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Record W1972740834 · doi:10.1049/ip-rsn:20000492

Modulation identification of digital signals by the wavelet transform

2000· article· en· W1972740834 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEE Proceedings - Radar Sonar and Navigation · 2000
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsFrequency-shift keyingWavelet transformWaveletDiscrete wavelet transformStationary wavelet transformModulation (music)Computer scienceIdentification (biology)Second-generation wavelet transformHarmonic wavelet transformWavelet packet decompositionSIGNAL (programming language)Speech recognitionPattern recognition (psychology)Electronic engineeringArtificial intelligenceMathematicsTelecommunicationsAcousticsChannel (broadcasting)EngineeringDemodulationPhysics

Abstract

fetched live from OpenAlex

There is a need, for example in electronic surveillance, to determine the modulation type of an incoming signal. The use of the wavelet transform for modulation identification of digital signals is described. The wavelet transform can effectively extract the transient characteristics in a digital communication signal, yielding distinct patterns for simple identification. Three identifiers for classifying PSK and FSK, M-ary PSK and M-ary FSK are considered. Statistics for hypothesis testing are derived. When the carrier-to-noise ratio is low, the symbol period and synchronisation time are needed to improve identification accuracy. A method for estimating them from the wavelet transform coefficients is included. The performance of the identifier is investigated through simulations.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.452

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 communication0.0000.002
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 head0.012
GPT teacher head0.227
Teacher spread0.216 · 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