Design of high order FIR Unimodular Filter Banks and its application in combatting instantaneous erasures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it