775 resultados para mining data streams


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A 1.55 mu m InGaAsP-InP partly gain-coupled two-section DFB self-pulsation laser (SPL) with a varied ridge width has been fabricated. The laser produces self-pulsations with a frequency tuning range of more than 135 GHz. All-optical clock recovery from 40 Gb/s degraded data streams has been demonstrated. Successful lockings of the device at frequencies of 30 GHz, 40 GHz, 50 GHz, and 60 GHz to a 10 GHz sidemode injection are also conducted, which demonstrates the capability of the device for all-optical clock recovery at different frequencies. This flexibility of the device is highly desired for practical uses. Crown Copyright

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This paper describes an algorithm for scheduling packets in real-time multimedia data streams. Common to these classes of data streams are service constraints in terms of bandwidth and delay. However, it is typical for real-time multimedia streams to tolerate bounded delay variations and, in some cases, finite losses of packets. We have therefore developed a scheduling algorithm that assumes streams have window-constraints on groups of consecutive packet deadlines. A window-constraint defines the number of packet deadlines that can be missed in a window of deadlines for consecutive packets in a stream. Our algorithm, called Dynamic Window-Constrained Scheduling (DWCS), attempts to guarantee no more than x out of a window of y deadlines are missed for consecutive packets in real-time and multimedia streams. Using DWCS, the delay of service to real-time streams is bounded even when the scheduler is overloaded. Moreover, DWCS is capable of ensuring independent delay bounds on streams, while at the same time guaranteeing minimum bandwidth utilizations over tunable and finite windows of time. We show the conditions under which the total demand for link bandwidth by a set of real-time (i.e., window-constrained) streams can exceed 100% and still ensure all window-constraints are met. In fact, we show how it is possible to guarantee worst-case per-stream bandwidth and delay constraints while utilizing all available link capacity. Finally, we show how best-effort packets can be serviced with fast response time, in the presence of window-constrained traffic.

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Current low-level networking abstractions on modern operating systems are commonly implemented in the kernel to provide sufficient performance for general purpose applications. However, it is desirable for high performance applications to have more control over the networking subsystem to support optimizations for their specific needs. One approach is to allow networking services to be implemented at user-level. Unfortunately, this typically incurs costs due to scheduling overheads and unnecessary data copying via the kernel. In this paper, we describe a method to implement efficient application-specific network service extensions at user-level, that removes the cost of scheduling and provides protected access to lower-level system abstractions. We present a networking implementation that, with minor modifications to the Linux kernel, passes data between "sandboxed" extensions and the Ethernet device without copying or processing in the kernel. Using this mechanism, we put a customizable networking stack into a user-level sandbox and show how it can be used to efficiently process and forward data via proxies, or intermediate hosts, in the communication path of high performance data streams. Unlike other user-level networking implementations, our method makes no special hardware requirements to avoid unnecessary data copies. Results show that we achieve a substantial increase in throughput over comparable user-space methods using our networking stack implementation.

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A power and resource efficient ‘dynamic-range utilisation’ technique to increase operational capacity of DSP IP cores by exploiting redundancy in the data epresentation of sampled analogue input data, is presented. By cleverly partitioning dynamic-range into separable processing threads, several data streams are computed concurrently on the same hardware. Unlike existing techniques which act solely to reduce power consumption due to sign extension, here the dynamic range is exploited to increase operational capacity while still achieving reduced power consumption. This extends an existing system-level, power efficient framework for the design of low power DSP IP cores, which when applied to the design of an FFT IP core in a digital receiver system gives an architecture requiring 50% fewer multipliers, 12% fewer slices and 51%-56% less power.

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An orthogonal vector approach is proposed for the synthesis of multi-beam directional modulation (DM) transmitters. These systems have the capability of concurrently projecting independent data streams into different specified spatial directions while simultaneously distorting signal constellations in all other directions. Simulated bit error rate (BER) spatial distributions are presented for various multi-beam system configurations in order to illustrate representative examples of physical layer security performance enhancement that can be achieved.

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A 10 GHz Fourier Rotman lens enabled dynamic directional modulation (DM) transmitter is experimentally evaluated. Bit error rate (BER) performance is obtained via real-time data transmission. It is shown that Fourier Rotman DM functionality enhances system security performance in terms of narrower decodable low BER region and higher BER values associated with BER sidelobes especially under high signal to noise ratio (SNR) scenarios. This enhancement is achieved by controlled corruption of constellation diagrams in IQ space by orthogonal injection of interference. Furthermore, the paper gives the first report of a functional dual-beam DM transmitter, which has the capability of simultaneously projecting two independent data streams into two different spatial directions while simultaneously scrambling the information signals along all other directions.

