Chirplet-based imaging using compact piezoelectric array
Bibliographic record
Abstract
This paper presents the implementation of a chirplet-based matching pursuit technique called excitelet for imaging. High frequency bursts are injected into a structure by a piezoceramic (PZT) actuator and measurement is conducted by a compact array of PZT sensors, located remotely from the damage. The matching pursuit algorithm is implemented with a dictionary of atoms obtained from dispersed versions of the excitation, where the parameters of each atom are the propagation distance and the mode. For a selected point in the scan area and a given mode, the measured signal is correlated with a given atom value for each propagation path in the array configuration. A round-robin technique is used to add the contributions of all these correlation values for each point in the scan area for imaging. Simulations are first conducted for a 1.5 mm thick aluminium plate with signals synthesized for A0 mode propagating over distances corresponding to the location of a reflection or diffusion point in an area in front of an array of measurement points. The simulations show that the excitelet offers better localization of the reflection point, when compared with a group velocity-based, or time-of-flight (ToF) approach. The simulation results are validated experimentally using a 1.5 mm thick aluminium plate with a notch in the periphery of a hole. Bonded PZTs are used for both actuation and sensing of 2.5 cycles bursts at 300 kHz, 500 kHz and 850 kHz. Significant improvement of imaging quality is demonstrated with respect to classical imaging techniques.
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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 | 0.000 |
| 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) | 0.001 | 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".