A New Model for Array Spatial Signature for Two-Layer Imaging With Applications to Nondestructive Testing Using Ultrasonic Arrays
Bibliographic record
Abstract
Imaging multilayer materials is a common challenge in seismology, medical diagnosis, and nondestructive testing. One of the applications of multilayer imaging is ultrasonic immersion test where the material under test and the transducer array are immersed in water. The main imaging challenge in immersion test (or in imaging any multilayer medium) is that since the sound wave propagates with different speeds in different layers of a multilayer medium, such a medium cannot be assumed homogenous. As a result, calculating the sound travel time for the received signal due to backscattering from such a nonhomogenous medium is not as straightforward as in the case of homogenous materials. In this paper, we propose a new model for the array spatial signature which can be used in frequency-domain algorithms that are used for imaging a two-layer medium when an array of transducers is utilized. To do so, we model the interface between the two layers as a spatially distributed source which consists of infinite number of point sources. Then, we use this model to develop a new array spatial signature for any point inside the second layer of a two-layer medium. This new array spatial signature can be used for multilayer ultrasonic imaging in frequency-domain imaging techniques including the conventional beamforming technique, the MUSIC method, and the Capon algorithm. Numerical simulations as well as experimental data are used to examine the accuracy of the proposed model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".