Speckle reduction by employing two green lasers and two-dimensional vibration of lenses
Bibliographic record
Abstract
Abstract A method of speckle reduction suitable for use in a laser projector was proposed in the paper. Speckle contrast ratio (SCR) reduction was achieved by combining wavelength diversity and angular diversity methods. First, wavelength diversity was demonstrated by the use of two green laser sources (a 520 nm laser diode (LD) and a 532 nm diode-pumped solid-state (DPSS) laser) at a power ratio of 4:1. Second, angular diversity was achieved via the vibration of two lenses in two orthogonal directions placed directly after the laser source. The vibrating lenses are small and do not require changes to the beam path of the laser source, allowing for more compact projector designs. The frequency of vibration of these lenses was optimized to minimize the SCR in the output image. A SCR of less than 4% was achieved without the use of optical diffusers, which significantly reduces optical losses. Optical transmission could be further increased with the optimization of optical coatings on the lenses. This result shows great promise for applications such as laser pico-projectors within the realm of heads-up displays (HUDs) and mobile devices.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".