Custom PXIe-567X-Based SAW RFID Interrogation Signal Generator
Bibliographic record
Abstract
This paper presents a fully reconfigurable and reliable PXIe-567X software-defined interrogation signal generator for surface acoustic wave (SAW)-based passive radio frequency identification (RFID). Contrary to most commercial SAW RFID readers that operate only at a unique predefined frequency, the proposed vector signal generator is aimed to operate at any frequency defined by the user, from 85 MHz to 6.6 GHz, including the 902-928 MHz and 2.45-GHz industrial scientific and medical bands. The PXIe-567X-based signal generator can accurately generate not only conventional ON-OFF keying (OOK)-modulated or pulsed binary phase shift keying (BPSK)-modulated SAW RFID interrogation signals, but also completely user-defined interrogation signals. A custom LabVIEW user interface that allows to control the carrier frequency, the power, and the waveform of the interrogation signal has been designed. The operator can switch between custom waveforms without resetting and reprogramming the whole system. The proposed RFID request signal generator has been validated using a complete measurement setup. Time-domain and frequency-domain validation tests of OOK modulated and wideband pulsed BPSK-modulated signal generation have been conducted at 170, 340, 915 MHz and 2.45 GHz. Very accurate results have been obtained. The PXIe-567X-based RFID interrogator has been tested with a fabricated 3-b SAW RFID tag. The operability of the entire SAW RFID system has been demonstrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".