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Record W2929958646 · doi:10.3390/s19071606

Fast Volumetric Feedback under Microscope by Temporally Coded Exposure Camera

2019· article· en· W2929958646 on OpenAlexfundno aff
Kazuki Yamato, Toshihiko Yamashita, Hiroyuki Chiba, Hiromasa Oku

Bibliographic record

VenueSensors · 2019
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsnot available
FundersInstitute of GeneticsJapan Science and Technology Agency
KeywordsLens (geology)Focus (optics)Computer scienceArtificial intelligenceOpticsMicroscopeComputer visionTracking (education)BrightnessFocal lengthCamera lensHigh-speed cameraPhysics

Abstract

fetched live from OpenAlex

We developed a temporally coded exposure (TeCE) camera that can cope with high-speed focus variations of a tunable acoustic gradient index (TAG) lens. The TeCE camera can execute a very short exposure multiple times at an arbitrary timing during one shot. Furthermore, by accumulating the photoelectrons generated by each exposure, it is possible to maintain the brightness even with a short exposure time. By synchronously driving the TeCE camera and the TAG lens, different focal planes of an observation target can be acquired at high speed. As a result, high-speed three-dimensional measurement becomes possible, and this can be used for feedback of three-dimensional information. In the work described in this paper, we conducted a focus tracking experiment to evaluate the feedback performance of the TeCE camera. From the experimental results, we confirmed the feedback capability of the TeCE camera.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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