FPGA architectures for electronically scanned wide-band RF beams using 3-D FIR/IIR digital filters for rectangular array aperture receivers
Bibliographic record
Abstract
Real-time digital implementation of three-dimensional (3-D) infinite impulse response (IIR) beam filters are discussed. The 3-D IIR filter building blocks have filter coefficients, which are defined using algebraic closed-form expressions that are functions of desired beam personalities, such as the look-direction of the aperture, the bandwidth and sampling frequency of interest, inter antenna spacing, and 3dB beam size. Real-time steering of such 3-D beam filters are obtained by proposed calculation of filter coefficients. Application specific computing units for rapidly calculating the 3-D IIR filter coefficients at nanosecond speed potentially allows fast real-time tracking of low radar cross section (RCS) objects at close range. Proposed design consists of 3-D IIR beam filter with 4 4 antenna grid and the filter coefficient generation block in separate FPGAs. The hardware is designed and co-simulated using a Xilinx Virtex-6 XC6VLX240T FPGA. The 3-D filter operates over 90 MHz and filter coefficient computing structure can operate at up to 145 MHz.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".