MétaCan
Menu
Back to cohort
Record W2066149656 · doi:10.1117/12.839588

New optical instruments for coherence tomography

2009· article· en· W2066149656 on OpenAlexaffabout
Youxin Mao, Shoude Chang, Costel Flueraru

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOptical coherence tomographyInterferometryOpticsMaterials scienceLens (geology)WavelengthOptical fiberGradient-index opticsNarrowbandOptical tomographySingle-mode optical fiberRefractive indexPhysics

Abstract

fetched live from OpenAlex

We present the following novel technologies for optical coherence tomography developed in Institute for Microstructural Sciences, National Research Council Canada. We first demonstrate a high power mode-lock wavelength swept laser using a polygon-based narrowband optical scanning filter first. Peak and average output powers of 98 mW and 71 mW have been achieved, respectively, without an external amplifier, while the wavelength was swept continuously from 1247 nm to 1360 nm. Then, we present a design, construction and characterization of ultra-small OCT probes using fiber lenses. Those fiber lens modules are made of a single mode fiber and a GRIN or ball fiber lens with or without a fiber spacer between them. The lens diameters are smaller than 0.3 mm. We discuss theoretical models, design methods, fabrication techniques, and measured performance compared with modeling results. Finally, we demonstrate an instantaneous complex conjugate resolved swept-source OCT using a 3x3 Mach-Zehnder interferometer. The interferometer provides simultaneous access to complementary phase components of the complex interferometric signals; therefore, the effective imaging depth was doubled. The complex conjugate artifact suppression of 27 dB was obtained. OCT Images of human nail and animal tissue are 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
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
Published2009
Admission routes2
Has abstractyes

Explore more

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