A Systolic-Array Architecture for First-Order 3-D IIR Frequency-Planar Filters
Bibliographic record
Abstract
A massively parallel systolic-array architecture is proposed for the implementation of real-time VLSI spatio-temporal 3-D IIR frequency-planar filters at a throughput of one-frame-per-clock-cycle (OFPCC). The architecture is based on a differential-form transfer function and is of low circuit complexity compared with the direct-form architecture. A 3-D look-ahead (LA) form of the transfer function is proposed for maximizing the speed of the implementation, which has a nonseparable 3-D transfer function. The systolic array enables real-time implementation of 3-D IIR frequency-planar filters at radio-frequency (RF) frame-rates and is therefore a suitable building block for 3-D IIR digital filters having beam- and cone-shaped passbands as required for smart-antenna-array beam-forming applications involving the broadband spatio-temporal filtering of plane-waves. The fixed-point systolic-array implementation have a throughput of OFPCC and the tested real-time prototype achieves frame (clock) sample frequencies of up to 90 MHz using one Xilinx Virtex-4 sx35-10ff668 FPGA device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".