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Resultats de la cerca45 resultats

  • Estudio de la coordinación entre estaciones base en un sistema multi-usuario y multi-antena

    Querol Bataller, Elena

    (2012).Projecte fi de màster

    Se realiza un estudio de un sistema LTE-Advanced de 4G (aún en evolución y desarrollo), sobre sistemas coordinados (cooperación) y sistemas distribuidos para ver como reacciona el sistema ante diversas características como multiusuario, multiantena y macrocelda.

  • Optimal Gossip Algorithm for Distributed Consensus SVM Training in Wireless Sensor Networks

    Flouri, K.; Beferull-Lozano, B.; Tsakalides, P.

    (2010).Article

    In this paper, we consider the distributed training of a SVM using measurements collected by the nodes of a Wireless Sensor Network in order to achieve global consensus with the minimum possible inter-node communications for data exchange. We derive a novel mathematical characterization for the optimal selection of partial information that neighboring sensors should exchange in order to achieve consensus in the network. We provide a selection function which ranks the training vectors in order of importance in the learning process. The amount of information exchange can vary, based on an appropriately chosen threshold value of this selection function, providing a desired trade-off between...

    In this paper, we consider the distributed training of a SVM using measurements collected by the nodes of a Wireless Sensor Network in order to achieve global consensus with the minimum possible inter-node communications for data exchange. We derive a novel mathematical characterization for the optimal selection of partial information that neighboring sensors should exchange in order to achieve consensus in the network. We provide a selection function which ranks the training vectors in order of importance in the learning process. The amount of information exchange can vary, based on an appropriately chosen threshold value of this selection function, providing a desired trade-off between classification accuracy and power consumption. Through simulation experiments, we show that the proposed algorithm uses significantly less measurements to achieve a consensus that coincides with the optimal hyperplane obtained using a centralized SVM-based classifier that uses the entire sensor data at a fusion center.

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  • Closed-form Approximations for Cooperative LLR-based Energy Detection in Cognitive Radios

    Khalife, I.; Beferull-Lozano, B.

    (2010).Article

    In this paper, we obtain approximations for the optimal Log-Likelihood Ratio (LLR) decision rule in cooperative detection when local energy detectors are assumed. Considering conditional independence, we also show under which bandwidth and sampling frequency regimes these approximations hold best. Furthermore, we present simulations where the performance of the approximated LLR decision rule is compared with other sub- optimal decision rules given in the literature such as the optimal linear weighting. The simulations show that the density functions of the approximations exhibit negligible error in comparison with the exact ones, when conditions on bandwidth and sampling frequencies are...

    In this paper, we obtain approximations for the optimal Log-Likelihood Ratio (LLR) decision rule in cooperative detection when local energy detectors are assumed. Considering conditional independence, we also show under which bandwidth and sampling frequency regimes these approximations hold best. Furthermore, we present simulations where the performance of the approximated LLR decision rule is compared with other sub- optimal decision rules given in the literature such as the optimal linear weighting. The simulations show that the density functions of the approximations exhibit negligible error in comparison with the exact ones, when conditions on bandwidth and sampling frequencies are met. The approximations presented in this paper allow to perform efficiently the joint LLR decision rule for a set of nodes without requiring Monte-Carlo simulations.

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  • Accelerating consensus gossip algorithms: Sparsifying networks can be good for you

    Asensio, C.; Beferull-Lozano, B.

    (2010).Article

    In this paper, we consider the problem of improving the convergence speed of an average consensus gossip algorithm by sparsifying a sufficiently dense network graph. Thus, instead of adding links, as usually proposed in the literature, or globally optimizing the mixing matrix of the gossip algorithm for a given network, which requires global knowledge at every node, we find a sparser network that has better spectral properties and faster convergence than the original denser one. This allows to reduce simultaneously both the convergence time and the communication cost involved in the execution of the gossip algorithm. We first show why it is possible to sparsify a network while increasing...

    In this paper, we consider the problem of improving the convergence speed of an average consensus gossip algorithm by sparsifying a sufficiently dense network graph. Thus, instead of adding links, as usually proposed in the literature, or globally optimizing the mixing matrix of the gossip algorithm for a given network, which requires global knowledge at every node, we find a sparser network that has better spectral properties and faster convergence than the original denser one. This allows to reduce simultaneously both the convergence time and the communication cost involved in the execution of the gossip algorithm. We first show why it is possible to sparsify a network while increasing its convergence rate and also that there exists an optimal fraction of links to be removed. As a benchmark, we devise a centralized method that selects in an optimal way the set of links to be removed from the original network. Then, we propose a low complexity and scalable decentralized protocol requiring only local information at each node, which also generates a sparser network having a substantially better convergence rate. Simulation results are presented to verify and show clearly the efficiency of our approach.

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  • Finding Sparse Connectivity Patterns in Power-Constrained Ad-hoc Networks for accelerating consensus algorithms

    Asensio, C.; Beferull-Lozano, B.

    (2010).Article

    In this paper, we show how to critically sparsify a given network while improving the convergence rate of the associated average consensus algorithm. Thus, instead of adding new links or reallocating them, we propose novel distributed methods to nd much sparser networks with better convergence results than the original denser ones. We propose two distributed algorithms; a) in the first one, each node solves a local optimization problem using only its two-hop neighborhood, b) the second one is a distributed algorithm based on using, at each node, the power method. As compared with previous work, the reduction in the number of active links is doubled while improving the convergence rate and...

