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Record W2972476057 · doi:10.1088/2631-8695/ab42eb

Laser speckle reduction utilized by lens vibration for laser projection applications

2019· article· en· W2972476057 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering Research Express · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSpeckle patternLaserOpticsMaterials scienceLaser diodeLens (geology)Reduction (mathematics)Laser power scalingElectronic speckle pattern interferometryDistributed feedback laserSpeckle noiseLaser beam qualityLaser beamsPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, a compact speckle reduction method utilizing vibrating lenses for laser beam scanning is proposed and demonstrated. The maximum speckle reduction efficiency was found to be 75.6% and 81.25% for a 532 nm diode-pumped solid-state (DPSS) laser and a 520 nm laser diode (LD), respectively. The minimum speckle contrast ratio observed using our method was 0.11 for the DPSS laser and 0.06 for the LD. The proposed method can provide speckle reduction with minimal power requirements, a low implementation cost, and no bending for the optical path of the laser beam. Additionally, this method is promising to withstand high-power lasers for use in high lumen laser projectors by optimizing the lens parameters. The demonstrated technique has a small form factor while simultaneously demonstrating a high degree of speckle reduction, which shows potential for speckle reduction in mini- and pico- laser projector applications.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.687

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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