied power in steady state was 0.2 W, whereas in transient state, it reached 0.44 W. The paper also describes the characterization of the system within the temperature range going from -70 to +100?°C.
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Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.
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In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.
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Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.
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Development of PCB-integrateable microsensors for monitoring chemical species is a goal in areas such as lab-on-a-chip analytical devices, diagnostics medicine and electronics for hand-held instruments where the device size is a major issue. Cellular phones have pervaded the world inhabitants and their usefulness has dramatically increased with the introduction of smartphones due to a combination of amazing processing power in a confined space, geolocalization and manifold telecommunication features. Therefore, a number of physical and chemical sensors that add value to the terminal for health monitoring, personal safety (at home, at work) and, eventually, national security have started to be developed, capitalizing also on the huge number of circulating cell phones. The chemical sensor-enabled “super” smartphone provides a unique (bio)sensing platform for monitoring airborne or waterborne hazardous chemicals or microorganisms for both single user and crowdsourcing security applications. Some of the latest ones are illustrated by a few examples. Moreover, we have recently achieved for the first time (covalent) functionalization of p- and n-GaN semiconductor surfaces with tuneable luminescent indicator dyes of the Ru-polypyridyl family, as a key step in the development of innovative microsensors for smartphone applications. Chemical “sensoring” of GaN-based blue LED chips with those indicators has also been achieved by plasma treatment of their surface, and the micrometer-sized devices have been tested to monitor O2 in the gas phase to show their full functionality. Novel strategies to enhance the sensor sensitivity such as changing the length and nature of the siloxane buffer layer are discussed in this paper.
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El objetivo principal del presente trabajo es estudiar y explotar estructuras que presentan un gas bidimensional de electrones (2DEG) basadas en compuestos nitruros con alto contenido de indio. Existen muchas preguntas abiertas, relacionadas con el nitruro de indio y sus aleaciones, algunas de las cuales se han abordado en este estudio. En particular, se han investigado temas relacionados con el análisis y la tecnología del material, tanto para el InN y heteroestructuras de InAl(Ga)N/GaN como para sus aplicaciones a dispositivos avanzados. Después de un análisis de la dependencia de las propiedades del InN con respecto a tratamientos de procesado de dispositivos (plasma y térmicos), el problema relacionado con la formación de un contacto rectificador es considerado. Concretamente, su dificultad es debida a la presencia de acumulación de electrones superficiales en la forma de un gas bidimensional de electrones, debido al pinning del nivel de Fermi. El uso de métodos electroquímicos, comparados con técnicas propias de la microelectrónica, ha ayudado para la realización de esta tarea. En particular, se ha conseguido lamodulación de la acumulación de electrones con éxito. En heteroestructuras como InAl(Ga)N/GaN, el gas bidimensional está presente en la intercara entre GaN y InAl(Ga)N, aunque no haya polarización externa (estructuras modo on). La tecnología relacionada con la fabricación de transistores de alta movilidad en modo off (E-mode) es investigada. Se utiliza un método de ataque húmedo mediante una solución de contenido alcalino, estudiando las modificaciones estructurales que sufre la barrera. En este sentido, la necesidad de un control preciso sobre el material atacado es fundamental para obtener una estructura recessed para aplicaciones a transistores, con densidad de defectos e inhomogeneidad mínimos. La dependencia de la velocidad de ataque de las propiedades de las muestras antes del tratamiento es observada y comentada. Se presentan también investigaciones relacionadas con las propiedades básicas del InN. Gracias al uso de una puerta a través de un electrolito, el desplazamiento de los picos obtenidos por espectroscopia Raman es correlacionado con una variación de la densidad de electrones superficiales. En lo que concierne la aplicación a dispositivos, debido al estado de la tecnología actual y a la calidad del material InN, todavía no apto para dispositivos, la tesis se enfoca a la aplicación de heteroestructuras de InAl(Ga)N/GaN. Gracias a las ventajas de una barrera muy fina, comparada con la tecnología de AlGaN/GaN, el uso de esta estructura es adecuado para aplicaciones que requieren una elevada sensibilidad, estando el canal 2DEG más cerca de la superficie. De hecho, la sensibilidad obtenida en sensores de pH es comparable al estado del arte en términos de variaciones de potencial superficial, y, debido al poco espesor de la barrera, la variación de la corriente con el pH puede ser medida sin necesidad de un electrodo de referencia externo. Además, estructuras fotoconductivas basadas en un gas bidimensional presentan alta ganancia debida al elevado campo eléctrico en la intercara, que induce una elevada fuerza de separación entre hueco y electrón generados por absorción de luz. El uso de metalizaciones de tipo Schottky (fotodiodos Schottky y metal-semiconductormetal) reduce la corriente de oscuridad, en comparación con los fotoconductores. Además, la barrera delgada aumenta la eficiencia de extracción de los portadores. En consecuencia, se obtiene ganancia en todos los dispositivos analizados basados en heteroestructuras de InAl(Ga)N/GaN. Aunque presentando fotoconductividad persistente (PPC), los dispositivos resultan más rápidos con respeto a los valores que se dan en la literatura acerca de PPC en sistemas fotoconductivos. ABSTRACT The main objective of the present work is to study and exploit the two-dimensionalelectron- gas (2DEG) structures based on In-related nitride compounds. Many open questions are analyzed. In particular, technology and material-related topics are the focus of interest regarding both InNmaterial and InAl(Ga)N/GaNheterostructures (HSs) as well as their application to advanced devices. After the analysis of the dependence of InN properties on processing treatments (plasma-based and thermal), the problemof electrical blocking behaviour is taken into consideration. In particular its difficulty is due to