Experimental results of compressive sensing based imaging in ultrasonic non-destructive testing
Bibliographic record
Abstract
In this paper, we use sparse signal recovery for non-destructive testing application, where the image of a test sample is extracted from ultrasonic array data. Using a frequency-domain model for the received signals, we propose two rearrangements of the data model to convert it to the format needed for sparse signal recovery. Each proposed approach is tested on the experimental data and the performance is compared with a MUSIC based imaging algorithm. The first rearrangement uses the measurement data obtained from individual transmitter elements in the array at a single frequency bin. We call this approach incoherent compressive sensing (IncCS). The second rearrangement is based on multiple measurement vectors (MMV) model. While the IncCS image has less background noise, the MMV results show better resolution in imaging the targets in the region of interest (ROI). The performance of the proposed approaches is better than MUSIC based algorithm. The MMV results also show that by using only half of the ultrasonic elements in the array, we can obtain an image which has comparable performance with the image obtained using the full array data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".