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Record W2154554543 · doi:10.1109/ultsym.2015.0464

Mutual radiation impedance for modeling of multi-frequency CMUT arrays

2015· article· en· W2154554543 on OpenAlexaff
Mohammad Maadi, Ryan K. W. Chee, Roger J. Zemp

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersRadiation impedanceAcousticsCapacitive sensingUltrasonic sensorAcoustic impedanceElectrical impedanceMaterials sciencePiston (optics)TransducerOpticsPhysicsRadiationEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Recently closely packed interlaced capacitive micromachined ultrasonic transducers (CMUTs) with different membrane sizes, on a scale smaller than the acoustic wavelength to mitigate grating lobes, have been introduced for multi-band operation, imaging-therapy, contrast and photoacoustic imaging. An accurate model including the effects of the self and mutual acoustic interactions between CMUT cells for simulating the multi-frequency arrays is necessary. However, the previous works evaluated the effects of the mutual radiation impedance on CMUT arrays with the same membrane sizes. Using the Bouwkamp integral method for integrating the farfield directionality factor of two flexural disks with different sizes, vibrating in phase, series expressions have been obtained for the mutual radiation impedance between two circular radiators of different radii in a rigid plane. Models for the mutual acoustic impedance between membranes of different sizes were validated against previous piston results but extended to flexural disks. The obtained expressions were implemented in an equivalent circuit model of multi-frequency CMUTs. Model predictions of resonance frequencies and displacements closely matched experimental vibrometer measurements of multi-frequency CMUT arrays that were done for low and high frequency transducers.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.251

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)0.0000.000

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 head0.046
GPT teacher head0.305
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations11
Published2015
Admission routes1
Has abstractyes

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