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Record W2132519869 · doi:10.1109/ccece.1993.332413

On the spectral characteristics of continuous guided scrambling line codes

2002· article· en· W2132519869 on OpenAlexaff
I.J. Fair, V.K. Bhargava, Q. Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScramblingCode (set theory)Symbol (formal)Computer scienceAlgorithmLimit (mathematics)Sequence (biology)MathematicsDiscrete mathematics

Abstract

fetched live from OpenAlex

Line codes are used in digital communication systems to control the characteristics of transmitted symbol sequences. Recently, an efficient and easily implemented family of codes called guided scrambling (GS) codes has been introduced. Previous work indicated that continuous GS (CGS) codes can be configured to generate encoded sequences with statistics nearly independent from those of the source bit stream, a property not available with any other efficient line code technique developed to date. This paper presents analysis of the spectral characteristics of these codes which confirms this conjecture. The principle of guided scrambling is reviewed. Then, by showing that the scrambling polynomials currently recommended for GS codes can be regarded as base polynomials for large families of polynomials, expanded sets of scrambling polynomials are proposed. Evaluation of the power spectral density of CGS codes which use scrambling polynomials from these expanded sets is then considered, under the condition only that the input bit stream is stationary. Expressions which limit required computation to practical limits are developed, and results for several code configurations are given. Here it is demonstrated that when scrambling polynomials of high degree are used, the characteristics of the transmitted symbol sequence become nearly independent of the source bit stream statistics.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.241
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2002
Admission routes1
Has abstractyes

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