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- Alon Orlitsky, Krishnamurthy Viswanathan, Junan Zhang
- IEEE Transactions on Information Theory
- 2005

Stopping sets determine the performance of low-density parity-check (LDPC) codes under iterative decoding over erasure channels. We derive several results on the asymptotic behavior of stopping sets in Tanner-graph ensembles, including the following. An expression for the normalized average stopping set distribution, yielding, in particular, a critical… (More)

- Chengwei Wang, Krishnamurthy Viswanathan, Choudur Lakshminarayan, Vanish Talwar, Wade Satterfield, Karsten Schwan
- 12th IFIP/IEEE International Symposium on…
- 2011

Online anomaly detection is an important step in data center management, requiring light-weight techniques that provide sufficient accuracy for subsequent diagnosis and management actions. This paper presents statistical techniques based on the Tukey and Relative Entropy statistics, and applies them to data collected from a production environment and to… (More)

- Alon Orlitsky, Narayana P. Santhanam, Krishnamurthy Viswanathan, Junan Zhang
- IEEE Transactions on Information Theory
- 2004

We determine the entropy rate of patterns of certain random processes including all finite-entropy stationary processes. For independent and identically distributed (i.i.d.) processes, we also bound the speed at which the per-symbol pattern entropy converges to this rate, and show that patterns satisfy an asymptotic equipartition property. To derive some of… (More)

Many statistical properties are determined not by the probabilities of the possible outcomes, but just by the multiset of these probabilities. We estimate this probability multiset from an observed data sample. Akin to standard maximum likelihood, which maximizes the probability of the observed outcomes, we propose profile maximum likelihood (PML), which… (More)

- Krishnamurthy Viswanathan, Ram Swaminathan
- SODA
- 2008

Consider a binary string <b>x</b> of length <i>n</i> transmitted <i>m</i> times over a memoryless channel that randomly inserts, deletes and flips bits. We consider the problem of reconstructing <b>x</b> from the collection of <i>m</i> binary strings received at the output of the channel. We present an algorithm for this problem and show that, as <i>n</i>… (More)

We consider the problem of estimating the distribution underlying an observed sample of data. Instead of maximum likelihood, which maximizes the probability of the observed values, we propose a different estimate, the high-profile distribution, which maximizes the probability of the observed profile—the number of symbols appearing any given number of times.… (More)

- Alon Orlitsky, Krishnamurthy Viswanathan
- IEEE Transactions on Information Theory
- 2003

We establish a further connection between one-way communication where a sender conveys information to a receiver who has related information, and error-correction coding where a sender attempts to communicate reliably over a noisy channel. Using this connection we obtain three results on the two problems. We derive an often-tight lower bound on the number… (More)

- Erik Ordentlich, Gadiel Seroussi, Sergio Verdú, Krishnamurthy Viswanathan
- IEEE Transactions on Information Theory
- 2008

In many applications, an uncompressed source stream is systematically encoded by a channel code (which ignores the source redundancy) for transmission over a discrete memoryless channel. The decoder knows the channel and the code but does not know the source statistics. This paper proposes several universal channel decoders that take advantage of the source… (More)

- Ron M. Roth, Krishnamurthy Viswanathan
- 2007 IEEE International Symposium on Information…
- 2007

The Gale-Berlekamp (in short, GB) code is the dual code of the binary product code in which the horizontal and vertical constituent codes are both the parity code. It is shown that the problem of deciding whether there is a codeword of the GB code within a prescribed distance from a given received word, is NP-complete. The problem remains hard (in a… (More)

- Alon Orlitsky, N. P. Santhanam, Krishnamurthy Viswanathan
- 2007 IEEE International Symposium on Information…
- 2007

We estimate the population size by sampling uniformly from the population. Given an accuracy to which we need to estimate the population with a pre-specified confidence, we provide a simple stopping rule for the sampling process.