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Record W1989124831 · doi:10.1117/12.873105

Design of a spectrometer for all-reflective optics-based line scan Fourier domain optical coherence tomography

2010· article· en· W1989124831 on OpenAlexaff
Mohammad Mostafa Kamal, N.R. Sivakumar, Muthukumaran Packirisamy

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsOpticsSpectrometerOptical coherence tomographyZemaxImaging spectrometerChromatic aberrationPhysicsDetectorChromatic scaleComputer scienceSoftware

Abstract

fetched live from OpenAlex

Optical design of a spectrometer for all-reflective optics based line scan Fourier domain optical coherence tomography (FD-OCT) imaging has been reported in this work for high-speed scanning. FD-OCT imaging data acquisition offers significantly improved imaging speed in the depth direction compared to conventional time domain optical coherence tomography (TD-OCT). On the other hand, line focused scanning improve imaging speed in the transverse direction compare to commonly used flying spot scanning. Combination of FD-OCT acquisition and line focused scanner can give higher imaging speed. Spectrometer is a critical submodule in FD-OCT system. Apart from the spectrometer optical resolution, and detector array resolution, the chromatic aberration should be considered to design a high-quality FD-OCT imaging spectrometer. The proposed imaging spectrometer consists of a planer reflective grating, off-axis parabolic cylindrical mirror and a CCD array detector. Mirror focusing reduces the chromatic aberration because of its insensitivity to the wavelength of the laser beam, therefore the spectrometer image quality enhanced by the reflective optics focusing. Spot profile fall-off characteristic was analyzed by using ZEMAX optical design software.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.583
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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 designBench or experimental
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

Citations8
Published2010
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207