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Record W2163076405 · doi:10.1109/ccece.2006.277333

A Hybrid Approach to Modulation Recognition for Intentional Modulation on Pulse (IMOP) Applications

2006· article· en· W2163076405 on OpenAlexaff
Yifeng Zhou, Stephen Sung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsWaveformPhase-shift keyingComputer scienceEnvelope (radar)Modulation (music)Noise (video)Frequency modulationPhase modulationSpeech recognitionElectronic engineeringArtificial intelligencePattern recognition (psychology)Phase noiseAlgorithmTelecommunicationsPhysicsAcousticsEngineeringBit error rateBandwidth (computing)Decoding methodsRadar

Abstract

fetched live from OpenAlex

In this paper, a hybrid approach to modulation recognition is proposed for classifying unmodulated CW, narrow-band FM, wide-band FM, triangular FM, BPSK, DSB-SC and AM as well as noise. The algorithm is based on the use of decision theoretic and pattern recognition techniques. The decision theoretic approach is used to separate noise from signals, constant-envelope from varying-envelope waveforms, unmodulated CW from waveforms with phase information, and the two varying-envelope waveforms from one another. The pattern recognition technique is used to distinguish the three FM and BPSK waveforms. Computer simulations are used to demonstrate the performance of the proposed algorithm

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)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.258
Teacher spread0.214 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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