The Effect of Acoustic Impedance on Subsurface Absorber Geometry Reconstruction using 1D Frequency-Domain Photoacoustics
Bibliographic record
Abstract
This paper considers the effect of an impedance mismatch between the absorber and its surroundings on the aborber reconstructions from the photoacoustic signal profile, in particular when a non-delta input pulse is used. A transfer function approach is taken, demonstrating in the case of impedance mismatch how the total response can be modeled using the sum of the mismatch-free response and its time-delayed, time-reversed replicas, which may or may not overlap. It is shown how this approach can be exploited to accommodate the effects of non-delta pulses and/or pulse-equivalent waveforms such as linear-frequency-modulated (LFM) chirps, and impedance mismatches in any inversion algorithms, even in the presence of large reflection coefficients. As a consequence, for simple-absorber reconstruction algorithms that assume impulses or 'short enough' pulses, the compressive portion of the measured response may be used in reconstruction formulas that do not model the impedance mismatch, regardless of the size of the mismatch. For longer-duration input waveforms, it is demonstrated how existing reconstruction methods can be successfully adapted to include the effect of the impedance mismatch. Simulations are used to illustrate these ideas. The gained physical insight into how components of the generated pressure wave carry absorber information is then exploited for signal inversion and absorber reconstruction in the frequency domain when multi-frequency modulation chirps are used for photoacoustic radar pressure measurements. The foundational theoretical developments ultimately address impendance mismatch issues germane to the major photoacoustic frequency-domain imaging modality to-date, which is the photoacoustic radar.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".