Quantitative ultrasonic phased array imaging
Bibliographic record
Abstract
When imaging with ultrasonic phased arrays, what do we actually image? What quantitative information is contained in the image? Ad-hoc delay-and-sum methods such as the synthetic aperture focusing technique (SAFT) and the total focusing method (TFM) fail to answer these questions. We have shown that a new quantitative approach allows the formation of flaw images by explicitly inverting the Thompson-Gray measurement model. To examine the above questions, we have set up a software simulation test bed that considers a 2-D scalar scattering problem of a cylindrical inclusion with the method of separation of variables. It is shown that in SAFT types of imaging the only part of the flaw properly imaged is the front surface specular response of the flaw. Other responses (back surface reflections, creeping waves, etc.) are improperly imaged and form artifacts in the image. In the case of TFM-like imaging the quantity being properly imaged is an angular integration of the front surface reflectivity. The other, improperly imaged responses are also averaged, leading to a reduction in some of the artifacts present. Our results have strong implications for flaw sizing and flaw characterization with delay-and-sum images.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".