MétaCan
Menu
Back to cohort
Record W4401482294 · doi:10.1117/1.jom.4.3.034502

Ultrafast ultrasound imaging by optical polymer microring resonator array and a dual optical frequency comb: a theoretical concept

2024· article· en· W4401482294 on OpenAlex
Ahmed S. Bahgat, Jean Provost, Denis V. Seletskiy, Yves-Alain Peter

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Optical Microsystems · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsUltrashort pulseResonatorDual (grammatical number)OpticsMaterials scienceOptical imagingOptoelectronicsOptical frequency combUltrasoundPhysicsLaserAcoustics

Abstract

fetched live from OpenAlex

Ultrasound imaging is typically based on the use of arrays of piezoelectric transducers that can both emit and receive ultrasound. It has recently been shown that on-chip optical microresonator transducers can achieve massive improvements in minimizing footprint and increasing both ultrasound sensitivity and bandwidth; however, the construction of practical arrays remains an open problem. We study the feasibility of making an array of optical microresonators for ultrafast imaging. As a proof of concept, we propose the design of a linear array of polymer microring resonators with equally spaced resonance frequencies. Optical dual-comb setup simultaneously interrogates the whole array’s ultrasound perturbation by assigning each microring to a single comb tooth. Using an optical frequency comb for detection provides an efficient way of sampling a large array of transducers while using only a single balanced heterodyne detection scheme per branch.

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.

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 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 score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.004
GPT teacher head0.234
Teacher spread0.230 · 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