Control system design issues for retinal imaging adaptive optics systems
Bibliographic record
Abstract
This paper presents a review of as well as solutions to control system design issues for adaptive optics systems used in retinal imaging. The development of retinal imaging systems allows for early diagnosis of eye diseases. Such systems can increase the quality of life of patients as well as curtail increasing health care costs through early eye disease detection and treatment. A discussion on control methods that have been, or can be used, in vision based adaptive optics systems is presented. In this paper, the control problem for adaptive optics systems is generalized to that of shape control for a flexible membrane representing a deformable mirror. Due to the unknown dynamic nature of the aberrations in the eye, the control problem addressed is the tracking of an unknown and time-varying shape for a distributed membrane. The design of a controller to achieve the shape control objective is based on a model of a distributed parameter system representing the mirror membrane. Controllers are discussed with respect to two types of membrane models, namely infinite dimensional models, and finite dimensional models. Control system design problems that must be overcome in order to make adaptive optics based retinal imaging systems a viable technology are presented. A solution to these problems, in the form of adaptive shape control algorithms for a flexible membrane, is proposed
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".