Efficient Segmentation for Large-Scale Filtering Based on Overlap-Save Method
Bibliographic record
Abstract
In modern communication systems, especially in fiber optics, implementing large-scale filters is inevitable. Finite impulse response implementation of these filters leads to intractable complexity, but advanced methods such as Overlap-Save (OLS) and Overlap-Add exist that significantly reduce complexity by implementing filters in the frequency domain. However, these methods require the Fast Fourier Transform (FFT) size to be larger than the filter size, limiting their applicability due to hardware constraints on the FFT size. In this paper, to remove this limitation, we propose a novel approach called low-complexity OLS-based segmented filter (LOSF). LOSF not only allows the FFT size to be smaller than the filter but also efficiently handles digital processing tasks, minimizing the number of FFT blocks required. We demonstrate the efficacy of LOSF through theoretical analysis and simulation studies, showcasing its ability to implement arbitrarily large filters within practical hardware constraints. Simulation results indicate that LOSF outperforms existing methods, achieving significant complexity reduction while maintaining filter performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".