The Experts below are selected from a list of 1680 Experts worldwide ranked by ideXlab platform
C.r. Johnson - One of the best experts on this subject based on the ideXlab platform.
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blind adaptive channel shortening by sum squared auto correlation minimization sam
IEEE Transactions on Signal Processing, 2003Co-Authors: J Balakrishnan, Richard K Martin, C.r. JohnsonAbstract:We propose a new blind, adaptive channel shortening algorithm for updating the coefficients of a time-domain equalizer in a system employing multicarrier modulation. The technique attempts to minimize the sum-squared auto-correlation terms of the effective channel impulse response outside a window of desired length. The proposed algorithm, known as "sum-squared auto-correlation minimization" (SAM), requires the Source Sequence to be zero-mean, white, and wide-sense stationary, and it is implemented as a stochastic gradient descent algorithm. Simulation results are provided, demonstrating the success of the SAM algorithm in an asymmetric digital subscriber loop (ADSL) system.
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blind adaptive channel shortening by sum squared auto correlation minimization sam
Asilomar Conference on Signals Systems and Computers, 2002Co-Authors: J Balakrishnan, Richard K Martin, C.r. JohnsonAbstract:We propose a new blind, adaptive channel shortening algorithm for updating a time-domain equalizer (TEQ) in a system employing multicarrier modulation. The technique attempts to minimize the sum-squared auto-correlation of the combined channel-TEQ impulse response outside a window of desired length. The proposed algorithm, sum-squared auto-correlation minimization (SAM), assumes the Source Sequence to be white and wide-sense stationary, and it is implemented as a stochastic gradient descent algorithm. Simulation results demonstrating the success of the SAM algorithm are provided.
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Fractionally-spaced constant modulus algorithm blind equalizer error surface characterization: effects of Source distributions
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1996Co-Authors: J.p. Leblanc, I. Fijalkow, C.r. JohnsonAbstract:The constant modulus algorithm (CMA) is a popular blind equalization algorithm. A common device used in demonstrating the convergence properties of CMA is the assumption that the Source Sequence is i.i.d. (independent, identically distributed). Previous results in the literature show that a finite length fractionally-spaced equalizer allows for perfect equalization of moving average channels (under certain channel conditions known as zero-forcing criteria). CMA has previously been shown to converge to such perfectly equalizing settings under an independent, platykurtic Source. This paper investigates the effect of the distribution from which an independent Source Sequence is drawn on the CMA error surface and stationary points in the perfectly-equalizable fractionally-sampled equalizer case. Results include symbolic identification of all stationary points, as well as the eigenvalues and eigenvectors associated with their Hessian matrix. Results show quantitatively the loss of error surface curvature (in both direction and magnitude) at all stationary points. Simulations included demonstrate the affect this has on convergence speed.
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ICASSP - Fractionally-spaced constant modulus algorithm blind equalizer error surface characterization: effects of Source distributions
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1996Co-Authors: J.p. Leblanc, Inbar Fijalkow, C.r. JohnsonAbstract:The constant modulus algorithm (CMA) is a popular blind equalization algorithm. A common device used in demonstrating the convergence properties of CMA is the assumption that the Source Sequence is i.i.d. (independent, identically distributed). Previous results in the literature show that a finite length fractionally-spaced equalizer allows for perfect equalization of moving average channels (under certain channel conditions known as zero-forcing criteria). CMA has previously been shown to converge to such perfectly equalizing settings under an independent, platykurtic Source. This paper investigates the effect of the distribution from which an independent Source Sequence is drawn on the CMA error surface and stationary points in the perfectly-equalizable fractionally-sampled equalizer case. Results include symbolic identification of all stationary points, as well as the eigenvalues and eigenvectors associated with their Hessian matrix. Results show quantitatively the loss of error surface curvature (in both direction and magnitude) at all stationary points. Simulations included demonstrate the affect this has on convergence speed.
Tsachy Weissman - One of the best experts on this subject based on the ideXlab platform.
