Bibliographic record
Abstract
A sensor array having invariant signal-to-noise (SNR) gain over a wide band of frequencies is desirable, even essential, in communication. The design of sensor arrays with frequency invariant (FI) gain has been involved in several investigations. We attain a frequency-variable white noise gain constraint by approximating the constant gain contours in the plane of frequency/wavelength and white noise gain by a piecewise polynomial function, which reduces gain variation with frequency within the lower and middle portions of the design band. Meanwhile, we slightly decrease the sensor spacing, which reduces gain variation within the upper portion of the band, so that gain variation over a 10:1 design band is limited to 1 dB without increasing the sensor number. We perform the design of the equally-spaced array prescribed by J.G. Ryan and R.A. Goubran (see IEEE Trans. Speech Audio Processing, vol.8, no.2, p.173-6, 2000), in order to compare our design result with their's, and conduct the design of a space-tapered array with FI gain, leading to a result with more generality. We discuss the array's gain improvement, robustness to errors and capability of distance discrimination, and illustrate the trade-off between them.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".