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Scorefunction quantization for distributed estimation
, 2007
"... We study the problem of quantization for distributed parameter estimation. We propose the design of scorefunction quantizers to optimize different metrics of estimation performance. Scorefunction quantizers are a class of quantizers known to maximize the Fisher Information for a fixed value of par ..."
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Cited by 1 (1 self)
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We study the problem of quantization for distributed parameter estimation. We propose the design of scorefunction quantizers to optimize different metrics of estimation performance. Scorefunction quantizers are a class of quantizers known to maximize the Fisher Information for a fixed value
Quantization For Distributed Estimation With Unknown Observation Statistics
 In Proceedings of the 31th Annual Conference on Information Sciences and Systems
, 1997
"... We consider the problem of quantizer design in a distributed estimation system with communication channels of limited capacity in the case where the observation statistics are unknown and only a training sequence is available. We consider the scheme of Cooperative DesignSeparate Encoding. We introd ..."
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Cited by 4 (3 self)
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We consider the problem of quantizer design in a distributed estimation system with communication channels of limited capacity in the case where the observation statistics are unknown and only a training sequence is available. We consider the scheme of Cooperative DesignSeparate Encoding. We
Quantization for distributed estimation in large scale sensor networks
 in Proc. ICISIP
, 2005
"... We study the problem of quantization for distributed parameter estimation in large scale sensor networks. Assuming a Maximum Likelihood estimator at the fusion center, we show that the Fisher Information is maximized by a scorefunction quantizer. This provides a tight bound on best possible MSE for ..."
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Cited by 4 (1 self)
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We study the problem of quantization for distributed parameter estimation in large scale sensor networks. Assuming a Maximum Likelihood estimator at the fusion center, we show that the Fisher Information is maximized by a scorefunction quantizer. This provides a tight bound on best possible MSE
Quantization For Distributed Estimation With Communication And Storage Constraints
"... We consider the problem of quantizer design in a distributed estimation system subject not only to communication constraints at the channels but also to storage constraints at the fusion center in the case where the observation statistics are unknown and only a training sequence is available. Our sc ..."
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We consider the problem of quantizer design in a distributed estimation system subject not only to communication constraints at the channels but also to storage constraints at the fusion center in the case where the observation statistics are unknown and only a training sequence is available. Our
2 Space efficient quantization for distributed estimation by a
, 2003
"... 3 multisensor fusion system ..."
Space Efficient Quantization for Distributed Estimation by a Multisensor Fusion System
, 2004
"... We present methods for designing quantizers for a distributed system that estimates a continuous quantity at a fusion center based on the observations of multiple sensors subject to communication constraints at the channels and to storage constraints at the fusion center. We consider the case where ..."
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Cited by 1 (0 self)
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We present methods for designing quantizers for a distributed system that estimates a continuous quantity at a fusion center based on the observations of multiple sensors subject to communication constraints at the channels and to storage constraints at the fusion center. We consider the case where
DIMENSIONALITY REDUCTION, COMPRESSION AND QUANTIZATION FOR DISTRIBUTED ESTIMATION WITH WIRELESS SENSOR NETWORKS ∗
"... Abstract. The distributed nature of observations collected by inexpensive wireless sensors necessitates transmission of the individual sensor data under stringent bandwidth and power constraints. These constraints motivate: i) a means of reducing the dimensionality of local sensor observations; ii) ..."
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) quantization of sensor observations prior to digital transmission; and iii) estimators based on the quantized digital messages. These three problems are addressed in the present paper. We start deriving linear estimators of stationary random signals based on reduceddimensionality observations
Least squares quantization in pcm
 IEEE Transactions on Information Theory
, 1982
"... AbstractIt has long been realized that in pulsecode modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as th ..."
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Cited by 1358 (0 self)
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as the number of quanta become large. The optimum quantization schemes for 26 quanta, b = 1,2, t,7, are given numerically for Gaussian and for Laplacian distribution of signal amplitudes. T I.
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding
 IEEE TRANS. ON INFORMATION THEORY
, 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing informationembedding rate, mini ..."
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Cited by 495 (15 self)
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, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortioncompensated QIM (DCQIM), and develop convenient realizations in the form of what we
Results 1  10
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