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Record W2158889907 · doi:10.1109/tuffc.2006.1632694

Investigation of cross talk in Kerfless annular arrays for high-frequency imaging

2006· article· en· W2158889907 on OpenAlexaff
Christine Démoré, Jeremy A. Brown, G.R. Lockwood

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2006
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhysicsOpticsAcousticsMaterials science

Abstract

fetched live from OpenAlex

The effect of electromechanical cross talk in high-frequency (> 30 MHz) kerfless annular arrays is investigated. Finite-element model predictions of the radiation patterns from arrays are compared to predictions from an ideal model without cross talk and with experimental measurements. High cross talk in the array causes element broadening and an increase in the amplitude of secondary lobes in the radiation pattern. However, an increase in the pulse ring-down time was not found. This can be attributed to the absence of lateral modes in the kerfless substrate. The level of the pedestal secondary lobes in the two-way radiation pattern increases linearly with the element path difference. The element broadening increases the effective element path difference, which increases the pedestal level for a kerfless annular array above the level for an ideal array. The broadening limits how close to an array one can image compared to the ideal case by reducing the contrast available in the image at small f-numbers. When the element broadening is taken into account by widening the electrode dimensions, the ideal radiation pattern agrees well with the finite-element model and experimental radiation patterns.

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 categoriesMeta-epidemiology (narrow)
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 score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.203
Teacher spread0.197 · 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.

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

Citations25
Published2006
Admission routes1
Has abstractyes

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