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Record W7083190957 · doi:10.1587/transfun.2025eap1066

Further Designs of Low-Hit-Zone Frequency-Hopping Sequence Set with Strictly Optimal Partial Hamming Correlation

2025· article· en· W7083190957 on OpenAlexaff

Bibliographic record

VenueIEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersYibin University
KeywordsHamming distanceSequence (biology)Hamming codeHamming weightSet (abstract data type)AutocorrelationHamming graphComplementary sequences

Abstract

fetched live from OpenAlex

The quasi-synchronous frequency-hopping (FH) multiple access (QS-FHMA) communication system has the advantages of not requiring precise time synchronization, low equipment complexity, and easy to implement in engineering applications. And it is widely applied in military communication systems, vehicle-to-everything (V2X) communication, satellite communication systems, and industrial internet of things (IIoT), among others. FH sequences set with Low-Hit-Zone (LHZ), in LHZ both the Hamming autocorrelation of each sequence and the Hamming cross-correlation between distinct sequences remain low, are an important component of QS-FHMA communication system. The utilization of LHZ FH sequence set (LHZ FHS set) with optimal partial Hamming correlation (PHC) can effectively enhance the communication performance of QS-FHMA system. Based on q-ary m-sequence with degree n and its decimated sequence, q ≠ 2, n ≥ 2, this paper constructs three types of LHZ FHS sets. Experimental results show that these sequence sets have strictly optimal periodic PHC property, good wide gap property, and favorable complexity. The new constructions can provide more high-performance LHZ FHS sets for QS-FHMA communication systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score0.406

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 science0.0000.000
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 head0.035
GPT teacher head0.301
Teacher spread0.266 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIEICE Transactions on Fundamentals of Electronics Communications and Computer SciencesSame topicGene expression and cancer classificationFrench-language works237,207