Sensitivity-Enhanced Displacement Sensing System Based on a Microwave Photonic Filter Incorporating a Figure-Eight Loop Interferometer
Bibliographic record
Abstract
This work introduces a novel displacement sensing system with enhanced sensitivity and resolution by integrating a figure-eight loop interferometer (FELI) into a microwave photonic filter (MPF) architecture. Unlike conventional Mach-Zehnder interferometer (MZI)-based fiber sensors that rely on optical spectrum analyzers (OSAs) with limited resolution (∼0.02 nm), the proposed system leverages the distinct interferometric properties of the FELI and the high spectral resolution of MPF-based electrical demodulation. This innovation enables substantial performance improvement. The FELI-based MPF sensor achieves a displacement sensitivity of 0.207 MHz/μm, which is nearly twice that of the MZI-based MPF counterpart (0.113 MHz/μm). Analysis indicates a theoretical displacement resolution as fine as 4.83 pm, with practical accuracy reaching 0.3 μm, far surpassing the 7.168 mm resolution achievable via typical optical wavelength demodulation. This four-order-of-magnitude improvement highlights the FELI-MPF system as a breakthrough platform for ultra-high-resolution sensing, offering strong potential for compact, high-performance sensing in biometrics, on-chip diagnostics, and IoT applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".