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Record W2382520845

A Study of The Maximum Resolution of 2D Mirror Galvanometer Pointwise Scan Laser Display

2011· article· en· W2382520845 on OpenAlexvenueno aff

Bibliographic record

VenueMicrocomputer applications · 2011
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsGalvanometerRaster scanOpticsPointwiseHorizontal scan rateScan lineRaster graphicsLaserDeflection (physics)Frame rateResolution (logic)PhysicsComputer scienceMathematicsComputer visionArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

To obtain the maximum resolution of 2D mirror galvanometer pointwise scan laser display,the laser beam should scan the most points on the screen within the time T which is equal to 1/f,where f is the frame rate.Suppose that the minimum time spent to scan one point is t,so the maximum resolution is T/t.In contrast to traditional raster scan based on electrostatic or magnetic deflection,the mirror galvanometer is based on the mechanical deflection.The small angle step response time specification of the mirror galvanometer determines that the larger the deflection angle step,the longer the response time spent to reach the target position.The horizontal and vertical blanks in raster scanning all would result in the large angle step response,and in that the time spent to scan lines is almost equal to the blanks.So,raster scanning based on a mirror galvanometer only obtains nearly half the maximum resolution.This paper proposes one novel scan method called pointwise consistent scan which guarantees that the time spent to scan any point is the minimum time t,and which completely eliminates the blanks.Using this method can achieve the maximum scan display resolution.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.315

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 science0.0000.000
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 head0.031
GPT teacher head0.221
Teacher spread0.190 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2011
Admission routes1
Has abstractyes

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