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DCC - Compression for Similarity Identification: Computing the Error Exponent
Proceedings. Data Compression Conference, 2015Co-Authors: Amir Ingber, Tsachy WeissmanAbstract:We consider the problem of compressing discrete memory less data Sequences for the purpose of similarity identification, first studied by Ahlswede et al. (1997). In this setting, a Source Sequence is compressed, where the goal is to be able to identify whether the original Source Sequence is similar to another given Sequence (called the query Sequence). There is no requirement that the Source will be reproducible from the compressed version. In the case where no false negatives are allowed, a compression scheme is said to be reliable if the probability of error (false positive) vanishes as the Sequence length grows. The minimal compression rate in this sense, which is the parallel of the classical rate distortion function, is called the identification rate. The rate at which the error probability vanishes is measured by its exponent, called the identification exponent (which is the analog of the classical excess distortion exponent). While an information-theoretic expression for the identification exponent was found in past work, it is uncomputable due to a dependency on an auxiliary random variable with unbounded cardinality. The main result of this paper is a cardinality bound on the auxiliary random variable in the identification exponent, thereby making the quantity computable (solving the problem that was left open by Ahlswede et al.). The new proof technique relies on the fact that the Lagrangian in the optimization problem (in the expression for the exponent) can be decomposed by coordinate (of the auxiliary random variable). Then a standard Caratheodory - style argument completes the proof.
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Compression for Similarity Identification: Computing the Error Exponent
2015 Data Compression Conference, 2015Co-Authors: Amir Ingber, Tsachy WeissmanAbstract:We consider the problem of compressing discrete memory less data Sequences for the purpose of similarity identification, first studied by Ahlswede et al. (1997). In this setting, a Source Sequence is compressed, where the goal is to be able to identify whether the original Source Sequence is similar to another given Sequence (called the query Sequence). There is no requirement that the Source will be reproducible from the compressed version. In the case where no false negatives are allowed, a compression scheme is said to be reliable if the probability of error (false positive) vanishes as the Sequence length grows. The minimal compression rate in this sense, which is the parallel of the classical rate distortion function, is called the identification rate. The rate at which the error probability vanishes is measured by its exponent, called the identification exponent (which is the analog of the classical excess distortion exponent). While an information-theoretic expression for the identification exponent was found in past work, it is uncomputable due to a dependency on an auxiliary random variable with unbounded cardinality. The main result of this paper is a cardinality bound on the auxiliary random variable in the identification exponent, thereby making the quantity computable (solving the problem that was left open by Ahlswede et al.). The new proof technique relies on the fact that the Lagrangian in the optimization problem (in the expression for the exponent) can be decomposed by coordinate (of the auxiliary random variable). Then a standard Caratheodory - style argument completes the proof.
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An Implementable Scheme for Universal Lossy Compression of Discrete Markov Sources
2009 Data Compression Conference, 2009Co-Authors: Shirin Jalali, Andrea Montanari, Tsachy WeissmanAbstract:We present a new lossy compressor for discrete Sources. For coding a Source Sequence xn, the encoder starts by assigning a certain cost to each reconstruction Sequence. It then finds the reconstruction that minimizes this cost and describes it losslessly to the decoder via a universal lossless compressor. The cost of a Sequence is given by a linear combination of its empirical probabilities of some order k+1 and its distortion relative to the Source Sequence. The linear structure of the cost in the empirical count matrix allows the encoder to employ a Viterbi-like algorithm for obtaining the minimizing reconstruction Sequence simply. We identify a choice of coefficients for the linear combination in the cost function which ensures that the algorithm universally achieves the optimum rate-distortion performance of any Markov Source in the limit of large n, provided k is increased as o(log n).
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DCC - An Implementable Scheme for Universal Lossy Compression of Discrete Markov Sources
2009 Data Compression Conference, 2009Co-Authors: Shirin Jalali, Andrea Montanari, Tsachy WeissmanAbstract:We present a new lossy compressor for discrete Sources. For coding a Source Sequence $x^n$, the encoder starts by assigning a certain cost to each reconstruction Sequence. It then finds the reconstruction that minimizes this cost and describes it losslessly to the decoder via a universal lossless compressor. The cost of a Sequence is given by a linear combination of its empirical probabilities of some order $k+1$ and its distortion relative to the Source Sequence. The linear structure of the cost in the empirical count matrix allows the encoder to employ a Viterbi-like algorithm for obtaining the minimizing reconstruction Sequence simply. We identify a choice of coefficients for the linear combination in the cost function which ensures that the algorithm universally achieves the optimum rate-distortion performance of any Markov Source in the limit of large $n$, provided $k$ is increased as $o(\log n)$.
