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  • Approximation of Continuous Media Models for Granular Systems Using Cellular Automata

    Pla Castells, M.; García Fernández I.; Martínez Durá, R. J.

    (2004). Article

    Lecture Notes in Computer Science, 2004, 230-237 In this paper a new cellular automata model suitable for granular systems simulation is presented. The proposed model is shown to be equivalent to a particularization of the well known BCRE model of granular systems and a correspondence between the parameters of the presented model and the BCRE model is also set, allowing to fit these parameters for a given system. The model has the advantage over other cellular automata models of being more realistic in the behavior of the surface of heaps and slopes. The dynamics of the CA is analyzed in order to confirm that it also has one of the most important features of these systems, 1/f noise.

  • Continuous force reaction in animation of avatars

    Rodríguez Cerro, A.; García Fernández, I.; Martínez Durá, R. J.

    (2004). Article

    SCA '10: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2010 Pre-recorded animation is the best way to reproduce detailed and natural human movements. However, in interactive animations we need techniques to modify the avatar movement in a feasible way. This research combines pre-recorded and physics-based animation to simulate the movement of the body when it is perturbed by external forces. The main results of this work are the possibility of separately animating different parts of the body and to obtain the reaction to forces that last long intervals of time.

  • Rotation-Invariant Features based on Steerable Transforms with an application to distributed image classification

    Beferull-Lozano, B.; Xie, H.; Ortega, A.

    (2004). Article

    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...

    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). Article

    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...

    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). Article

    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...

    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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  • On Network Correlated Data Gathering

    Cristescu, R.; Beferull-Lozano, B.; Vetterli, M.

    (2004). Article

    We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an...

    We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NP-complete and we propose various distributed approximation algorithms.

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  • Scaling Laws for Correlated Data Gathering

    Cristescu, R.; Beferull-Lozano, B.; Vetterli, M.

    (2004). Article

    Consider a set of correlated sources located at the nodes of a network, and a sink to which the data from all the sources have to arrive. We address the minimization of a separable joint communication cost function given by the product [rate] o [edge weight]. We present two possible approaches for rate allocation, namely Slepian-Wolf coding, and coding by explicit communication, and compare asymptotically (large networks) the associated total costs by finding their corresponding scaling laws and analyzing the ratio between them. We also provide the specific conditions on the correlation structure which determine the different cases of asymptotic behaviors.

  • Oversampled A/D Conversion of Non-Bandlimited Signals with Finite Rate of Innovation

    Jovanovic, I.; Befferull-Lozano, B.

    (2004). Article

    We consider the problem of A/D conversion for non-bandlimited signals that have a finite rate of innovation, in particular, the class of a continuous periodic stream of Diracs, characterized by a set of time positions and weights. Previous research has only considered the sampling of these signals, ignoring quantization which is necessary for any practical application (e.g. UWB, CDMA). In order to achieve accuracy under quantization, we introduce two types of oversampling, namely, oversampling in frequency and oversampling in time. High accuracy is achieved by enforcing the reconstruction to satisfy either three convex sets of constraints related to (1) sampling kernel, (2) quantization and...

    We consider the problem of A/D conversion for non-bandlimited signals that have a finite rate of innovation, in particular, the class of a continuous periodic stream of Diracs, characterized by a set of time positions and weights. Previous research has only considered the sampling of these signals, ignoring quantization which is necessary for any practical application (e.g. UWB, CDMA). In order to achieve accuracy under quantization, we introduce two types of oversampling, namely, oversampling in frequency and oversampling in time. High accuracy is achieved by enforcing the reconstruction to satisfy either three convex sets of constraints related to (1) sampling kernel, (2) quantization and (3) periodic streams of Diracs, which is then said to provide strong consistency, or only the first two, providing weak consistency. We propose three reconstruction algorithms, the first two achieving weak consistency and the third one achieving strong consistency. For these three algorithms, respectively, the experimental MSE performance for time positions decreases as O(1/Rt2 Rf3), and O(1/Rt2 Rf4), where Rt and Rf are the oversampling ratios in time and in frequency, respectively. It is also proved theoretically that our reconstruction algorithms satisfying weak consistency achieve an MSE performance of at least O(1/Rt2 Rf3).

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  • Error-Rate Dependence of Non-Bandlimited Signals with Finite Rate of Innovation

    Jovanovic, I.; Beferull-Lozano, B.

    (2004). Article

    Recent results in sampling theory [M. Vetterli et al., (2002)] showed that perfect reconstruction of nonbandlimited signals with finite rate of innovation can be achieved performing uniform sampling at or above the rate of innovation. We study analog-to-digital (A/D) conversion of these signals, introducing two types of oversampling and consistent reconstruction.

  • Power-Efficient Sensor Placement and Transmission Structure for Data Gathering under Distortion Constraints

    Ganesan, D.; Cristescu, R.; Beferull-Lozano, B.

    (2004). Article

    We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use joint entropy coding based on explicit communication between sensor nodes, and consider both maximum and average distortion bounds. The optimization is complex since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds. We address this problem...

    We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use joint entropy coding based on explicit communication between sensor nodes, and consider both maximum and average distortion bounds. The optimization is complex since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds. We address this problem by first looking at the simplified problem of optimal placement in the one-dimensional case. An analytical solution is derived for the case when there is a simple aggregation scheme, and numerical results are provided for the cases when joint entropy encoding is used. We use the insight from our 1-D analysis to extend our results to the 2-D case, and show that our algorithm for two-dimensional placement and transmission structure provides significant power benefit over a commonly used combination of uniformly random placement and shortest path trees.

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