Enabling low-cost full matrix capture acquisition using MobileViT with binary ultrasonic data
Bibliographic record
Abstract
The combination of Full Matrix Capture (FMC) and the Total Focusing Method is nowadays considered the gold standard in ultrasonic imaging. However, with FMC, the size of the acquire data increases quadratically with the number of elements, leading to significant storage and processing requirements that limit its use in portable systems and complicates the adoption of 2-D matrix arrays. Our approach is to propose a simplified, easily scalable, and low-cost binary acquisition hardware architecture. This strategy enhances the detection of small diffractive targets (e.g., high temperature hydrogen attack and macrotextured regions) but causes significant information loss in amplitude and A-scan interpretation. Recent work has shown that U-NET autoencoders can reconstruct FMC amplitudes from binarized data, but their high computational load restricts practical deployment. We propose a lightweight alternative based on the MobileViTV3_V1_FPN architecture, combining convolutional efficiency with the generalization capability of vision transformers. The network is trained on a hybrid dataset comprising finite element simulations and experiments from realistic defects. Results demonstrate that the proposed model accurately reconstructs FMC and TFM amplitudes with a low computational cost, paving the way for compact low-cost multichannel acquisition systems. [Work supported by Evident Scientific and NSERC.]
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".