    In this paper, we show how to critically sparsify a given network while improving the convergence rate of the associated average consensus algorithm. Thus, instead of adding new links or reallocating them, we propose novel distributed methods to nd much sparser networks with better convergence results than the original denser ones. We propose two distributed algorithms; a) in the first one, each node solves a local optimization problem using only its two-hop neighborhood, b) the second one is a distributed algorithm based on using, at each node, the power method. As compared with previous work, the reduction in the number of active links is doubled while improving the convergence rate and having a much lower power consumption. Simulation results are presented to verify and show clearly the eciency of our approach.

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  • On Multiterminal source coding for temperature sensing

    Beferull-Lozano, B.; Konsbruck, R.

    (2010).Article

    We study the source coding problem in sensor networks deployed to monitor the evolution of spatio-temporal temperature distributions. The sensors sample the temperature field, quantize the samples and transmit the encoded samples through digital channels to some central unit, which computes an estimate of the original temperature field. Our analysis is based on the heat kernel's spectral properties, which are induced by the physics of heat diffusion. We determine rate distortion functions for various source coding schemes. In particular, we compare centralized coding, independent coding, Berger-Tung coding, and predictive quantization.

  • Joint Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks

    Portu-Repollés, E.; Beferull-Lozano, B.

    (2009).Article

    The authors consider a Wireless Sensor Network (WSN) deployed over a large geographical area, where a querying node wishes to perform a estimation of a localized phenomenon. The authors formulate the problem as a joint optimization of sensor selection and routing structure where they minimize the estimation distortion subject to a total communication power constraint for the WSN. Two scenarios are analyzed: measurement forwarding and estimation-and-forward at the nodes. The authors show that the optimization problems corresponding to these scenarios are both NP-hard and they propose two approximation algorithms.

  • Distributed Consensus Algorithms for SVM Training in Wireless Sensor Networks

    Flouri, K.; Beferull-Lozano, B.; Tsakalides, P.

    (2008).Article

    This paper studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion. We propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier, while minimizing the required amount of information exchange between neighbor sensors. The first proposed algorithm calls for the local exchange of support vectors between sensors, while the second technique requires the exchange of all sample vectors that define uniquely and completely the...

    This paper studies coordination and consensus mechanisms for Wireless sensor networks in order to train a Support Vector Machine (SVM) classifier in a distributed fashion. We propose two selective gossip algorithms, which take advantage of the sparse representation that SVMs provide for their decision boundary (hyperplane), in order to ensure convergence to an optimal or close-to-optimal classifier, while minimizing the required amount of information exchange between neighbor sensors. The first proposed algorithm calls for the local exchange of support vectors between sensors, while the second technique requires the exchange of all sample vectors that define uniquely and completely the convex hulls of the two classes. Through simulation experiments, we show that the proposed algorithms achieve a consensus close to the desired hyperplane obtained with a centralized SVM-based classifier that uses the entire sensor data.

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  • Rotation-Invariant Texture retrieval via Signature alignment based on steerable sub-Gaussian Modeling

    Tzagkarakis, G.; Beferull-Lozano, B.; Tsakalides, P.

    (2008).Article

    This paper addresses the construction of a novel efficient rotation-invariant texture retrieval method that is based on the alignment in angle of signatures obtained via a steerable sub-Gaussian model. In our proposed scheme, we first construct a steerable multivariate sub-Gaussian model, where the fractional lower-order moments of a given image are associated with those of its rotated versions. The feature extraction step consists of estimating the so-called covariations between the orientation subbands of the corresponding steerable pyramid at the same or at adjacent decomposition levels and building an appropriate signature that can be rotated directly without the need of rotating the...

    This paper addresses the construction of a novel efficient rotation-invariant texture retrieval method that is based on the alignment in angle of signatures obtained via a steerable sub-Gaussian model. In our proposed scheme, we first construct a steerable multivariate sub-Gaussian model, where the fractional lower-order moments of a given image are associated with those of its rotated versions. The feature extraction step consists of estimating the so-called covariations between the orientation subbands of the corresponding steerable pyramid at the same or at adjacent decomposition levels and building an appropriate signature that can be rotated directly without the need of rotating the image and recalculating the signature. The similarity measurement between two images is performed using a matrix-based norm that includes a signature alignment in angle between the images being compared, achieving in this way the desired rotation-invariance property. Our experimental results show how this retrieval scheme achieves a lower average retrieval error, as compared to previously proposed methods having a similar computational complexity, while at the same time being competitive with the best currently known state-of-the-art retrieval system. In conclusion, our retrieval method provides the best compromise between complexity and average retrieval performance.

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  • Space-frequency quantization using Directionlets

    Velisavljevic, V.; Beferull-Lozano, B.; Vetterli, M.

    (2007).Article

    In our previous work we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. Here, we combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional (2-D) wavelet transform (WT). We show that our new compression method outperforms the standard SFQ as well as the state-of-the-art compression methods, like SPIHT and JPEG-2000, in terms of the quality of compressed images, especially in a low-rate compression regime. We also show that the order of...

    In our previous work we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets. Here, we combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional (2-D) wavelet transform (WT). We show that our new compression method outperforms the standard SFQ as well as the state-of-the-art compression methods, like SPIHT and JPEG-2000, in terms of the quality of compressed images, especially in a low-rate compression regime. We also show that the order of computational complexity remains the same, as compared to the complexity of the standard SFQ algorithm.

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