Efficient Implementation of Broadband Beamformers Using Nested Hexagonal Arrays and Frustum Filters
Bibliographic record
Abstract
A broadband beamformer is proposed based on nested hexagonal arrays, hexagonal frustum filters and multirate techniques. The nested hexagonal arrays used here consist of several hexagonal arrays of increasing size in the x-y plane (each one called subarray) where the distance between elements in each subarray is two times larger than in the previous one. The proposed beamformer consists of subarray beamformers, each one using the signals obtained from one of the nested hexagonal arrays as the input. These signals are filtered and downsampled so that the Region of Support (ROS) of the resulting 3D signals in the 3D frequency domain are the same for all subbands. The same hexagonal frustum filter design can therefore be used for all subarray beamformers to pass the desired signal and eliminate interferences. The use of nested arrays leads to larger effective aperture at low temporal frequencies and thus, better selectivity for low frequencies. Further, hexagonal arrays are known to require a lower sensor density for alias free sampling than rectangular arrays. Examples illustrate the performance of the proposed beamformer with respect to beampattern and computational complexity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".