Динамическая визуализация геометрических понятий как средство развития пространственных представлений подростков
Why this work is in the frame
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Bibliographic record
Abstract
Synthetic aperture focusing techniques (SAFT) make the spatial resolution of the conventional ultrasound imaging from a single-element focused transducer more uniform in the lateral direction. In this work, two new frequency-domain (FD-SAFT) algorithms are proposed, which are based on the synthetic aperture radar's wavenumber algorithm, and 2-D matched filtering technique for the image reconstruction. The first algorithm is the FD-SAFT virtual source (FD-SAFT-VS) that treats the focus of a focused transducer as a virtual source having a finite size and the diffraction effect in the far-field is taken into consideration in the image reconstruction. The second algorithm is the FD-SAFT deconvolution (FD-SAFT-DE) that uses the simulated point spread function of the imaging system as a matched filter kernel in the image reconstruction. The performance of the proposed algorithms was studied using a series of simulations and experiments, and it was compared with the conventional B-mode and time-domain SAFT (TD-SAFT) imaging techniques. The image quality was analyzed in terms of spatial resolution, sidelobe level, signal-to-noise ratio (SNR), contrast resolution, contrast-to-speckle ratio, and ex vivo image quality. The results showed that the FD-SAFT-VS had the smallest spatial resolution and FD-SAFT-DE had the second smallest spatial resolution. In addition, FD-SAFT-DE had generally the largest SNR. The computation run time of FD-SAFT-VS and FD-SAFT-DE, depending on the image size, was lower by 4 to 174 times and 4 to 189 times, respectively, compared to the TD-SAFT-virtual point source.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.010 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it