A split-aperture transmit beamforming technique with phase coherence grating lobe suppression
Bibliographic record
Abstract
A small element-to-element pitch (~.5λ) is conventionally required for phased array ultrasound transducers to avoid large grating lobes. This constraint can introduce many fabrication difficulties, particularly in the development of highfrequency phased arrays at operating frequencies greater than 30 MHz. In this paper, a new transmit beamforming technique along with sign coherence factor (SCF) receive beamforming is proposed to suppress grating lobes in large-pitch phased-array transducers. It is based on splitting the transmit aperture (N elements) into N/K transmit elements and receive beamforming on all N elements to reduce the temporal length of the transmit grating lobe signal. Therefore, the use of synthetic aperture beamforming, which can introduce relative phase distortions between the echoes received over many transmit events, can be avoided. After each transmit-receive event, the received signals are weighted by the calculated SCF to suppress the grating lobes. After pulsing all sub-apertures, the RF signals are added to generate one line of the image. Simulated 2-way radiation patterns for different K values show that grating lobes can be suppressed significantly at different steering angles. Grating lobes can be suppressed by approximately 20 dB with K = 2 at steering angles greater than 25° and an element pitch greater than 0.75λ. A technique for determining the optimal transmit sub-apertures has been developed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".