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Near optimal lossy Source coding and compression-based denoising via Markov chain Monte Carlo
2008 42nd Annual Conference on Information Sciences and Systems, 2008Co-Authors: Shirin Jalali, Tsachy WeissmanAbstract:We propose an implementable new universal lossy Source coding algorithm. The new algorithm utilizes two well- known tools from statistical physics and computer science: Gibbs sampling and simulated annealing. In order to code a Source Sequence xn, the encoder initializes the reconstruction block as xn = xn, and then at each iteration uniformly at random chooses one of the symbols of xn, and updates it. This updating is based on some conditional probability distribution which depends on a parameter beta representing inverse temperature, an integer parameter k = o(logn) representing context length, and the original Source Sequence. At the end of this process, the encoder outputs the Lempel-Ziv description of xn, which the decoder deciphers perfectly, and sets as its reconstruction. The complexity of the proposed algorithm in each iteration is linear in k and independent of n. We prove that, for any stationary ergodic Source, the algorithm achieves the optimal rate-distortion performance asymptotically in the limits of large number of iterations, beta, and n. We also show how our approach carries over to such problems as universal Wyner-Ziv coding and compression-based denoising.
F.m.j. Willems - One of the best experts on this subject based on the ideXlab platform.
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Switching between two universal Source coding algorithms
Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225), 1998Co-Authors: P.a.j. Volf, F.m.j. WillemsAbstract:This paper discusses a switching method which can be used to combine two sequential universal Source coding algorithms. The switching method treats these two algorithms as black-boxes and can only use their estimates of the probability distributions for the consecutive symbols of the Source Sequence. Three weighting algorithms based on this switching method are presented. Empirical results show that all three weighting algorithms give a performance better than the performance of the Source coding algorithms they combine.
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The context-tree weighting method: extensions
IEEE Transactions on Information Theory, 1998Co-Authors: F.m.j. WillemsAbstract:First we modify the basic (binary) context-tree weighting method such that the past symbols x/sub 1-D/, x/sub 2-D/, ..., x/sub 0/ are not needed by the encoder and the decoder. Then we describe how to make the context-tree depth D infinite, which results in optimal redundancy behavior for all tree Sources, while the number of records in the context tree is not larger than 2T-1. Here T is the length of the Source Sequence. For this extended context-tree weighting algorithm we show that with probability one the compression ratio is not larger than the Source entropy for Source Sequence length T/spl rarr//spl infin/ for stationary and ergodic Sources.
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The context-tree weighting method: basic properties
IEEE Transactions on Information Theory, 1995Co-Authors: F.m.j. Willems, Y.m. Shtarkov, T.j. TjalkensAbstract:Describes a sequential universal data compression procedure for binary tree Sources that performs the "double mixture." Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree Sources, and achieves a desirable coding distribution for tree Sources with an unknown model and unknown parameters. Computational and storage complexity of the proposed procedure are both linear in the Source Sequence length. The authors derive a natural upper bound on the cumulative redundancy of the method for individual Sequences. The three terms in this bound can be identified as coding, parameter, and model redundancy, The bound holds for all Source Sequence lengths, not only for asymptotically large lengths. The analysis that leads to this bound is based on standard techniques and turns out to be extremely simple. The upper bound on the redundancy shows that the proposed context-tree weighting procedure is optimal in the sense that it achieves the Rissanen (1984) lower bound.
Tomohiko Uyematsu - One of the best experts on this subject based on the ideXlab platform.
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Coding Theorems for Asynchronous Slepian–Wolf Coding Systems
IEEE Transactions on Information Theory, 2020Co-Authors: Tetsunao Matsuta, Tomohiko UyematsuAbstract:The Slepian-Wolf (SW) coding system is a Source coding system with two encoders and a decoder, where these encoders independently encode Source Sequences from two correlated Sources into codewords, and the decoder reconstructs both Source Sequences from the codewords. In this paper, we consider the situation in which the SW coding system is asynchronous, i.e., each encoder samples a Source Sequence with some unknown delay. We assume that delays are unknown but maximum and minimum values of possible delays are known to encoders and the decoder. We also assume that Sources are discrete stationary memoryless and the probability mass function (PMF) of the Sources is unknown but the system knows that it belongs to a certain set of PMFs. For this asynchronous SW coding system, we clarify the achievable rate region which is the set of rate pairs of encoders such that the decoding error probability vanishes as the blocklength tends to infinity. We show that this region does not always coincide with that of the synchronous SW coding system in which each encoder samples a Source Sequence without any delay.
