Design of high order FIR Unimodular Filter Banks and its application in combatting instantaneous erasures
Bibliographic record
Abstract
FIR Unimodular Filter Banks (UFBs) offer the minimum possible delay among all the filter banks with the same downsampling rate. Although Order-one UFBs can be factorized into degree-one unimodular matrices, this factorization is not possible for higher order UFBs. This is unfavorable because in most applications, banks with longer length filters result in better performance. In this paper, we investigate the design of high order FIR UFBs using a time domain approach. We show that due to the flexibility of this method, long filters with decent frequency responses are achievable. We also propose a special factorization for high order UFBs which reduces the large number of free parameters of time domain approach. Although this factorization is not complete, we show that it can give reasonably good filters. Finally we use the designed filters in the application of reconstructing the output of an Oversampled FB in the case of instantaneous erasure in sub-band domain. Instantaneous erasure accounts for a situation where the sub-band samples are erased based on different patterns in each time instance and the minimum delay of the OFB is crucial for reconstructing the output.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".