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Record W2099685905 · doi:10.1109/tit.2010.2040887

New Sets of Zero or Low Correlation Zone Sequences via Interleaving Techniques

2010· article· en· W2099685905 on OpenAlexaff
Honggang Hu, Guang Gong

Bibliographic record

VenueIEEE Transactions on Information Theory · 2010
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInterleavingHadamard transformSequence (biology)Zero (linguistics)MathematicsCode division multiple accessComplementary sequencesIdeal (ethics)AlgorithmConstruct (python library)Discrete mathematicsCombinatoricsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Sequence families with zero or low correlation zone can be used in the quasi-synchronous code-division multiple-access (QS-CDMA) communication systems. Interleaving techniques are very useful for sequence design. In this paper, we present a general construction of sequence families with zero or low correlation zone using interleaving techniques and complex Hadamard matrices. The component sequences are perfect or ideal two-level. In two cases, we construct the shift sequences: 1) P|L; 2) P is even, and L ¿ P/2 ( mod P), which results in sequence families with zero or low correlation zone of parameters (NP, MP, L, P¿), where N is the period of component sequences, M is the number of inequivalent shift sequences, and ¿ = 0 or 1. The conditions are derived under which the new construction is optimal. Some examples are also given to specify the new construction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations40
Published2010
Admission routes1
Has abstractyes

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