Bibliographic record
Abstract
This thesis presents an electromagnetic FPCB (Flexible Printed Circuit Board) micromirror based scanning triangulation laser rangefinder (LRF). Two configuration designs of the electromagnetic FPCB micromirror have been developed and tested. The FPCB micromirror has a large aperture (8 mm x 5.5 mm) and high flatness (ROC, radius of curvature, ~ 15m), that overcomes conventional MEMS micromirrors’ limitation of small aperture (less than 5 mm). Subsequently high power lasers with large beam sizes and good collimation can be used in micromirror based scanning LRF for better performance. As a result, the LRF in this thesis achieved a larger scanning angle and longer detecting distance than those in literature. Both modelling and prototyping are presented. Three lasers (Laser 1: 2 mW; Laser 2: 20 mW; and Laser 3: 100 mW) are used to characterise the LRF. Eye-safety calculation is presented for the three lasers. Achieved performance (measurement distance and FOV, field of view) is: with Laser 1, distance of 15 – 70 cm and FOV of -15° to 10°, error ≤ 4% ; with Laser 2, distance of 15 – 130 cm and FOV of 15° to 15°, error ≤ 5%; with Laser 3, distance 15 – 200 cm and FOV of -15° to (5~9°), error ≤ 5%. Fatigue test indicates 1.2 billion scanning cycles have been reached.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".