the presence of a surface electron accumulation (SEA) in the form of a 2DEG, due to Fermi level pinning. The use of electrochemical methods, compared to standard microelectronic techniques, helped in the successful realization of this task. In particular, reversible modulation of SEA is accomplished. In heterostructures such as InAl(Ga)N/GaN, the 2DEGis present at the interface between GaN and InAl(Ga)N even without an external bias (normally-on structures). The technology related to the fabrication of normally off (E-mode) high-electron-mobility transistors (HEMTs) is investigated in heterostructures. An alkali-based wet-etching method is analysed, standing out the structural modifications the barrier underwent. The need of a precise control of the etched material is crucial, in this sense, to obtain a recessed structure for HEMT application with the lowest defect density and inhomogeneity. The dependence of the etch rate on the as-grown properties is observed and commented. Fundamental investigation related to InNis presented, related to the physics of this degeneratematerial. With the help of electrolyte gating (EG), the shift in Raman peaks is correlated to a variation in surface eletron density. As far as the application to device is concerned, due to the actual state of the technology and material quality of InN, not suitable for working devices yet, the focus is directed to the applications of InAl(Ga)N/GaN HSs. Due to the advantages of a very thin barrier layer, compared to standard AlGaN/GaN technology, the use of this structure is suitable for high sensitivity applications being the 2DEG channel closer to the surface. In fact, pH sensitivity obtained is comparable to the state-of-the-art in terms of surface potential variations, and, due to the ultrathin barrier, the current variation with pH can be recorded with no need of the external reference electrode. Moreover, 2DEG photoconductive structures present a high photoconductive gain duemostly to the high electric field at the interface,and hence a high separation strength of photogenerated electron and hole. The use of Schottky metallizations (Schottky photodiode and metal-semiconductor-metal) reduce the dark current, compared to photoconduction, and the thin barrier helps to increase the extraction efficiency. Gain is obtained in all the device structures investigated. The devices, even if they present persistent photoconductivity (PPC), resulted faster than the standard PPC related decay values.
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With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used. In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost.
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The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops.
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Several boost-derived topologies are analyzed and compared for an aerospace application that uses a 100 V voltage bus. All these topologies have been designed and optimized considering the electrical requirements and the reduced number of space-qualified components. The comparison evaluates the power losses, mass, and dynamic response. Special attention has been paid to those topologies that may cancel the inherent right half plane zero (RHP) zero of the boost topology. Experimental results of the less common topologies are presented.
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While for years traditional wireless sensor nodes have been based on ultra-low power microcontrollers with sufficient but limited computing power, the complexity and number of tasks of today’s applications are constantly increasing. Increasing the node duty cycle is not feasible in all cases, so in many cases more computing power is required. This extra computing power may be achieved by either more powerful microcontrollers, though more power consumption or, in general, any solution capable of accelerating task execution. At this point, the use of hardware based, and in particular FPGA solutions, might appear as a candidate technology, since though power use is higher compared with lower power devices, execution time is reduced, so energy could be reduced overall. In order to demonstrate this, an innovative WSN node architecture is proposed. This architecture is based on a high performance high capacity state-of-the-art FPGA, which combines the advantages of the intrinsic acceleration provided by the parallelism of hardware devices, the use of partial reconfiguration capabilities, as well as a careful power-aware management system, to show that energy savings for certain higher-end applications can be achieved. Finally, comprehensive tests have been done to validate the platform in terms of performance and power consumption, to proof that better energy efficiency compared to processor based solutions can be achieved, for instance, when encryption is imposed by the application requirements.
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This paper proposes a low cost and complexity indoor location and navigation system using visible light communications and a mobile device. LED lamps work as beacons transmitting an identifier code so a mobile device can know its location. Experimental designs for transmitter and receiver interfaces are presented and potential applications are discussed.
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Regionalización de tipos de régimen natural de caudales en la cuenca del Ebro y validación biológica de los tipos de regímen natural.
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In recent years, many experimental and theoretical research groups worldwide have actively worked on demonstrating the use of liquid crystals (LCs) as adaptive lenses for image generation, waveform shaping, and non-mechanical focusing applications. In particular, important achievements have concerned the development of alternative solutions for 3D vision. This work focuses on the design and evaluation of the electro-optic response of a LC-based 2D/3D autostereoscopic display prototype. A strategy for achieving 2D/3D vision has been implemented with a cylindrical LC lens array placed in front of a display; this array acts as a lenticular sheet with a tunable focal length by electrically controlling the birefringence. The performance of the 2D/3D device was evaluated in terms of the angular luminance, image deflection, crosstalk, and 3D contrast within a simulated environment. These measurements were performed with characterization equipment for autostereoscopic 3D displays (angular resolution of 0.03 ).
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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 simul- taneously 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.