Phase modulators for refractive corrections of human eyes
Bibliographic record
Abstract
This research project investigates the specifications of phase modulators for the treatment of presbyopia (accommodation loss). Correction of presbyopia is simulated using a pixilated phase modulator directly in front of a human eye model. The results show that maximal phase modulation depth of 17.5l (550nm) is required for a 2 Diopter change in a 6 mm pupil. The same phase retardation provides 7 Diopters of correction for a 3 mm pupil as opposed to 1.2 Diopters for 8 mm pupil. The impact of diffraction due to pixelation of the phase array on image quality is measured using encircled energy, where a well-focused point is defined as having 75% of its energy within a 15 mm radius. For a 2 Diopter correction in a 6 mm pupil, pixelation effects are important at low pixel density and decrease asymptotically with increasing density, stabilizing at about 51 x 51 pixels. In smaller pupils, the equivalent optical correction requires less pixel density to provide equal image quality. In conclusion, a phase modulator with a maximal phase change of 17.5l and 64 x 64 pixels could provide up to 2 Diopters of accommodation in a 6 mm pupil and significantly more in a smaller pupil, thus providing an excellent correction.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".