Low-complexity distributed parallel processor for 2D IIR broadband beam plane-wave filters
Bibliographic record
Abstract
Real-time systolic-array-based implementations of VLSI two-dimensional (2D) infinite-impulse-response (IIR) frequency-planar beam plane-wave filters have potentially wide applications in the filtering of spatio-temporal RF broadband plane waves based on their directions of arrival (DOAs). Distributed-parallel-processor (DPP) implementations of the systolic arrays allow synchronous sampling of the 2D input signal array, but because of the direct-form structure they have high circuit complexity. To address the high-complexity problem, the differential-form 2D z-domain transfer function is employed here to obtain a novel DPP systolic-array-based filter architecture. Differential operators are obtained by applying elemental predistortion to the passive LR prototype filter network using series-connected negative-resistance elements. The proposed systolic 2D IIR architecture is implemented on a single Xilinx Virtex-4 Xc4v Sx35-10ff668 FPGA chip. Two examples of broadband plane-wave filtering supporting N = 32 and N = 64 sensors are reported. On-chip test results are achieved using stable real-time tests at frame sample frequencies of up to 90MHz as well as stepped hardware cosimulation in conjunction with a parallel-operating MATLAB/Simulink simulation.
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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) | 0.000 | 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) | 0.004 | 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".