Bibliographic record
Abstract
In state-of-the-art laser ultrasonics (LU), the signal-to-noise ratio (SNR) is limited by the shot noise of the detected laser radiation. Further improving the SNR then requires averaging multiple signals or increasing generation and/or detection laser intensities. The former strategy is time consuming and the latter leads to surface damages. For signal-independent limiting noises, Hadamard multiplexing increases the SNR by averaging multiple signals in parallel using a single detector. Here we consider the use of Hadamard multiplexing in LU for the non-contact ultrasonic inspection of materials. By using 31 element Hadamard masks to modulate the spatial intensity distribution of the generation laser beam, the measured SNR is improved by a factor 2.8, in good agreement with the expected multiplexing or Fellgett advantage. In contrast to many other applications of Hadamard multiplexing, the SNR is improved for shot-noise-limited measurements since the shot noise level is independent of the signal in LU. The Hadamard multiplexing of the detection laser beam is also considered but can only lead to a throughput or Jacquinot advantage. However, for pulse-echo LU, the Hadamard multiplexing of both generation and detection laser beams allows using the synthetic aperture focusing technique (SAFT).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".