Tiltable Ultrasonic Transducers: Concept, Beamforming Methods and Simulation
Bibliographic record
Abstract
This paper investigates the concept of tiltable ultrasonic transducers, and their application in focusing and steering in a transducer array. Results of simulated imaging processes suggest that physical focusing and steering with tiltable transducers is promising in reducing grating lobe and side lobe artifacts, and preserving the beam power, especially when steering to large angles. We propose that one embodiment of the tiltable transducers can be adaptive capacitive micromachined ultrasonic transducers (CMUTs) modeled as clamped plate radiators. By applying different levels of electrical field on their split electrodes, one can adjust the shape and orientation of the adaptive CMUTs adaptively and dynamically, creating a tilted effect in the beam direction. The feasibility of using adaptive CMUTs to implement tiltable transducers is studied using finite element modeling (FEM) and analytical modeling. Experimental measurements of the tilted behavior of fabricated adaptive CMUTs are also provided. Possible applications of the tiltable transducers, including spatial compounding, high intensity focused ultrasound (HIFU), adaptive imaging, and harmonic imaging, are discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".