Photoacoustics Waveform Design for Optimal Signal to Noise Ratio
Bibliographic record
Abstract
Time-frequency analysis in waveform engineering can be applied to many detection and imaging systems, such as radar, sonar, and ultrasound to improve their Signal-to-Noise Ratio (SNR). Recently, photoacoustic imaging systems have attracted researchers’ attention. However, the SNR optimization problem for photoacoustic systems has not been fully addressed. In this paper, the one-dimensional SNR optimization of the photoacoustic response to an input waveform with finite duration and energy was considered. This paper applied an eigenfunction optimization approach to find the waveform for optimal SNR for various photoacoustic absorber profiles. SNR gains via the obtained optimal waveform were compared with simple square-pulse and pulsed sinusoidal waveforms in simulations. Results showed that by using the optimal waveform, SNR can be enhanced especially if the input wave duration is comparable with the absorber time profile duration. The optimal waveforms can achieve 5%–10% higher SNR than square pulses and over 100% higher SNR compared with pulsed sinusoids. The symmetry between time and frequency domains assures similar behavior when temporal durations of the input waveforms are too short or too long compared with the absorber.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".