A Note on “A Systematic (12,8) Code for Correcting Single Errors and Detecting Adjacent Errors”

J.W. Schwartz and J.K. Wolf (ibid., vol. 39, no. 11, pp. 1403-1404, Nov. 1990) gave a parity check matrix for a systematic (12,8) binary code that corrects all single errors and detects eight of the nine double adjacent errors within any of the three 4-bit nibbles. We present a parity check matrix for a systematic (12,8) binary code that corrects all single errors and detects any pair of errors within a nibble

Interpolation and Approximation of Polynomials in Finite Fields over a Short Interval from Noisy Values

Motivated by a recently introduced HIMMO key distribution scheme, we consider a modification of the noisy polynomial interpolation problem of recovering an unknown polynomial $f(X) \in Z[X]$ from approximate values of the residues of $f(t)$ modulo a prime $p$ at polynomially many points $t$ taken from a short interval

On parity check collections for iterative erasure decoding that correct all correctable erasure patterns of a given size

Recently there has been interest in the construction of small parity check sets for iterative decoding of the Hamming code with the property that each uncorrectable (or stopping) set of size three is the support of a codeword and hence uncorrectable anyway. Here we reformulate and generalise the problem, and improve on this construction. First we show that a parity check collection that corrects all correctable erasure patterns of size m for the r-th order Hamming code (i.e, the Hamming code with codimension r) provides for all codes of codimension $r$ a corresponding ``generic'' parity check collection with this property. This leads naturally to a necessary and sufficient condition on such generic parity check collections. We use this condition to construct a generic parity check collection for codes of codimension r correcting all correctable erasure patterns of size at most m, for all r and m <= r, thus generalising the known construction for m=3. Then we discussoptimality of our construction and show that it can be improved for m>=3 and r large enough. Finally we discuss some directions for further research.Comment: 13 pages, no figures. Submitted to IEEE Transactions on Information Theory, July 28, 200

Entropy of a bit-shift channel

We consider a simple transformation (coding) of an iid source called a bit-shift channel. This simple transformation occurs naturally in magnetic or optical data storage. The resulting process is not Markov of any order. We discuss methods of computing the entropy of the transformed process, and study some of its properties.Comment: Published at http://dx.doi.org/10.1214/074921706000000293 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org