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Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.

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Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

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The upcoming IEEE 802.11ac standard boosts the throughput of previous IEEE 802.11n by adding wider 80 MHz and 160 MHz channels with up to 8 antennas (versus 40 MHz channel and 4 antennas in 802.11n). This necessitates new 1-8 stream 256/512-point Fast Fourier Transform (FFT) / inverse FFT (IFFT) processing with 80/160 MSample/s throughput. Although there are abundant related work, they all fail to meet the requirements of IEEE 802.11ac FFT/IFFT on point size, throughput and multiple data streams at the same time. This paper proposes the first software defined FFT/IFFT architecture as a solution. By making use of a customised soft stream processor on FPGA, we show how a software defined FFT architecture can meet all the requirements of IEEE 802.11ac with low cost and high resource efficiency. When compared with dedicated Xilinx FFT core, our implementation exhibits only one third of the resources also up to three times of resource efficiency.

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End-stopped cells in cortical area V1, which combine out- puts of complex cells tuned to different orientations, serve to detect line and edge crossings (junctions) and points with a large curvature. In this paper we study the importance of the multi-scale keypoint representa- tion, i.e. retinotopic keypoint maps which are tuned to different spatial frequencies (scale or Level-of-Detail). We show that this representation provides important information for Focus-of-Attention (FoA) and object detection. In particular, we show that hierarchically-structured saliency maps for FoA can be obtained, and that combinations over scales in conjunction with spatial symmetries can lead to face detection through grouping operators that deal with keypoints at the eyes, nose and mouth, especially when non-classical receptive field inhibition is employed. Al- though a face detector can be based on feedforward and feedback loops within area V1, such an operator must be embedded into dorsal and ventral data streams to and from higher areas for obtaining translation-, rotation- and scale-invariant face (object) detection.

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Models of visual perception are based on image representations in cortical area V1 and higher areas which contain many cell layers for feature extraction. Basic simple, complex and end-stopped cells provide input for line, edge and keypoint detection. In this paper we present an improved method for multi-scale line/edge detection based on simple and complex cells. We illustrate the line/edge representation for object reconstruction, and we present models for multi-scale face (object) segregation and recognition that can be embedded into feedforward dorsal and ventral data streams (the “what” and “where” subsystems) with feedback streams from higher areas for obtaining translation, rotation and scale invariance.

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Avec les nouvelles technologies des réseaux optiques, une quantité de données de plus en plus grande peut être transportée par une seule longueur d'onde. Cette quantité peut atteindre jusqu’à 40 gigabits par seconde (Gbps). Les flots de données individuels quant à eux demandent beaucoup moins de bande passante. Le groupage de trafic est une technique qui permet l'utilisation efficace de la bande passante offerte par une longueur d'onde. Elle consiste à assembler plusieurs flots de données de bas débit en une seule entité de données qui peut être transporté sur une longueur d'onde. La technique demultiplexage en longueurs d'onde (Wavelength Division Multiplexing WDM) permet de transporter plusieurs longueurs d'onde sur une même fibre. L'utilisation des deux techniques : WDM et groupage de trafic, permet de transporter une quantité de données de l'ordre de terabits par seconde (Tbps) sur une même fibre optique. La protection du trafic dans les réseaux optiques devient alors une opération très vitale pour ces réseaux, puisqu'une seule panne peut perturber des milliers d'utilisateurs et engendre des pertes importantes jusqu'à plusieurs millions de dollars à l'opérateur et aux utilisateurs du réseau. La technique de protection consiste à réserver une capacité supplémentaire pour acheminer le trafic en cas de panne dans le réseau. Cette thèse porte sur l'étude des techniques de groupage et de protection du trafic en utilisant les p-cycles dans les réseaux optiques dans un contexte de trafic dynamique. La majorité des travaux existants considère un trafic statique où l'état du réseau ainsi que le trafic sont donnés au début et ne changent pas. En plus, la majorité de ces travaux utilise des heuristiques ou des méthodes ayant de la difficulté à résoudre des instances de grande taille. Dans le contexte de trafic dynamique, deux difficultés majeures s'ajoutent aux problèmes étudiés, à cause du changement continuel du trafic dans le réseau. La première est due au fait que la solution proposée à la période précédente, même si elle est optimisée, n'est plus nécessairement optimisée ou optimale pour la période courante, une nouvelle optimisation de la solution au problème est alors nécessaire. La deuxième difficulté est due au fait que la résolution du problème pour une période donnée est différente de sa résolution pour la période initiale à cause des connexions en cours dans le réseau qui ne doivent pas être trop dérangées à chaque période de temps. L'étude faite sur la technique de groupage de trafic dans un contexte de trafic dynamique consiste à proposer différents scénarios pour composer avec ce type de trafic, avec comme objectif la maximisation de la bande passante des connexions acceptées à chaque période de temps. Des formulations mathématiques des différents scénarios considérés pour le problème de groupage sont proposées. Les travaux que nous avons réalisés sur le problème de la protection considèrent deux types de p-cycles, ceux protégeant les liens (p-cycles de base) et les FIPP p-cycles (p-cycles protégeant les chemins). Ces travaux ont consisté d’abord en la proposition de différents scénarios pour gérer les p-cycles de protection dans un contexte de trafic dynamique. Ensuite, une étude sur la stabilité des p-cycles dans un contexte de trafic dynamique a été faite. Des formulations de différents scénarios ont été proposées et les méthodes de résolution utilisées permettent d’aborder des problèmes de plus grande taille que ceux présentés dans la littérature. Nous nous appuyons sur la méthode de génération de colonnes pour énumérer implicitement les cycles les plus prometteurs. Dans l'étude des p-cycles protégeant les chemins ou FIPP p-cycles, nous avons proposé des formulations pour le problème maître et le problème auxiliaire. Nous avons utilisé une méthode de décomposition hiérarchique du problème qui nous permet d'obtenir de meilleurs résultats dans un temps raisonnable. Comme pour les p-cycles de base, nous avons étudié la stabilité des FIPP p-cycles dans un contexte de trafic dynamique. Les travaux montrent que dépendamment du critère d'optimisation, les p-cycles de base (protégeant les liens) et les FIPP p-cycles (protégeant les chemins) peuvent être très stables.

