Spatial Mode Demodulation of Multimode Interference Sensors by a “Fiber Camera”
Bibliographic record
Abstract
Interference due to transverse mode phase differences is the fundamental principle of multimode fiber sensors. Traditional demodulation methods based on spectral domain have limited sensitivity and accuracy due to spectral resolution constraints. To overcome this limitation, we propose a new sensing approach that maps the intermodal interference in spatial domain. We achieve this by sparsely sampling the fiber transverse mode field distribution (MFD) via a multi-core single-mode fiber (MCF), which acts as a cost-effective “fiber camera”. Additionally, spatial mode modulation was introduced to selectively excite different modes in the sensor. By switching the launching cores, different excited modes respond differently for a given environmental perturbation, resulting in a significant improvement in sensing resolution by up to 20 times. Multiplexing of sensor head offers a solution to cross-sensitivity by leveraging different core excitations. The discrimination of temperature and refractive index was demonstrated by use of a deep learning algorithm. Our spatial mode demodulation scheme presents exciting possibilities for low-cost, highly sensitive, and fast MMI sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".