John S. Richters

dblp:294/4448 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1965
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 91% Information theory · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes › convolutional codes
burst-correcting convolutional codes
0.011965
Application of Pareto error statistics to Hagelbarger codes · IEEE Trans. Inf. Theory 1965
Coding theory › error-correcting codes
burst error correction
0.011965
Application of Pareto error statistics to Hagelbarger codes · IEEE Trans. Inf. Theory 1965
Coding theory
error-correcting codes
0.011965
Application of Pareto error statistics to Hagelbarger codes · IEEE Trans. Inf. Theory 1965
Information theory › communication channels
channel models
0.011965
Application of Pareto error statistics to Hagelbarger codes · IEEE Trans. Inf. Theory 1965

Methods — techniques the papers use, named apart from their topics

pareto distribution · 0.0asymptotic analysis · 0.0
YearPublicationVenuePosition
1965 Application of Pareto error statistics to Hagelbarger codes
abstract
One statistical model that has been proposed for the generation of errors in telephone circuits consists of errors with successive inter-arrival times drawn independently from a Pareto distribution, resulting in errors that tend to occur in bursts. These error statistics are applied to a class of burst correcting codes due to Hagelbarger, with particular attention to codes capable of correcting very long error bursts. For such codes, asymptotic expressions are derived for the expected number of output errors per data digit error and also the expected number of false corrections per data digit error. These expressions are what one might intuitively expect, indicating that the results obtained here can perhaps be extended to other codes by simple intuitive reasoning.
John S. Richters
IEEE Trans. Inf. Theory1