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Record W2920694809 · doi:10.1117/12.2506629

Holographic camera for non-contact measurement of nanoscale surface heights

2019· article· en· W2920694809 on OpenAlexaff
Hui Wang, Parsa Omidi, Jeffrey J. L. Carson, Mamadou Diop

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDigital Holography and Microscopy
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsOpticsHolographyImage resolutionInterferometryMaterials scienceHolographic interferometryInterference (communication)Resolution (logic)PhysicsComputer visionArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

We report on the development of a holographic camera capable of measuring nanometer-scale surface features. The system is based on a modified off-axis Mach-Zehnder interferometer and was optimized to provide high-quality interference patterns. Fast imaging was implemented with a time-gated intensified CCD camera directly facing the surface of the object. By increasing the intensifier gain, holographic images with good contrast could be captured within 1 ns. We tested the ability of the camera to measure nanometer-scale height differences using a patterned USAF target. The depth resolution of the camera was estimated to be better than 10 nm. We also found that both the object-CCD distance and the angle between the object and reference beams had significant impact on the quality of the reconstructed surface profiles. Potential applications of the camera include measurement of tissue surface displacements for non-contact photoacoustic imaging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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