A system for automated noise parameter measurements on MR preamplifiers and application to high <i>B</i><sub>0</sub> fields
Bibliographic record
Abstract
A noise figure and noise parameter measurement system was developed that consists of a combination spectrum and network analyzer, preamplifier, programmable power supply, noise source, tuning board, and desktop computer. The system uses the Y-factor method for noise figure calculation and allows calibrations to correct for a decrease in excess noise ratio between the noise source and device under test, second stage (system) noise, ambient temperature variations, and available gain of the device under test. Noise parameters are extracted by performing noise figure measurements at several source impedance values obtained by adjusting an electronically controlled tuner. Results for several amplifiers at 128 MHz and 200 MHz agree with independent measurements and with the corresponding datasheets. With some modifications, the system was also used to characterize the noise figure of MRI preamplifiers in strong static magnetic fields up to 9.4 T. In most amplifiers tested the gain was found to be reduced by the magnetic field, while the noise figure increased. These changes are detrimental to signal quality (SNR) and are dependent on the electron mobility and design of the amplifier's semiconductor devices. Consequently, gallium arsenide (GaAs) field-effect transistors are most sensitive to magnetic fields due to their high electron mobility and long, narrow channel, while silicon-germanium (SiGe) bipolar transistor amplifiers are largely immune due to their very thin base.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".