Simulation Study Of Super-Resolution In Hydrophone Measurements Of Pulsed Ultrasonic Fields
Bibliographic record
Abstract
In order to carry out reliable measurements of pulsed ultrasonic fields, the use of a piezoelectric hydrophone as receiver is recommended. However, due the finite size of the receiver aperture the measured acoustic pressure is affected by spatial averaging on the surface active face. The aim of this work is to deconvolve the spatial effects of the receiver hydrophone in order to reconstruct the pulsed ultrasonic field with a better spatial resolution. Hereby, the linear pulsed pressure field radiated in water by wideband planar transducers of 19 mm diameter, with central frequencies fc=2.25 MHz and fc=15 MHz are considered. The receivers are PVDF membrane hydrophones of 25 ?m - thickness with rectangular and circular apertures. The results of this study show the strong dependency of the reconstruction quality upon the signal-to-noise ratio (SNR). Generally, the quality of the reconstruction decreases with decreasing SNR. Good reconstruction quality has been obtained with correlation coefficient larger than 0.9936 when the “acquired” signals are not too much noisy (SNR=60dB). In this case, improvement of the spatial resolution by a factor of 5 and 9 respectively could be reached. The reconstruction quality depends also upon the hydrophone dimensions, the axial distance to the source, the central frequency and the spectral frequency bandwidth of the pressure pulse.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".