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Les quatre principales activités de la gestion de risque thérapeutique comportent l’identification, l’évaluation, la minimisation, et la communication du risque. Ce mémoire aborde les problématiques liées à l’identification et à la minimisation du risque par la réalisation de deux études dont les objectifs sont de: 1) Développer et valider un outil de « data mining » pour la détection des signaux à partir des banques de données de soins de santé du Québec; 2) Effectuer une revue systématique afin de caractériser les interventions de minimisation de risque (IMR) ayant été implantées. L’outil de détection de signaux repose sur la méthode analytique du quotient séquentiel de probabilité (MaxSPRT) en utilisant des données de médicaments délivrés et de soins médicaux recueillis dans une cohorte rétrospective de 87 389 personnes âgées vivant à domicile et membres du régime d’assurance maladie du Québec entre les années 2000 et 2009. Quatre associations « médicament-événement indésirable (EI) » connues et deux contrôles « négatifs » ont été utilisés. La revue systématique a été faite à partir d’une revue de la littérature ainsi que des sites web de six principales agences réglementaires. La nature des RMIs ont été décrites et des lacunes de leur implémentation ont été soulevées. La méthode analytique a mené à la détection de signaux dans l'une des quatre combinaisons médicament-EI. Les principales contributions sont: a) Le premier outil de détection de signaux à partir des banques de données administratives canadiennes; b) Contributions méthodologiques par la prise en compte de l'effet de déplétion des sujets à risque et le contrôle pour l'état de santé du patient. La revue a identifié 119 IMRs dans la littérature et 1,112 IMRs dans les sites web des agences réglementaires. La revue a démontré qu’il existe une augmentation des IMRs depuis l’introduction des guides réglementaires en 2005 mais leur efficacité demeure peu démontrée.

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A new generation of advanced surveillance systems is being conceived as a collection of multi-sensor components such as video, audio and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are: the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers) and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronisation. The paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of Real Time Networks/Data Oriented Requirements Implementation Scheme (RTN/DORIS). It also proposes the implementation of these components using the well-known middleware technology Common Object Request Broker Architecture (CORBA). Results using this architecture for video surveillance are presented through an implemented prototype.

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We explore the influence of the choice of attenuation factor on Katz centrality indices for evolving communication networks. For given snapshots of a network observed over a period of time, recently developed communicability indices aim to identify best broadcasters and listeners in the network. In this article, we looked into the sensitivity of communicability indices on the attenuation factor constraint, in relation to spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We proposed relaxed communicability measures where the spectral radius bound on attenuation factor is relaxed and the adjacency matrix is normalised in order to maintain the convergence of the measure. Using a vitality based measure of both standard and relaxed communicability indices we looked at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We illustrated our findings with two examples of real-life networks, MIT reality mining data set of daily communications between 106 individuals during one year and UK Twitter mentions network, direct messages on Twitter between 12.4k individuals during one week.