Bibliographic record
Abstract
Abstract This paper presents a 2D flexible printed circuit board (FPCB) micromirror and a scanning 3D light detection and ranging (LIDAR) based on it by integrating the 2D FPCB micromirror with a commercially available single point LIDAR. The 2D FPCB micromirror retains the benefits of previously developed 1D FPCB micromirrors, i.e. large aperture and low cost while providing rotation of the mirror plate about two orthogonal axes to be able to scan a laser beam about both vertical and horizontal axes to achieve 2D scanning. One 2D FPCB micromirror is integrated with a single point LIDAR to achieve a 3D scanning LIDAR, which, in comparison to the previously developed 1D FPCB micromirror based 3D LIDAR, achieved more compact structure and easier fabrication/assembly due to no strict requirement on the alignment between two micromirrors while only one 2D micromirror rather than two 1D micromirrors used. Prototypes of the 2D FPCB micromirror and the 3D LIDAR based on it are fabricated and tested. The test results demonstrate that the 2D FPCB micromirror based 3D LIDAR achieved a volume reduction over the previous 1D FPCB micromirror based 3D LIDAR from 1042 cm3 to 754 cm3 with a field of view of 40°× 24° at 150 Hz horizontal scanning and 2 Hz vertical scanning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".