A virtual point source pulse probing technique for suppressing grating lobes in large-pitch phased arrays
Bibliographic record
Abstract
We have been investigating a recently developed technique called Phase Coherence Imaging (PCI) that suppresses grating lobes in large-pitch arrays by defining a weighting factor based on the instantaneous phase of received echoes. This could lessen many of the limitations associated with the fabricating of high-frequency micro-arrays. The technique is not very effective however, when used with conventional transmit beamforming because the received grating lobe echoes are narrowband. In previous work, we suggested and theoretically evaluated a new beamforming technique called "Pulse Probing" in order to generalize the application of PCI for suppressing grating lobes when using conventional transmit focusing. In this work, the experimental verification of the technique using a commercially available high-frequency linear array system (Vevo 2100, VisualSonics) is reported. We demonstrate that by pre-calculating PCI weighting factors using a defocused pulse from a virtual point source, and later applying them to conventional transmit beamforming, the grating lobes resulting from a 50 MHz, 64- element, 1.26 λ pitch phased array can be suppressed approximately 40 dB more than when using PCI alone. We have further experimentally shown that with this technique, grating lobes resulting from wire targets embedded in a tissue-mimicking phantom could be suppressed while the tissue speckle and the wire target were preserved.
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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 | 0.000 |
| 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 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".