Broadband beamfoming using Nested Planar Arrays and 3D FIR frustum filters
Bibliographic record
Abstract
A topology of planar array called Nested Planar Arrays (NPAs) is used for broadband beamforming. The NPAs consist of several Uniform Planar Arrays (UPAs), each one with the double element distance of the previous array. The signals from these arrays are fed into different subbands which process different octaves of temporal frequency bands. The combination of NPAs and multirate techniques leads to the same 3D frustum filter frequency specifications for all subbands. The passband of these 3D frustum filters does not include the low temporal frequencies where it is difficult to achieve high selectivity. Simulation results indicate that with the same number of sensors, NPA can achieve longer aperture size compared to a UPA and thus higher selectivity particularly for lower temporal frequencies. For the same aperture size, NPA can be implemented with much less sensors and much less computations than a UPA with small deterioration in the performance.
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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) | 0.001 | 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 | 0.000 |
| 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".