Closed-loop control of magnetic fluid deformable mirrors
Bibliographic record
Abstract
Adaptive optics (AO) systems make use of active optical elements, namely wavefront correctors (WFC), to improve the resolution of imaging systems by compensating for complex optical aberrations. Recently, magnetic fluid deformable mirrors (MFDM) were proposed as a promising new type of WFCs. These mirrors are developed by coating the free surface of a magnetic fluid with a thin reflective film of nano-particles. The reflective surface of the mirrors can be deformed using a locally applied magnetic field and thus serves as a WFC. MFDMs have been found particularly suitable for ophthalmic imaging systems where they can be used to compensate for the complex aberrations in the eye that blur the images of the internal parts of the eye. However, their practical implementation in clinical devices is hampered by the lack of effective methods to control the shape of their deformable surface. This paper presents a control algorithm that facilitated the first-ever use of a MFDM in a closed-loop AO system. The algorithm utilizes the influence function technique to decouple the multi-input multi-output system and features a proportional-integral controller structure. Experimental results showing the performance of the closed-loop system comprising the presented controller and a 19-channel prototype MFDM are presented.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".