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Record W2040764310 · doi:10.1364/optica.1.000064

Time-stretched sampling of a fast microwave waveform based on the repetitive use of a linearly chirped fiber Bragg grating in a dispersive loop

2014· article· en· W2040764310 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptica · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFiber Bragg gratingMaterials scienceOpticsMicrowaveWaveformBandwidth (computing)Optical amplifierSIGNAL (programming language)Optical fiberPhysicsLaserRadarTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Conventional sampling techniques may not be able to meet the ever-increasing demand for increased bandwidth from modern communications and radar signals. Optical time-stretched sampling has been considered an effective solution for wideband microwave signal processing. Here, we demonstrate a significantly increased stretching factor in the photonic time-stretched sampling of a fast microwave waveform. The microwave waveform to be sampled is intensity modulated on a chirped optical pulse generated jointly by a mode-locked laser and a length of dispersion compensating fiber. The pulse is then injected into an optical dispersive loop that includes an erbium-doped fiber amplifier and is stretched by a linearly chirped fiber Bragg grating multiple times in the loop. A record stretching factor of 36 is achieved based on an equivalent group delay dispersion coefficient of 12,000  ps/nm. This result could help address the new challenges imposed on signal processors to operate at a very high sampling rate.

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: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.443

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 communication0.0000.000
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 head0.020
GPT teacher head0.228
Teacher spread0.208 · 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