A Battery-Less Non-Hybrid Six-Port RFID-Based Wireless Sensor Architecture for IoT Applications
Bibliographic record
Abstract
This article introduces a novel battery-less wireless sensor architecture, which is based on the principle of direct frequency conversion. The proposed architecture uses a non-hybrid six-port structure to integrate a UHF radio frequency identification (RFID) chip with sensing elements to create a sensor node. The RFID chip provides a unique identification to the sensor node and the sensing element enables the reading of environmental conditions. The non-hybrid six-port structure unequally divides an incoming RFID interrogator signal into an in-phase and quadrature branch. Using the novel unequal distribution in a six-port allows higher power being directed to the RFID chip to ensure a longer read range. The I and Q signals reflected by the RFID chip and the sensing element, respectively, are mixed without the use of a lossy or an active mixer. The mixed signal’s amplitude and phase are directly dependent on the values of the attached sensing element. To read the value of the sensing element wirelessly at a reader, an IQ demodulator is used, which determines the phase of the backscattered signal. As a result, various sensed parameters such as light intensity, voltage, or force may be read wirelessly without requiring a battery at the node. To demonstrate the performance, a p-i-n diode is used as a sensing element to read voltages wirelessly at a distance of up to 2.45 m.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".