Resultats de la cerca45 resultats
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Efficient Distributed Multiresolution processing for data gathering in sensor networks
Acimovic, J.; Cristescu, R.; beferull-Lozano, B.
(2005). ArticleWe consider large sensor networks where the cost of collecting data from the network nodes to the data gathering sink is critical. We propose several algorithms that use limited local communication and distributed signal processing to make communication more efficient in terms of transmission cost. We consider a model that uses distributed wavelet-based signal processing. We first propose an algorithm that performs processing at nodes as data is forwarded to the sink. Then, we analyze algorithms that perform network division into groups of adaptive size and for which signal processing is applied separately to each group. We show by numerical simulations that such multiresolution approaches...
We consider large sensor networks where the cost of collecting data from the network nodes to the data gathering sink is critical. We propose several algorithms that use limited local communication and distributed signal processing to make communication more efficient in terms of transmission cost. We consider a model that uses distributed wavelet-based signal processing. We first propose an algorithm that performs processing at nodes as data is forwarded to the sink. Then, we analyze algorithms that perform network division into groups of adaptive size and for which signal processing is applied separately to each group. We show by numerical simulations that such multiresolution approaches result in significant improvements for data gathering in terms of total communication costs.
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Rotation-Invariant Texture Retrieval with gaussianized steerable pyramids
Tzagkarakis, G.; Beferull-Lozano, B.; Tsakalides, P.
(2005). ArticleThis paper presents a novel rotation-invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alpha-stable sub-Gaussian model to capture their non-Gaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity between two distinct texture images is measured by minimizing a rotation-invariant version of the Kullback-Leibler Divergence between their corresponding multivariate Gaussian...
This paper presents a novel rotation-invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alpha-stable sub-Gaussian model to capture their non-Gaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity between two distinct texture images is measured by minimizing a rotation-invariant version of the Kullback-Leibler Divergence between their corresponding multivariate Gaussian distributions, where the minimization is performed over a set of rotation angles.
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Lossy network correlated data gathering with high-resolution coding
Cristescu, R.; Beferull-Lozano,
(2005). ArticleWe consider a sensor network measuring correlated data, where the task is to gather all data from the network nodes to a sink. We consider the case where data at nodes is lossy coded with high-resolution, and the information measured by the nodes should be available at the sink within certain total and individual distortion bounds. First, we consider the problem of finding the optimal transmission structure and the rate-distortion allocations at the various spatially located nodes, such as to minimize the total power consumption cost of the network. We prove that the optimal transmission structure is the shortest path tree and that the problems of rate and distortion allocation separate in...
We consider a sensor network measuring correlated data, where the task is to gather all data from the network nodes to a sink. We consider the case where data at nodes is lossy coded with high-resolution, and the information measured by the nodes should be available at the sink within certain total and individual distortion bounds. First, we consider the problem of finding the optimal transmission structure and the rate-distortion allocations at the various spatially located nodes, such as to minimize the total power consumption cost of the network. We prove that the optimal transmission structure is the shortest path tree and that the problems of rate and distortion allocation separate in the high-resolution case, namely, we first find the distortion allocation as a function of the transmission structure, and then the rate allocation is computed. Then, we also study the case when the node positions can be chosen, by finding the optimal node placement when two different targets of interest are considered, namely total power minimization and network lifetime extension.
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Efficient routing with small buffers in dense networks
Barrenetxea, G.; Beferull-Lozano, B.; Vetterli, M.
(2005). ArticleThe analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's theorem that give more insight into the effect of routing algorithms on the queue size distributions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases....
The analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's theorem that give more insight into the effect of routing algorithms on the queue size distributions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases. Motivated by the insight gained in grid networks, we apply the same principles to the design of routing algorithms for random networks with finite buffers that minimize overflow losses. We show that this requires adequately combining shortest path tree routing and traveling salesman routing. Our results show that such specially designed routing algorithms increase the transmitted rate for a given loss probability up to almost three times, on average, with respect to the usual shortest path tree routing.
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Adaptive distributed algorithms for power-efficient data gathering in sensor networks
Acimovic, J.; Beferull-Lozano, B.; Cristescu, R.
(2005). ArticleIn this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution wavelet-based algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide...
In this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution wavelet-based algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide distributed algorithms that perform network division into groups of different sizes. The distribution of the group sizes within the network is the result of an optimal trade-off between the local communication inside each group needed to perform decorrelation, the communication needed to bring the processed data (coefficients) to the sink and the local computation cost, which grows as the network becomes larger. Our experimental results show clearly that important gains in power consumption can be obtained with respect to the case of not performing any distributed decorrelating processing.
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Approximation power of Directionlets
Velisavljevic, V.; Beferull-Lozano, B.; vetterli, M.; Dragotti, P. L.
(2005). ArticleIn spite of the success of the standard wavelet transform (WT) in image processing, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in only horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours), which are very important elements in visual perception, intersect too many wavelet basis functions and reduce the sparsity of the representation. To capture efficiently these anisotropic geometrical structures, a more complex multi-directional (M-DIR) and anisotropic transform is required. We present a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT (with the...
