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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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".