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Record W2013151081 · doi:10.1117/1.jbo.17.5.056006

Optimized off-axis cylindrical mirror-focused line-scanning system for optical coherence tomography imaging applications

2012· article· en· W2013151081 on OpenAlexaff
Mohammad Mostafa Kamal, Sivakumar Narayanswamy, Muthukumaran Packirisamy

Bibliographic record

VenueJournal of Biomedical Optics · 2012
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsOpticsOptical coherence tomographyOptical axisMaterials scienceField of viewFlatness (cosmology)Physics

Abstract

fetched live from OpenAlex

The parameters of an off-axis cylindrical mirror-focused line-scanning system were studied to optimize the flatness of the 2 mm scan field. The scanning system parameters included the beam size, the distance between the scanning and the focusing mirror, the angle between the incident beam and the reflected beam, the optical scan angle, and the effective focal length of the cylindrical mirror. Because of the off-axis line-scanning system configuration, the scanning could be carried out either in the tangential (Y-scan) or in the sagittal (X-scan) plane. A 53 nm spectral bandwidth light source was used to evaluate the imaging performance of the scanning system. Since reflective optics is employed in this work for focusing, the scanning system could be used with a higher spectral bandwidth light source for optical coherence tomography applications. The effect of the angle between of the incident and reflected beams, the distance between the mirrors, the focal length of the cylindrical mirror and the scanning directions, on the flatness of the scan field were studied. It was proved that the sagittal scanning is least sensitive to variations in scanning system parameters and thus provides maximum flexibility in design.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations4
Published2012
Admission routes1
Has abstractyes

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