Control System Design for Retinal Imaging Adaptive Optics Systems
Bibliographic record
Abstract
This paper presents a solution to control system design issues for adaptive optics systems used in retinal imaging. In this paper, the control problem for adaptive optics systems is generalized to that of shape control for a flexible membrane representing a deformable membrane mirror. Due to the dynamic nature of the aberrations in the eye, the shape control problem addressed is the tracking of an unknown and time-varying shape for a distributed membrane (i.e., desired shape of the mirror). 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. To accomplish this task, a multivariable centralized controller is utilized. Since the desired shape of the mirror is unknown and time-varying, the controller is tuned online to converge to the controller needed to achieve regulation. This is done iteratively, by taking advantage of the Q-parameterization of all stabilizing controllers, so that the controller will converge to the ideal controller. The online tuning is used to compensate for the lack of information of the desired shape for the deformable mirror.
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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".