High-Speed and High-Resolution Microwave Photonic Interrogation of a Fiber-Optic Refractometer With Plasmonic Spectral Comb
Bibliographic record
Abstract
Microwave photonic interrogation of a high-speed and high-resolution refractive index (RI) sensor based on a tilted fiber Bragg grating with surface plasmon resonance (TFBG-SPR) is proposed and experimentally demonstrated. Instead of demodulating the wavelength shift or intensity change of a TFBG-SPR spectrum in the optical domain, we convert the TFBG-SPR spectrum to the time domain based on spectral shaping (SS) and wavelength-to-time (WTT) mapping and use a digital signal processor (DSP) to extract the RI information at a high speed and high resolution. In the experiment, when an Au-coated TFBG is immersed in a solution, the TFBG-SPR spectrum will produce a dip. When the RI changes, the location of the dip in the TFBG-SPR spectrum will shift, which is a function of the RI. By passing a broadband frequency-chirped optical pulse generated by a frequency swept laser source to the TFBG-SPR and detecting the optical pulse at the output of the TFBG-SPR at a photodetector (PD), due to SS-WTT mapping, a temporal waveform with its shape identical to the optical spectrum is produced. A DSP is then used to extract the SPR envelope information from the temporal waveform. By monitoring the changes of the SPR envelope, the RI information is interrogated with a high resolution of 1.123 × 10-6RIU at a high speed over 20 kHz.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".