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Record W1986556677 · doi:10.1109/jphot.2012.2198805

FBG-Based Matched Filters for Optical Processing of RF Signals

2012· article· en· W1986556677 on OpenAlexaff
Mansour Dastmalchi, Serge Doucet, Leslie A. Rusch, Sophie LaRochelle

Bibliographic record

VenueIEEE photonics journal · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsApodizationOptical filterOpticsFiber Bragg gratingPhotonicsMatched filterImpulse responseComputer scienceOptical fiberMaterials scienceOptoelectronicsElectronic engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

We demonstrate the detection of RF pulses using complex matched filters implemented in photonics. The optical receiver consists of an intensity Mach-Zehnder modulator (MZM) for electrical-to-optical conversion as well as precisely tailored fiber Bragg grating (FBG) filters performing as matched filters. We examine the performance of the receiver under two bias conditions of the MZM, namely, quadrature and minimum transmission. We design the corresponding matched filters for both cases by optimizing the impulse response of linearly chirped FBGs. We fabricate the FBGs by achieving the required apodization profiles through a multiple-scan writing procedure. We discuss the relative performance of the two solutions.

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 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)0.001
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: none
Teacher disagreement score0.547
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.294
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2012
Admission routes1
Has abstractyes

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