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Record W2128716617 · doi:10.1109/milcom.1995.483654

Modulation identification by the wavelet transform

2002· article· en· W2128716617 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsFrequency-shift keyingComputer scienceModulation (music)DemodulationIdentification (biology)Wavelet transformDiscrete wavelet transformWaveletSIGNAL (programming language)Stationary wavelet transformWavelet packet decompositionPattern recognition (psychology)Electronic engineeringSpeech recognitionArtificial intelligenceTelecommunicationsEngineeringAcousticsChannel (broadcasting)

Abstract

fetched live from OpenAlex

There is a need, for example in surveillance of the radio spectrum, to determine the modulation type of a received signal. By knowing the modulation type, one can demodulate the incoming signal for useful information. This paper proposes the use of wavelet transform for the identification problem. Application of wavelet transform on a digital modulation signal results in distinctive patterns for different types, which enables simple processing for identification. Three identifiers for classifying PSK and FSK, M-ary PSK, and M-ary FSK are considered and the relevant statistics for optimum decision making are included. Simulations are included for performance evaluation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.451

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 communication0.0000.001
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 head0.028
GPT teacher head0.225
Teacher spread0.197 · 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

Quick stats

Citations79
Published2002
Admission routes1
Has abstractyes

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