Multi-Axial Transducers for Passive Point Source Localization
Bibliographic record
Abstract
Acoustic cavitation is often monitored by single passive cavitation detectors. A single transducer can provide information on the type, intensity, and duration of activity, while being small and relatively inexpensive. However, spatial information about cavitation activity is lacking with these systems. Multi-axial transducers, or transducers with more than one pair of orthogonal electrodes, are hypothesized to provide directivity information about a received signal using a single transducer. Thus, the objective of this study was to demonstrate in-silico that single multi-axial transducers can provide directivity information and two multi-axial transducers can provide accurate source location estimates. Two sets of frequency-domain simulations were performed, one each for two biaxial transducers (two pairs of orthogonal electrodes) and two triaxial transducers (two pairs of orthogonal electrodes). Transducers were placed 2 cm apart along the x axis while acoustic point sources were placed at depths between 10 and 14 cm from the top face of the transducers. Points were a maximum of 4 cm away from the origin in the xy-plane. Signal and amplitude ratio were mapped to source direction using a radial basis function. Trigonometry was then used to calculate two- and three-dimensional positions for biaxial and triaxial cases, respectively. RMS and median localization errors were calculated as a measure of accuracy. Median localization error of less than 1 mm was observer in all cases. Therefore, single multi-axial transducers can estimate the direction of a point source and pairs of multi-axial transducers can estimate the location of a point source.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".