Modelling of large-scale multi-frequency CMUT arrays with circular membranes
Bibliographic record
Abstract
Large-scale multi-frequency capacitive micromachined ultrasonic transducer (CMUT) arrays consist of thousands of closely packed interlaced large and small membranes for applications such as super-harmonic imaging, multi-band operation, imaging-therapy, contrast and photoacoustic imaging. An accurate nonlinear lumped equivalent circuit model including the effects of the self and mutual acoustic interactions between CMUT cells was used for modeling of multi-frequency CMUT arrays. Recently, we showed precise models for simulating the behavior of multi-frequency CMUT arrays including a few number of cells with excellent agreements with FEM and experimental results. In this work the model was extended to simulate large-scale multi-frequency CMUT arrays for realistic ultrasound applications which is not feasible with FEM analysis. The models predicted the effects of the mutual radiation impedance between cells with dissimilar sizes for complex and large-scale arrays. The effects of different biasing methods were evaluated on performance of the array for different sets of CMUT parameters. This tool can be used to optimize the performance of the multi-frequency CMUT arrays before fabrication.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".