Optical frequency domain reflectometry based on real-time Fourier transformation
Bibliographic record
Abstract
We propose and demonstrate an ultrahigh-speed optical frequency domain reflectometry (OFDR) system based on optical frequency-to-time conversion by pulse time stretching with a linearly chirped fiber Bragg grating (LCFG). This method will be referred to as OFDR based on real-time Fourier transformation (OFDR-RTFT). In this approach, the frequency domain interference pattern, from which the desired axial depth profile is reconstructed, can be captured directly in the time-domain over the duration of a single stretched pulse, which translates into unprecedented axial line acquisition rates (as high as the input pulse repetition rate). We provide here a comprehensive, rigorous mathematical analysis of this new OFDR approach. In particular, we derive the main design equations of an OFDR-RTFT system in terms of its key performance parameters. Our analysis reveals the detrimental influence of nonlinear phase variations in the input optical pulse (including higher-order dispersion terms and group delay ripples introduced by the LCFG stretcher) on the system performance, e.g. achievable resolution. A simple and powerful method based on Hilbert transformation is successfully demonstrated to compensate for these detrimental phase distortions. We show that besides its potential to provide ultrahigh acquisition speeds (in the MHz range), LCFG-based OFDR-RTFT also offers the potential for performance advantages in terms of axial resolution, depth range and sensitivity. All these features make this approach particularly attractive for imaging applications based on optical coherence tomography (OCT). In our experiments, single-reflection depth profiles with nearly transform-limited approximately 92.8 mum (average) axial resolutions over a remarkable 18 mm depth range have been obtained from OFDR-RTFT interferograms, each one measured over a time window of approximately 50 ns at 20 MHz repetition rate. Improved sensitivities up to -61 dB have been achieved without using any balanced detection scheme.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".