Adaptive micro endoscopy using liquid crystal lenses with segmented electrodes (Conference Presentation)
Bibliographic record
Abstract
Liquid crystalline (LC) materials are user friendly materials for electro optics applications [1], the most popular of which is the flat panel display [2]. Another interesting development, involving LCs, was the electrically variable lens that already had several commercial applications in DVD pick-up systems, Webcams and cell phones [3,4]. Their quality can be very high [5] and our team has recently demonstrated that they can be used in endoscopes for the study of deep regions of the brain [6]. We shall first describe various approaches explored to build electrically variable LC lenses. We shall then describe electrically variable LC lenses with segmented electrodes that enable almost adaptive optical capability, including the creation of a dynamic lens, prism, astigmatism and coma. This could be used to compensate various wavefront deformations in optical systems used to study various biological systems. We shall describe their advantages and drawbacks for the same application. References [1]. P.G. de Gennes and J. Prost, The Physics of Liquid Crystals, (Oxford University Press, 1995), 2nd Edition. [2]. Robert H. Chen, Liquid Crystal Displays: Fundamental Physics and Technology, Wiley, July 2011, ISBN: 978-0-470-93087-8. [3]. T. Galstian, Smart Mini-Cameras, CRC Press, Taylor & Francis group, Boca Raton, 2013. [4]. www.lensvector.com [5]. T. Galstian, K. Asatryan, V. Presniakov, A. Zohrabyan, A. Tork, A. Bagramyan, S. Careau, M. Thiboutot, M. Cotovanu, Optics Letters, Vol. 41, Issue 14, pp. 3265-3268 (2016), doi: 10.1364/OL.41.003265. [6]. A. Bagramyan, T. Galstian and A. Saghatelyan, Journal of Biophotonics, 1–13 (2016) / DOI 10.1002/jbio.201500261.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".