Author manuscript, published in "NEWCAS'12, Montreal: Canada (2012)" Robustness of Parallel Multi-Rate A/D Converters to Anti-Aliasing Filter Non-Idealities
Bibliographic record
Abstract
Abstract — Any digitization system must be preceded by an anti-aliasing filter. For wideband high frequency applications, parallel multi-rate conversion systems such as time-interleaved or hybrid filter bank analog-to-digital converters (resp. TI-ADC or HFB) are attractive solutions. This paper compares the robustness of both techniques with respect to non-idealities of the anti-aliasing filter (AAF). Theoretical results show that the signal-to-noise ratio (SNR) degradation due to out-of-band signals is lesser for HFBs than for TI-ADCs, provided that the analysis filters of the HFB are selective enough. Simulation results show that this is the case even for low-order analysis filters in the case of a four-channel HFB. I.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.429 | 0.159 |
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".