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Record W2033860722 · doi:10.1117/12.872874

Spectral signal processing in swept source optical coherence tomography

2010· article· en· W2033860722 on OpenAlexaff
Shoude Chang, Youxin Mao, Costel Flueraru

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOptical coherence tomographyOpticsFourier domainComputer scienceSIGNAL (programming language)Signal processingCoherence (philosophical gambling strategy)Optical tomographyOptical filterWavelengthFourier transformFrequency domainMedical imagingPhysicsTelecommunicationsArtificial intelligenceComputer visionRadar

Abstract

fetched live from OpenAlex

OCT (optical coherence tomography) is generally regarded as the 6th imaging modality. This light-based system ideally suits for bio-medical diagnostic imaging applications. Outperforming to the time-domain OCT, Swept-Source OCT (SSOCT) is termed as the second generation OCT, in which, usually, thousand of individual wavelengths are sent into the system in a time sequence. The backscattered or back reflected light from the testing sample is collected by a sensor corresponding to each wavelength. The thousand received signals actually represent the Fourier coefficients in the spectrum domain. However, these coefficients only have real positive values as the sensor can only produce intensity signal. This paper describes the signal processing issues related with this intensity spectrum. Various filters, high-pass, low-pass and band-pass; spectrum decomposition; spectrum combination are discussed and demonstrated. A method for spectral synthesis of multiple light sources is 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 score0.390
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 communication0.0000.001
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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
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