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Record W2019519683 · doi:10.1115/ihtc14-22621

Optical Tomography With a Least Square Finite Element Formulation of the Collimated Irradiation in Frequency Domain

2010· article· en· W2019519683 on OpenAlexaff
O. Balima, Joan Boulanger, André B. Charette, Daniel Marceau

Bibliographic record

Venue2010 14th International Heat Transfer Conference, Volume 5 · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsNational Research Council CanadaUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsCollimated lightOpticsFinite element methodScatteringOptical tomographyFrequency domainPhysicsSquare (algebra)TomographyMathematicsMathematical analysisGeometry

Abstract

fetched live from OpenAlex

This paper presents a numerical study of optical tomography in frequency domain for the reconstruction of optical properties of scattering and absorbing media with collimated irradiation light sources. The forward model is a least square finite element formulation of the collimated irradiation problem where the intensity is separated into its collimated and scattered parts. This model does not use any empirical stabilization and moreover the collimated source direction is taken into account. The inversion uses a gradient type minimization method where the gradient is computed through an adjoint formulation. Scaling is used to avoid numerical round errors, as the output readings at detectors are very low. Numerical reconstructions of optical properties of absorbing and scattering media with simulated data (noised and noise-free) are achieved in a complex geometry with satisfactory results. The results show that complex geometries are well handled with the proposed method.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.260
Teacher spread0.249 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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