2D parallel optical coherence tomography and multiple-layer information extraction
Bibliographic record
Abstract
During the past decade, optical coherence tomography (OCT) has been vigorously developed into a powerful tool for biomedical diagnosis applications. Because this technology has the nature of extracting the internal features of an object, its applications can be extended to document security, biometrics identification, and industrial inspection. In addition, its high imaging resolution makes OCT an ideal tool for massive storage/retrieval of 3D data. In this paper, we propose the 2D parallel OCT system and its application for multiple-layer information retrieval. We will study the issues that exist exclusively in this type of application, such as interlayer phase/intensity modulation and the parasitic fringe patterns resulting from the surfaces of the information layer. The basic procedure of the proposed OCT system includes three steps: 1) extraction of cross-section raw images at each layer of an object; 2) removal of the interfering fringes by algorithm derived from multiple phase-shifted images; 3) elimination of interlayer modulations and parasitic patterns. Other issues that may degrade the retrieved images are also discussed. The simulation results and experimental tomography obtained from different testing samples are presented and discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".