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Record W2032186844 · doi:10.1117/12.778741

Large area full-field optical coherence tomography using white light source

2007· article· en· W2032186844 on OpenAlexaff
Shoude Chang, Youxin Mao, Sherif Sherif, Costel Flueraru

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOptical coherence tomographySuperluminescent diodeOpticsHalogen lampNear and far fieldBandwidth (computing)Coherence (philosophical gambling strategy)Light sourcePenetration depthDiodeComputer scienceMaterials scienceOptoelectronicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Optical coherence tomography (OCT) is an emerging technology for high-resolution cross-sectional imaging of 3D structures. Not only could OCT extract the internal features of an object, but it could acquire the 3D profile of an object as well. Hence it has huge potentials for industrial applications. Owing to non-scanning along the X-Y axis, full-field OCT could be the simplest and most economic imaging system, especially for applications where the speed is critical. For an OCT system, the performance and cost basically depends on the light source being used. The broader the source bandwidth, the finer of the depth resolution that could be reached; the more power of the source, the better signal-to-noise ratio and the deeper of penetration the system achieves. A typical SLD (Superluminescent Diode) light source has a bandwidth of 15 nm and 10 mW optical power at a price around $6,000. However, a Halogen bulb having 50W power and 200nm bandwidth only costs less than $10. The design and implementation of a large-area, full-field OCT system using Halogen white-light source is described in the paper. The experimental results obtained from 3D shaping and multiple-layer tomographies are also presented.

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: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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

Citations0
Published2007
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