Lock-in Demodulation of Pulsed Ultrasonic Signals in High Noise Environments
Bibliographic record
Abstract
Acoustoelectric tomography (AET) maps tissue conductivity by locally modulating its electrical impedance with an ultrasonic wave in the presence of an electric field. The resulting nanoVolt-level, periodic pulsed signals are often buried in extreme noise levels, making traditional filtering and demodulation methods ineffective. This paper examines the feasibility and limitations of using lock-in demodulation to detect these signals in environments with low signal-to-noise ratios (SNR). While lock-in demodulation is traditionally designed to operate on continuous sinusoidal waveforms, its application and effectiveness to demodulate short and exponentially decaying pulsed signals has not been previously investigated. The paper presents a discrete model of a lock-in demodulation scheme and verifies it experimentally with ultrasound pulses measured in a phantom tissue and fed to a commercially available lock-in amplifier. The model is then used in a series of simulations to investigate the influence of model and signal parameters, such as filter order, cut-off frequency, pulse width, and SNR, on the demodulation scheme’s output. The results indicate that wider wavelets and higher-order filters with a narrow band allow for better noise rejection. The results can guide the design and use of lock-in demodulation schemes for pulsed ultrasonic signals and ultimately offer a more robust alternative to traditional demodulation methods used in AET.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".