Wavelet-based estimator versus spectral correlation method for acoustic target sizing
Bibliographic record
Abstract
This paper presents two different methods for estimating the diameter of an immersed wire insonified by an ultrasonic plane wave. The first part of the backscattered echo, called the quasi-rigid backscattered echo (QRBE), and the quasi-rigid form function (QRFF) are described first. It is shown that the QRBE contains the size information (diameter) of the target. In the first method, this size is obtained by associating a pattern recognition procedure with a spectral correlation. The second method is based on a continuous wavelet analysis of the QRBE at a particular scale with a judiciously selected wavelet. After a brief description of the wavelet tool, we present in detail the wavelet-based approach for target sizing. Results thus obtained are compared with experimental measurements using copper and steel wires. They show that for the largest diameters, the results are similar. On the other hand, the more the diameter decreases, the more the wavelet-based estimator is distinguished by giving a better relative precision. In terms of the cost of calculation, the second method is better since it requires only one wavelet transform at only one single scale against a Fourier transform and an iterative correlation procedure, necessary in the first technique.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".