Time‐division polynomial pre‐distorter for linearisation of 1.5 T MRI power amplifier
Bibliographic record
Abstract
A time‐division polynomial (TDP) model is proposed for modelling and linearising a 1.5 T magnetic resonance imaging (MRI) power amplifier (PA) with strong non‐linearity in high input signal dynamic range. In order to demonstrate the merit of this non‐linear model, a 64 dBm 1.5 T MRI PA (63.89 MHz) and two different Sinc‐pulse signals are used in modelling and linearisation measurements. The TDP is compared with the conventional non‐memory polynomial (NMP) and no digital pre‐distortion for the 1.5 T MRI PA, which is driven by test signal with 2 ms time length and 2% duty cycle. The proposed TDP leads to up to 9 dB improvement in the normalised mean square error compared with the NMP in two different test signals. More importantly, TDP illustrates significantly better reduction in amplitude modulation/amplitude modulation (AM/AM) and amplitude modulation/phase modulation (AM/PM) conversion compared with the NMP.
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.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".