Development of a Vector Display System Based on a Surface-Micromachined Micromirror
Bibliographic record
Abstract
This paper presents a small-size low-power portable vector display system. The display system uses a micromirror which consists of a simple two-thin-film structure and is fabricated using a surface-micromachining process, i.e., Polysilicon Multi User MEMS Processes. The micromirror exhibits rotational motion around two axes as well as translational motion through four repulsive-force electrostatic actuating units. The micromirror is free of the “pull-in” effect which is associated with conventional micro electrostatic actuators. The micromirror is operated in a nonresonant mode and is capable of static positioning. Measured data show that the micromirror can achieve an optical scanning angle of ±2.5° in each axis. The measured bandwidth (-3 dB) for each driving unit is 2000 Hz operating at a sinusoidal input voltage of 0-200 V. The settling times of the actuating unit are 2.75 and 3.32 ms with overshoots of 5% and 48% for step inputs of 200 to 0 V and 0 to 200 V, respectively. The portable vector display system is developed through integrating the micromirror, a laser source, the optics, and a microcontroller and is shown to be very effective in displaying complex geometries. Open-loop control methods using lookup table searching, spot dwelling, and scan speed control were developed to control the integrated portable device.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".