Results 1  10
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26
Distributed compressed sensing
, 2005
"... Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algori ..."
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Cited by 139 (25 self)
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Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the SlepianWolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In two of our three models, the results are asymptotically bestpossible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.
ATOMS OF ALL CHANNELS, UNITE! AVERAGE CASE ANALYSIS OF MULTICHANNEL SPARSE RECOVERY USING GREEDY ALGORITHMS
, 2007
"... ..."
Distributed compressed sensing of jointly sparse signals
 In Asilomar Conf. Signals, Sys., Comput
, 2005
"... Abstract—Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed coding al ..."
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Cited by 82 (6 self)
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Abstract—Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We present a second new model for jointly sparse signals that allows for joint recovery of multiple signals from incoherent projections through simultaneous greedy pursuit algorithms. We also characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. I.
Decentralized Compression and Predistribution via Randomized Gossiping
 in Proc. of the Fifth International Symposium on Information Processing in Sensor Networks (IPSN
, 2006
"... Developing energy efficient strategies for the extraction, transmission, and dissemination of information is a core theme in wireless sensor network research. In this paper we present a novel system for decentralized data compression and predistribution. The system simultaneously computes random pro ..."
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Cited by 81 (17 self)
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Developing energy efficient strategies for the extraction, transmission, and dissemination of information is a core theme in wireless sensor network research. In this paper we present a novel system for decentralized data compression and predistribution. The system simultaneously computes random projections of the sensor data and disseminates them throughout the network using a simple gossiping algorithm. These summary statistics are stored in an efficient manner and can be extracted from a small subset of nodes anywhere in the network. From these measurements one can reconstruct an accurate approximation of the data at all nodes in the network, provided the original data is compressible in a certain sense which need not be known to the nodes ahead of time. The system provides a practical and universal approach to decentralized compression and content distribution in wireless sensor networks. Two example applications, network health monitoring and field estimation, demonstrate the utility of our method.
Distributed sparse random projections for refinable approximation
 In IEEE/ACM Int. Symposium on Information Processing in Sensor Networks (IPSN
, 2007
"... berkeley.edu Consider a largescale wireless sensor network measuring compressible data, where n distributed data values can be wellapproximated using only k ≪ n coefficients of some known transform. We address the problem of recovering an approximation of the n data values by querying any L sensor ..."
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Cited by 61 (5 self)
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berkeley.edu Consider a largescale wireless sensor network measuring compressible data, where n distributed data values can be wellapproximated using only k ≪ n coefficients of some known transform. We address the problem of recovering an approximation of the n data values by querying any L sensors, so that the reconstruction error is comparable to the optimal kterm approximation. To solve this problem, we present a novel distributed algorithm based on sparse random projections, which requires no global coordination or knowledge. The key idea is that the sparsity of the random projections greatly reduces the communication cost of preprocessing the data. Our algorithm allows the collector to choose the number of sensors to query according to the desired approximation error. The reconstruction quality depends only on the number of sensors queried, enabling robust refinable approximation.
A fast reconstruction algorithm for deterministic compressive sensing using second order ReedMuller codes
 Conference on Information Sciences and Systems (CISS), Princeton, ISBN: 9781424422463, pp: 11  15
, 2008
"... Abstract—This paper proposes a deterministic compressed sensing matrix that comes by design with a very fast reconstruction algorithm, in the sense that its complexity depends only on the number of measurements n and not on the signal dimension N. The matrix construction is based on the second order ..."
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Cited by 30 (5 self)
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Abstract—This paper proposes a deterministic compressed sensing matrix that comes by design with a very fast reconstruction algorithm, in the sense that its complexity depends only on the number of measurements n and not on the signal dimension N. The matrix construction is based on the second order ReedMuller codes and associated functions. This matrix does not have RIP uniformly with respect to all ksparse vectors, but it acts as a near isometry on ksparse vectors with very high probability. I.
Recovery of jointly sparse signals from a few random projections
 Advances in Neural Information Processing Systems 18
, 2006
"... Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms ..."
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Cited by 29 (9 self)
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Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem in information theory for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays. 1
Avarage case analysis of multichannel thresholding
 Proc. ICASSP
, 2007
"... This paper introduces pthresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter results in much weaker conditions on the dic ..."
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Cited by 18 (10 self)
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This paper introduces pthresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter results in much weaker conditions on the dictionary. Numerical simulations confirm our theoretical findings and show that pthresholding is an interesting low complexity alternative to simultaneous greedy or convex relaxation algorithms for processing sparse multichannel signals with balanced coefficients. 1.
Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering
, 2010
"... We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor ..."
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Cited by 16 (0 self)
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We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multihop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [
Joint Sparsity Models for Distributed Compressed Sensing
"... Abstract — Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding ..."
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Cited by 16 (1 self)
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Abstract — Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail two simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the SlepianWolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In one of our models, the results are asymptotically bestpossible, meaning that both the upper and lower bounds match the performance of our practical algorithms. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays. I.