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Record W4323538475 · doi:10.1117/12.2651328

Double exposure ESPI for non-contact surface topography tracking

2023· article· en· W4323538475 on OpenAlexaff
Hui Wang, Parsa Omidi, Mamadou Diop, Jeffrey J. L. Carson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsLawson Health Research Institute
Fundersnot available
KeywordsSpeckle patternOpacityElectronic speckle pattern interferometryOpticsTracking (education)Imaging phantomPhase (matter)AcousticsMaterials scienceInterferometryPhysics

Abstract

fetched live from OpenAlex

In this paper, we introduce an electronic speckle pattern interferometry (ESPI) method for rapid assessment of transient deformations on an opaque object. The method records the change in speckle patterns over time, which relate to the change in phase of the reflected light. The system was capable of high-speed recordings enabled by a camera capable of double exposure and external triggering. Experiments were performed on an opaque PDMS phantom to track rapid surface movements from a piezoelectric acoustic driver located at the back of phantom. Acoustic pulses of different period and amplitude were tested. In each double exposure recording cycle, the image pair were digitally subtracted to reveal the change in the speckle pattern, which represented the change in phase of the object beam relative to the reference. We developed custom software to process the data, including an algorithm to unwrap the phase maps. Experiments revealed an ultimate sensitivity to displacements of approximately 1 nm for signals ranging in period from 50 μs to 200 μs. Future work will examine the capabilities of the system with respect to surfaces with different optical absorption and scattering characteristics.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.306
Teacher spread0.237 · 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 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
Published2023
Admission routes1
Has abstractyes

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