Online tuning for retinal imaging adaptive optics systems
Bibliographic record
Abstract
The imaging of the retina tissue in the back of the eye is important for a number of applications, such the early detection of eye disease. Adaptive optics systems are being increasingly used to provide clear images of the retina tissue by compensating for the eye's optical imperfections, also referred to as aberrations. Although current retinal imaging adaptive optics systems can compensate for some aberrations in the human eye, they are still unable to compensate for the unknown and time-varying higher order aberrations. This paper presents a control system design approach that allows the adaptive optics system to compensate for the unknown time-varying aberrations. The compensation for the aberrations is achieved by adjusting the shape of a deformable mirror in the adaptive optics system. Hence, the control problem addressed in this paper is that of tracking an unknown and time-varying desired shape for the membrane mirror. The proposed controller design method relies on tuning, online, a Q-parameterized stabilizing controller in such a way that the tuned controller converges to the desired controller needed to achieve regulation. The resulting system will allow for improved retinal images to be taken, resulting in the early detection of eye diseases
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".