A position sensing method for 2D scanning mirrors
Bibliographic record
Abstract
Abstract This paper presents a cost-effective position sensing method for 2D scanning mirrors. The method uses only one 1D PSD (position sensitive detector) located at the backside of the 2D scanning mirror plate to retrieve the 2D rotation angle about the two axes separately in real time. Any 2D scanning mirror with resonant vibration about one axis and quasi-static vibration such as sinusoidal, saw tooth, triangular oscillation about the other axis can use this method. The two vibration axes are orthogonal to each other to form the scanning patterns, which are most desired in scanning 3D LiDAR systems. 3D scanning LiDAR is the targeted application for this research. The method uses timing measurement to measure the resonant vibration angle and Lagrange interpolation polynomial approximation to retrieve the quasi-static vibration angle. A prototype has been built to measure the 2D rotation angle of a 2D micromirror. The measured angle using the proposed method was verified using a 2D PSD. The largest errors for the vertical/horizontal angles were 9.6% and 5.36% respectively. The position sensing mechanism is also integrated to a scanning 2D micromirror based LiDAR system to demonstrate it as real time capability.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".