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Record W4406137888 · doi:10.1109/jlt.2025.3526755

Photonic Generation of High-Sampling-Rate Arbitrary Waveforms in a Temporal Synthetic Dimension Created by Two Polarimetric Subspaces

2025· article· en· W4406137888 on OpenAlexafffund
Yiran Guan, Guangying Wang, Jiejun Zhang

Bibliographic record

VenueJournal of Lightwave Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWaveformAmplitudeOpticsLinear subspaceSampling (signal processing)PhysicsMean squared errorPolarization (electrochemistry)MathematicsVoltageStatisticsDetector

Abstract

fetched live from OpenAlex

We propose and experimentally demonstrate a novel approach to the photonic generation of high-sampling-rate arbitrary microwave waveforms in a temporal synthetic dimension created by two polarimetric subspaces. The two polarimetric subspaces are established based on a polarization-maintaining fiber (PMF) loop incorporating a polarization modulator (PolM). Due to the different refractive indices along the two orthogonal axes of the PMF, one physical PMF loop is equivalent to two dynamically coupled loops with different time delays and a variable coupling ratio controlled by the PolM. If a single linearly polarized pulse is injected into the PMF loop, it is split into two pulses with orthogonal polarizations and with different amplitudes controlled by the PolM. After multiple round trips, a pulse burst with a tailored amplitude profile is produced in the temporal synthetic dimension. The temporal interval between two adjacent pulses in the pulse burst, or the sampling rate of the generated waveform, is determined by the birefringence and the length of the PMF. The proposed approach is evaluated by an experiment. Arbitrary waveforms with different sampling rates of 16.7, 62.5, and 250 GSa/s are generated. The fidelity of the generated waveforms is evaluated by calculating the average root mean square error (RMSE). For the generated waveforms, the RMSE is as low as 0.0596, conforming a good fidelity of the generated waveforms.

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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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
Published2025
Admission routes2
Has abstractyes

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