Random sampling applied to array beamforming: The aggregate beamformer
Bibliographic record
Abstract
It is shown how beamforming can be accomplished by randomly sampling array elements and time-aligning the data samples. The time-aligned samples are referred to as the ‘‘aggregate’’ signal. Time-alignment is done using conventional beamforming delays but data values are not summed. Beamforming weights (array shading) can be applied by modifying the sampling probability for each sensor. Simple methods for reconstruction of the original signal from the aggregate signal are discussed. The reconstructed signal of this aggregate beamformer has the same directional response as a conventional beamformer except that off-beam signals are transformed into white noise. This noise can be reduced as required by adjusting the sampling rate of the aggregate signal. Higher beam-steering resolution than that of conventional beamforming is achieved without the need for data interpolation. The hardware and computational complexity of the aggregate beamformer does not increase as the number of array elements is increased. Aggregate beamforming offers a high degree of integration for arrays with many elements, such as 3-D arrays.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".