Correlation of electric field and acoustic output on a prismatic biaxial transducer: A finite element study
Bibliographic record
Abstract
In single multiaxial transducers, the ultrasound beam can be steered by shifting the phases of the signals of two or more orthogonal electric fields applied to the piezoelectric. These capabilities can improve focusing on therapy applications such as transcranial focused ultrasound. A biaxial piezoelectric transducer (DL47, DelPiezo, FL) was modelled using the finite element software COMSOL Multiphysics®. The upper face was in contact with a volume of water, while the rest were free. The steering angle was controlled by varying the difference between the electrodes’ phases at the crystal’s resonant frequency. To reduce bias, the acoustic particle velocity was recorded at various lengths above the upper transducer's face while the average electric field was calculated for different areas on the surface. Linear regression fits of the steering angle with respect to the average electric field and particle velocity were calculated. A correlation can be observed between the average electric field at the center of the piezoelectric and the steering angle. Furthermore, by reducing the recorded length of the particle velocity by 30%–40% there is a high correlation with the steering angle. A model can be derived using these correlations in which the ultrasound beam direction can be known by obtaining the electric field at the center of the crystal. [The authors want to acknowledge the support of the Government of Canada by the scholarship provided by Global Affairs Canada through the Emerging Leaders in the Americas Program.]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".