Efficient output-pruning of the 2-D FFT algorithm
Bibliographic record
Abstract
In this paper, an efficient algorithm for pruning the output samples of the radix-(2 /spl times/ 2) two dimensional decimation-in-time FFT algorithm is presented. Comparisons with the existing algorithm show that substantial savings on the arithmetic operations, data transfers, address computations, and twiddle factor evaluations or accesses to the lookup table can be made. This is achieved by grouping in the radix-(2 /spl times/ 2) 2-D DIT FFT algorithm all the stages that involve unnecessary operations into a single stage and introducing a new recursive technique for computing the resulting stage. Due to this grouping and the efficient indexing process introduced in this paper, the implementation of the proposed algorithm requires a minimum number of stages; however, that of the existing algorithm uses all the stages required by the radix-(2 /spl times/ 2) 2-D DIT FFT. Therefore, the proposed algorithm also reduces the overall control and structural complexities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".