In spite of the success of the standard wavelet transform (WT) in image processing, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in only horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours), which are very important elements in visual perception, intersect too many wavelet basis functions and reduce the sparsity of the representation. To capture efficiently these anisotropic geometrical structures, a more complex multi-directional (M-DIR) and anisotropic transform is required. We present a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT (with the corresponding basis functions called directionlets) that retains the separable filtering and simple filter design from the standard two-dimensional (2-D) WT and imposes directional vanishing moments (DVM). Further-more, we show that this novel transform has non-linear approximation efficiency competitive to the other previously proposed over-sampled transform constructions.
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Networked Slepian-Wolf: Theory, Algorithms and Scaling Laws
Cristescu, R.; Beferull-Lozano, B.; Vetterli, M.
(2005). ArticleConsider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two...
Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure is found, which in general amounts to finding a Steiner tree, and second, the optimal rate allocation is obtained by solving an optimization problem with cost weights determined by the given optimal transmission structure, and with linear constraints given by the Slepian-Wolf rate region. For the case of data gathering, the optimal transmission structure is fully characterized and a closed-form solution for the optimal rate allocation is provided. For the general case of an arbitrary traffic matrix, the problem of finding the optimal transmission structure is NP-complete. For large networks, in some simplified scenarios, the total costs associated with Slepian-Wolf coding and explicit communication (conditional encoding based on explicitly communicated side information) are compared. Finally, the design of decentralized algorithms for the optimal rate allocation is analyzed.
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Rotation-Invariant Features based on Steerable Transforms with an application to distributed image classification
Beferull-Lozano, B.; Xie, H.; Ortega, A.
(2004). ArticleIn this paper, we propose a new rotation-invariant image retrieval system based on steerable pyramids and the concept of angular alignment across scales. First, we define energy-based texture features which are steerable under rotation, i.e., such that features corresponding to the rotated version of an image can be easily obtained from the features of the original (non-rotated) image. We also propose an approach to measure similarity between images that is robust to rotation; images are compared after being aligned in angle. The retrieval process is performed by means of a decision tree classifier where the angular alignment is performed at each node in the tree. To demonstrate the...
In this paper, we propose a new rotation-invariant image retrieval system based on steerable pyramids and the concept of angular alignment across scales. First, we define energy-based texture features which are steerable under rotation, i.e., such that features corresponding to the rotated version of an image can be easily obtained from the features of the original (non-rotated) image. We also propose an approach to measure similarity between images that is robust to rotation; images are compared after being aligned in angle. The retrieval process is performed by means of a decision tree classifier where the angular alignment is performed at each node in the tree. To demonstrate the effectiveness of our system we consider a distributed image classification system, where the feature encoder and the classifier are physically apart and thus features are compressed before being transmitted. Our results of retrieval performance versus rate show a clear gain with respect to a wavelet transform (as an example, for the same rate, the retrieval precision is increased from 40% to 65%).
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Rate-Distortion problem for Physics-based distributed sensing
Beferull-Lozano, B.; Konsbruck, R. L.; Vetterli, M.
(2004). ArticleWe consider the rate-distortion problem for sensing the continuous space-time physical temperature in a circular ring on which a heat source is applied over space and time, and which is also allowed to cool by radiation or convection to its surrounding medium. The heat source is modelled as a continuous space-time stochastic process which is bandlimited over space and time. The temperature field is the result of a circular convolution over space and a continuous-time causal filtering over time of the heat source with the Green's function corresponding to the heat equation, which is space and time invariant. The temperature field is sampled at uniform spatial locations by a set of sensors...
We consider the rate-distortion problem for sensing the continuous space-time physical temperature in a circular ring on which a heat source is applied over space and time, and which is also allowed to cool by radiation or convection to its surrounding medium. The heat source is modelled as a continuous space-time stochastic process which is bandlimited over space and time. The temperature field is the result of a circular convolution over space and a continuous-time causal filtering over time of the heat source with the Green's function corresponding to the heat equation, which is space and time invariant. The temperature field is sampled at uniform spatial locations by a set of sensors and it has to be reconstructed at a base station. The goal is to minimize the mean-square-error per second, for a given number of nats per second, assuming ideal communication channels between sensors and base station. We find a) the centralized Rc (D) function of the temperature field, where all the space-time samples can be observed and encoded jointly. Then, we obtain b) the Rs-i (D) function, where each sensor, independently, encodes its samples optimally over time and c) the Rst-i (D) function, where each sensor is constrained to encode also independently over time. We also study two distributed prediction-based approaches: a) with perfect feedback from the base station, where temporal prediction is performed at the base station and each sensor performs differential encoding, and b) without feedback, where each sensor locally performs temporal prediction.
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Networked Slepian-Wolf: Theory and Algorithms
Cristescu, R.; Beferull-Lozano, B.; Vetterli, M.
(2004). ArticleConsider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two...
Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure is found, which in general amounts to finding a Steiner tree, and second, the optimal rate allocation is obtained by solving an optimization problem with cost weights determined by the given optimal transmission structure, and with linear constraints given by the Slepian-Wolf rate region. For the case of data gathering, the optimal transmission structure is fully characterized and a closed-form solution for the optimal rate allocation is provided. For the general case of an arbitrary traffic matrix, the problem of finding the optimal transmission structure is NP-complete. For large networks, in some simplified scenarios, the total costs associated with Slepian-Wolf coding and explicit communication (conditional encoding based on explicitly communicated side information) are compared. Finally, the design of decentralized algorithms for the optimal rate allocation is analyzed.
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