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Record W2039466950 · doi:10.1109/ultsym.2012.0389

Variably polarized ceramic for passive aperture apodization

2012· article· en· W2039466950 on OpenAlexaff
Andrew W. Joyce, Geoffrey R. Lockwood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsQueen's University
Fundersnot available
KeywordsApodizationPolarization (electrochemistry)TransducerPolingMaterials scienceAperture (computer memory)OpticsPiezoelectricityAcousticsCeramicSynthetic aperture radarElectrodeComputer scienceOptoelectronicsPhysicsFerroelectricityComputer visionDielectric

Abstract

fetched live from OpenAlex

Large aperture transducers, such as those required for the crossed-electrode array geometry for three-dimensional real-time ultrasound imaging, introduce edge artifacts called range secondary lobes. These artifacts are attenuated through apodization; however, accomplishing apodization without introducing additional system channels or new material interfaces proves challenging. An alternative technique is presented where the piezoelectric response of a PZT ceramic is engineered to achieve apodization in a passive manner. By utilizing a sacrificial curved structure during polarization, a variable degree of remnant polarization is achieved across the expanse of the transducer while using a constant polarizing potential. Experimental measurement of poling parameters and finite-element simulations allowed the ceramic structure to be designed so as to accomplish appropriate apodization. This passive means of apodization complements the low-system complexity of the crossed-electrode array.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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