Design of multichannel nonuniform transmultiplexers using general building blocks
Bibliographic record
Abstract
The paper considers design of multichannel, nonuniform-band transmultiplexers. It is well known that using traditional building blocks-up and downsamplers and linear time-invariant (LTI), causal filters-nonuniform transmultiplexers typically do not achieve perfect reconstruction. To alleviate this, we propose to build nonuniform transmultiplexers using general dual-rate structures that provide more design freedom, and hence, perfect reconstruction can be achieved. Such general transmultiplexers have a new source of error called aliasing distortion in addition to the traditional cross-talk, magnitude, and phase distortions. We propose a composite error criterion that captures all four distortions in one. Using this error criterion as reconstruction performance measure. We develop an optimal design procedure and apply it to a three-channel nonuniform example, yielding an FIR transmultiplexer that has good frequency-limiting properties in the synthesis end and is very close to perfect reconstruction.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".