Labview programming for swept-source full-field optical coherence tomography
Bibliographic record
Abstract
Full-field optical coherence tomography (FFOCT) acquires image data in parallel. It has a big advantage in high-speed imaging because 2-dimensional mechanical raster scanning in the sample arm, which is essentially needed in a common fiber-based OCT system, does not exist anymore. Swept-source FFOCT (SSFFOCT) further makes the system free of depth scanning that significantly increases the operation speed. National Instrument's LabVIEW is a powerful tool to fast develop optical-electronic systems which have motion/vision units, signal processing functions and easy-to-generate Graphic User Interface (GUI). In this paper, we describe the design and implementation of Labview program prepared for an SSFFOCT system. Basically, there are four modules of Labview programming in such a system: 1. Wavelength sweeping control; 2. Synchronized image grabbing; 3. SSFFOCT signal processing; 4. 3-dimensional tomogram displaying mode selection. A general graphic user interface is used to input the parameters and monitor all necessary data and curves. The tomographic images can be displayed at any given cutting direction. More details and examples are provided and discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.024 |
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".