Results 1  10
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11
Schemes for BiDirectional Modeling of Discrete Stationary Sources
, 2005
"... Adaptive models are developed to deal with bidirectional modeling of unknown discrete stationary sources, which can be generally applied to statistical inference problems such as noncausal universal discrete denoising that exploits bidirectional dependencies. Efficient algorithms for constructing ..."
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Cited by 16 (9 self)
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Adaptive models are developed to deal with bidirectional modeling of unknown discrete stationary sources, which can be generally applied to statistical inference problems such as noncausal universal discrete denoising that exploits bidirectional dependencies. Efficient algorithms for constructing those models are developed and implemented. Denoising is a primary focus of the application of those models, and we compare their performance to that of the DUDE algorithm [1] for universal discrete denoising.
Optimized concatenated LDPC codes for joint sourcechannel coding
 in Proc. IEEE Int. Symp. Information Theory, Seoul, Korea
, 2009
"... Abstract—In this paper a scheme for joint sourcechannel coding based on LDPC codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source and the other for channel coding, with joint belief propagation decoder. The asymptotic behavior is analyzed using ..."
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Cited by 4 (2 self)
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Abstract—In this paper a scheme for joint sourcechannel coding based on LDPC codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source and the other for channel coding, with joint belief propagation decoder. The asymptotic behavior is analyzed using EXtrinsic Information Transfer (EXIT) charts and this approximation is corroborated with illustrative experiments. The optimization of the degree distributions for our sparse code to maximize the information transmission rate is also considered. I.
Discrete denoising with shifts
 IEEE Trans. Inf. Theory
, 2007
"... We introduce SDUDE, a new algorithm for denoising DMCcorrupted data. The algorithm, which generalizes the recently introduced DUDE (Discrete Universal DEnoiser) of Weissman et al., aims to compete with a genie that has access, in addition to the noisy data, also to the underlying clean data, and c ..."
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Cited by 4 (2 self)
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We introduce SDUDE, a new algorithm for denoising DMCcorrupted data. The algorithm, which generalizes the recently introduced DUDE (Discrete Universal DEnoiser) of Weissman et al., aims to compete with a genie that has access, in addition to the noisy data, also to the underlying clean data, and can choose to switch, up to m times, between sliding window denoisers in a way that minimizes the overall loss. When the underlying data form an individual sequence, we show that the SDUDE performs essentially as well as this genie, provided that m is sublinear in the size of the data. When the clean data is emitted by a piecewise stationary process, we show that the SDUDE achieves the optimum distributiondependent performance, provided that the same sublinearity condition is imposed on the number of switches. To further substantiate the universal optimality of the SDUDE, we show that when the number of switches is allowed to grow linearly with the size of the data, any (sequence of) scheme(s) fails to compete in the above senses. Using dynamic programming, we derive an efficient implementation of the SDUDE, which has complexity (time and memory) growing only linearly with the data size and the number of switches m. Preliminary experimental results are presented, suggesting that SDUDE has the capacity to significantly improve on the performance attained by the original DUDE in applications where the nature of the data abruptly changes in time (or space), as is often the case in practice. Index Terms Discrete denoising, competitive analysis, individual sequence, universal algorithms, piecewise stationary processes, dynamic programming, discrete memoryless channel (DMC), switching experts, forwardbackward recursions. 1
Confidence Sets in Time–Series Filtering
"... Abstract—The problem of filtering of finite–alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed, such that the resulting set has the following properties: First, it includes the unknown signal with probability γ, wher ..."
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Cited by 3 (2 self)
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Abstract—The problem of filtering of finite–alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed, such that the resulting set has the following properties: First, it includes the unknown signal with probability γ, where γ is a parameter supplied to the filter. Second, the size of the confidence sets grows exponentially with the rate that is asymptotically equal to the conditional entropy of the signal given the data. Moreover, it is shown that this rate is optimal. I.
The iDUDE framework for grayscale image denoising
 IEEE Trans. IP
"... Image denosing, impulse noise, discrete universal denoising, dude ..."
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Cited by 3 (2 self)
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Image denosing, impulse noise, discrete universal denoising, dude
[ Proposing a lowdensity
"... paritycheck code] The objectives of this article are twofold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the fi ..."
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paritycheck code] The objectives of this article are twofold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the first part of the article, relevant contributions to JSC coding, ranging from the SlepianWolf problem to joint decoding of variable length codes with stateoftheart source codes, are reviewed and summarized. In the second part, a double
6. (algorithm) Discrete Universal DEnoiser
"... 3. (slang) A term of address for a man. 4. (archaic) A dandy, a man who is very concerned about his dress and appearance. 5. (slang) A cool person of either sex. ..."
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3. (slang) A term of address for a man. 4. (archaic) A dandy, a man who is very concerned about his dress and appearance. 5. (slang) A cool person of either sex.
unknown title
"... Abstract — We introduce SDUDE, a new algorithm for denoising DMCcorrupted data. The algorithm, which generalizes the recently introduced DUDE (Discrete Universal DEnoiser) of Weissman et al., aims to compete with a genie that has access, in addition to the noisy data, also to the underlying clean ..."
Abstract
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Abstract — We introduce SDUDE, a new algorithm for denoising DMCcorrupted data. The algorithm, which generalizes the recently introduced DUDE (Discrete Universal DEnoiser) of Weissman et al., aims to compete with a genie that has access, in addition to the noisy data, also to the underlying clean data, and can choose to switch, up to m times, between sliding window denoisers in a way that minimizes the overall loss. When the underlying data form an individual sequence, we show that the SDUDE performs essentially as well as this genie, provided that m is sublinear in the size of the data. When the clean data is emitted by a piecewise stationary process, we show that the SDUDE achieves the optimum distributiondependent performance, provided that the same sublinearity condition is imposed on the number of switches. To further substantiate the universal optimality of the SDUDE, we show that when the number of switches is allowed to grow linearly with the size of the data, any (sequence of) scheme(s) fails to compete in the above senses. Using dynamic programming, we derive an efficient implementation of the SDUDE, which has complexity (time and memory) growing only linearly with the data size and the number of switches m. Preliminary experimental results are presented, suggesting that SDUDE has the capacity to significantly improve on the performance attained by the original DUDE in applications where the nature of the data abruptly changes in time (or space), as is often the case in practice. I.
2011 IEEE International Symposium on Information Theory Proceedings Confidence Sets in Time–Series Filtering
"... Abstract—The problem of filtering of finite–alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed, such that the resulting set has the following properties: First, it includes the unknown signal with probability γ, wher ..."
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Abstract—The problem of filtering of finite–alphabet stationary ergodic time series is considered. A method for constructing a confidence set for the (unknown) signal is proposed, such that the resulting set has the following properties: First, it includes the unknown signal with probability γ, where γ is a parameter supplied to the filter. Second, the size of the confidence sets grows exponentially with the rate that is asymptotically equal to the conditional entropy of the signal given the data. Moreover, it is shown that this rate is optimal. I.