Fixed-Frequency Low-Loss Dielectric Material Sensing Transmitter
Bibliographic record
Abstract
This article presents an all-in-one system for the detection of the relative permittivity of samples in direct contact with a sensor antenna, based on the frequency variation detection and using a cost- and energy-effective manner. In a specific application, in which the antenna should be in contact with the sensing material, characteristics of the antenna change with respect to the frequency spectrum for different materials. Conventionally, a frequency spectrum monitoring device is required to monitor these changes, and the sensing data should be obtained by postprocessing the observation. The proposed system converts the sensing information in the frequency response of the device to a voltage, which can be utilized further for transmission as well as compensating and frequency retuning the system. The sensor antenna loads the radio frequency oscillator at the transmitter resulting in a change at the operating frequency of the system. A small portion of the signal is sampled and used for recovery in a phase/frequency comparator (PFC). The output of the PFC is a voltage corresponding to the difference between the operating frequency and the reference signal. The proposed sensor system is fabricated at the 915-MHz ultrahigh-frequency radio frequency identification band using on-off keying modulation as an evaluation, and the measured results with some known samples are presented. Since the proposed technique is implemented by utilizing the building blocks of a conventional transmitter, the power consumption and cost of the system are kept intact.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".