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Record W2047575621 · doi:10.1142/s0218127413500193

SPREAD SPECTRUM COMMUNICATION SYSTEM WITH SEQUENCE SYNCHRONIZATION UNIT USING CHAOTIC SYMBOLIC DYNAMICS MODULATION

2013· article· en· W2047575621 on OpenAlexaff
Georges Kaddoum, Ghyslain Gagnon, François Gagnon

Bibliographic record

VenueInternational Journal of Bifurcation and Chaos · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicChaos control and synchronization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsSynchronization (alternating current)ChaoticComputer scienceAsynchronous communicationFalse alarmSpread spectrumModulation (music)Communications systemSequence (biology)Symbolic dynamicsDirect-sequence spread spectrumSynchronization of chaosSecure communicationSIGNAL (programming language)Control theory (sociology)AlgorithmReal-time computingMathematicsTelecommunicationsChannel (broadcasting)Artificial intelligenceEncryptionComputer networkPhysics

Abstract

fetched live from OpenAlex

In this paper, we propose a new asynchronous multiuser communication system based on spread spectrum and chaotic symbolic dynamics modulation. By combining spread spectrum and chaotic modulation, the proposed system provides increased security by reducing the probability of detection while allowing multiuser transmissions. The sequence synchronization of chaos communication system is studied. A time acquisition technique based on serial search is proposed to achieve synchronization. An analysis is carried out to determine the probability of detection, the probability of false alarm and the bit error rate of the system. Simulation results show that the proposed system can achieve sequence synchronization under low signal-to-noise ratios, and also confirm our analytically computed expressions.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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
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

Citations19
Published2013
Admission routes1
Has abstractyes

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