Sensor Antenna Transmitter System for Material Detection in Wireless-Sensor-Node Applications
Bibliographic record
Abstract
This paper presents a fully combined sensor-antenna transmitter system for sensing relative permittivity of material at microwave frequencies. The transmit (Tx) antenna senses the relative permittivity of specimen and loads the radio frequency (RF) oscillator which translates the relative permittivity to the operational frequency of the carrier signal. At the same time, independent data, which can be the Radio Frequency Identification (RFID) information, are ON-OFF keying modulated over the carrier. At the receiver, a demodulator system extracts the data stream as well as recovers the carrier frequency using a zero-crossing detector. Therefore, the relative permittivity of the sample can be obtained from the extracted frequency shift as a digital number. As all the sensing elements in the proposed structure are passive, the sensor does not increase the power consumption of the system. The sensor-transceiver capability at low powers makes it ideal for sensor nodes in the Internet of Things applications. Since the sensing and demodulation are implemented at the same time, the complexity for detection reduces significantly compared with traditional RF/microwave sensing techniques which need complicated frequency spectrum monitoring equipment. The output of the system is a digital number correlated to the dielectric constant of the specimen and the independent data stream for communication. The proposed sensor node is fabricated at the 2.45-GHz ISM band as an evaluation and the measurement results with some known samples are presented.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".