A New 3-D Imaging Technique Integrating Ultrafast Compounding, Hadamard Encoding, and Reconfigurable Fresnel Lensing
Bibliographic record
Abstract
). However, creating a two-way focused 3-D image in real time is difficult with these arrays because azimuth and elevation dimensions cannot be beamformed at the same time. This work describes a new 3-D imaging technique that uses the flexibility of bias-sensitive substrates to create a high-quality elevation focus on a crossed electrode array. The principle behind this technique is to perform conventional compound imaging with an azimuth set of electrodes while implementing a bias controllable elevation lens with an elevation set of electrodes. On transmit, the biases are chosen to mimic a Fresnel lens. Then, on receive, the Hadamard coding is implemented along the elevation dimension. After decoding, we gain the RF data for each element across the elevation aperture even though there is effectively only one channel in that dimension. A 30-MHz, 128-element crossed electrode relaxor array was fabricated on a 1-3 electrostrictive composite substrate and was used to demonstrate the performance of the imaging technique. The on-axis -6-dB beamwidths were simulated to be 175 and [Formula: see text] in the azimuth and elevation directions, respectively, and the focus remained isotropic in the furthest elevation slice. Images were generated of a wire phantom to confirm the performance of the azimuth and elevational radiation patterns with good agreement between simulation and experiment. High-resolution 3-D volumetric images were generated of an ex vivo rat brain. Images of the cerebellum showed that the white and gray matter tracts could clearly be visualized with isometric resolution in both the azimuth and elevation dimensions.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".