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Record W2111079856 · doi:10.1109/jlt.2008.917774

Balanced Detection of Correlated Incoherent Signals: A Statistical Analysis of Intensity Noise With Experimental Validation

2008· article· en· W2111079856 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

VenueJournal of Lightwave Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsProbability density functionSpectral densityDetectorNoise (video)Correlation function (quantum field theory)MathematicsOpticsStatisticsPhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We study the balanced detection of broadband incoherent optical signals-signals characterized by high-intensity noise. We consider signals generated from a single incoherent source with two types of correlation: identical spectra, but delayed in time, and overlapping, nonidentical spectra but zero time delay. Our statistical analysis yields equations for the probability density function (pdf) of the balanced detector output for partially correlated input signals based on easily measured system parameters (power spectral densities in one case, relative time delay in the other). Using analytical tools we derive expressions for output pdfs giving extremely good prediction of measured pdfs for signals with correlation coefficient up to 95%. The analytic expressions can be used to characterize system performance, in particular, bit error rate for communications 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.

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: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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