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Record W4281492790 · doi:10.32920/19852864.v1

Electromagnetic FPCB micromirror based scanning laser rangefinder

2022· preprint· en· W4281492790 on OpenAlexaff
Vixen Joshua Tan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLaserOpticsFlatness (cosmology)Collimated lightRadius of curvatureAperture (computer memory)Laser scanningMicroelectromechanical systemsCurvatureLaser power scalingMaterials sciencePhysicsOptoelectronicsAcousticsMathematics

Abstract

fetched live from OpenAlex

<p>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.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.015
GPT teacher head0.264
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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