Wireless Microwave Sensor Network Using Split Ring Resonators for Ice Monitoring Applications
Bibliographic record
Abstract
Miniaturization of microwave sensor nodes contributes to the development of portable interconnected networks, expanding microwave sensors applications to distributed real-time monitoring and detection systems. This paper presents, for the first time, a portable and wireless microwave sensor network (WMSN) designed for ice monitoring applications. The network includes three sensor nodes that are wirelessly connected to a short-range central hub or server via WiFi technology for data transmission and communication. Each node within the WMSN is composed of a microwave resonator sensor and a readout circuit to monitor the ice formation in the designated area of the microwave resonator and to send the sensor data to the server. The microwave sensor is implemented using three split-ring resonator tags coupled to a transmission line, and operating at a frequency between 2.5 GHz and 3 GHz. The readout circuit is capable of inherently exciting the microwave sensor with a microwave signal at a finite number of frequencies. Then, the received signals from the resonators are converted to DC voltages to be transmitted to the server. The WMSN has been successfully tested over a temperature range of -40∘C to 20∘C and a frequency range of 1.75 GHz to 2.8 GHz at 16 discrete frequency points with a step size of 70 MHz. Measured results demonstrate that the WMSN can differentiate between ice, water, dust, and air, eliminating the need for a vector network analyzer. This feature makes the proposed WMSN attractive for industrial applications including roadways, aircraft, and wind turbines.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".