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Coding Theorems for Asynchronous Slepian-Wolf Coding Systems
arXiv: Information Theory, 2019Co-Authors: Tetsunao Matsuta, Tomohiko UyematsuAbstract:The Slepian-Wolf (SW) coding system is a Source coding system with two encoders and a decoder, where these encoders independently encode Source Sequences from two correlated Sources into codewords, and the decoder reconstructs both Source Sequences from the codewords. In this paper, we consider the situation in which the SW coding system is asynchronous, i.e., each encoder samples a Source Sequence with some unknown delay. We assume that delays are unknown but maximum and minimum values of possible delays are known to encoders and the decoder. We also assume that Sources are discrete stationary memoryless and the probability mass function (PMF) of the Sources is unknown but the system knows that it belongs to a certain set of PMFs. For this asynchronous SW coding system, we clarify the achievable rate region which is the set of rate pairs of encoders such that the decoding error probability vanishes as the blocklength tends to infinity. We show that this region does not always coincide with that of the synchronous SW coding system in which each encoder samples a Source Sequence without any delay.
Amir Ingber - One of the best experts on this subject based on the ideXlab platform.
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DCC - Compression for Similarity Identification: Computing the Error Exponent
Proceedings. Data Compression Conference, 2015Co-Authors: Amir Ingber, Tsachy WeissmanAbstract:We consider the problem of compressing discrete memory less data Sequences for the purpose of similarity identification, first studied by Ahlswede et al. (1997). In this setting, a Source Sequence is compressed, where the goal is to be able to identify whether the original Source Sequence is similar to another given Sequence (called the query Sequence). There is no requirement that the Source will be reproducible from the compressed version. In the case where no false negatives are allowed, a compression scheme is said to be reliable if the probability of error (false positive) vanishes as the Sequence length grows. The minimal compression rate in this sense, which is the parallel of the classical rate distortion function, is called the identification rate. The rate at which the error probability vanishes is measured by its exponent, called the identification exponent (which is the analog of the classical excess distortion exponent). While an information-theoretic expression for the identification exponent was found in past work, it is uncomputable due to a dependency on an auxiliary random variable with unbounded cardinality. The main result of this paper is a cardinality bound on the auxiliary random variable in the identification exponent, thereby making the quantity computable (solving the problem that was left open by Ahlswede et al.). The new proof technique relies on the fact that the Lagrangian in the optimization problem (in the expression for the exponent) can be decomposed by coordinate (of the auxiliary random variable). Then a standard Caratheodory - style argument completes the proof.
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Compression for Similarity Identification: Computing the Error Exponent
2015 Data Compression Conference, 2015Co-Authors: Amir Ingber, Tsachy WeissmanAbstract:We consider the problem of compressing discrete memory less data Sequences for the purpose of similarity identification, first studied by Ahlswede et al. (1997). In this setting, a Source Sequence is compressed, where the goal is to be able to identify whether the original Source Sequence is similar to another given Sequence (called the query Sequence). There is no requirement that the Source will be reproducible from the compressed version. In the case where no false negatives are allowed, a compression scheme is said to be reliable if the probability of error (false positive) vanishes as the Sequence length grows. The minimal compression rate in this sense, which is the parallel of the classical rate distortion function, is called the identification rate. The rate at which the error probability vanishes is measured by its exponent, called the identification exponent (which is the analog of the classical excess distortion exponent). While an information-theoretic expression for the identification exponent was found in past work, it is uncomputable due to a dependency on an auxiliary random variable with unbounded cardinality. The main result of this paper is a cardinality bound on the auxiliary random variable in the identification exponent, thereby making the quantity computable (solving the problem that was left open by Ahlswede et al.). The new proof technique relies on the fact that the Lagrangian in the optimization problem (in the expression for the exponent) can be decomposed by coordinate (of the auxiliary random variable). Then a standard Caratheodory - style argument completes the proof.
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A strong converse for joint Source-channel coding
2012 IEEE International Symposium on Information Theory Proceedings, 2012Co-Authors: Da Wang, Amir Ingber, Yuval KochmanAbstract:We consider a discrete memoryless joint Source-channel setting. In this setting, if a Source Sequence is reconstructed with distortion below some threshold, we declare a success event. We prove that for any joint Source-channel scheme, if this threshold lower (better) than the optimum average distortion, then the success probability approaches zero as the block length increases. Furthermore, we show that the probability has an exponential behavior, and evaluate the optimal exponent. Surprisingly, the best exponential behavior is attainable by a separation-based scheme.
