A ZERO-Power Sensor Using Multi-Port Direct-Conversion Sensing
Bibliographic record
Abstract
This paper presents a class of zero-power microwave sensor architecture based on the direct-conversion principle to eliminate data processing at the Internet of Things sensors and provide unpowered nodes. A base station (BS) transmits a single tone signal at the frequency of f0/2 toward the sensing node using an antenna. At the node, an antenna receives the signal, and a passive frequency doubler makes the frequency twice. Then, a multi-port structure directly modulates the sensing data at the frequency of f0and sent back to the BS by an antenna. The multi-port circuit has one input, one output, and some loading ports. In this paper, a six-port modulator and four similar sensitive capacitive resonators are used. A pair of resonators senses the variation of a sample under test (SUT) while the other pair is covered by a reference or known material. At the BS, any quadrature receiver can be used to demodulate sensing data. Here, a similar six-port structure is used to extract data and find the SUT variations. An example one-node system is implemented at f0= 2.45 GHz and evaluated by some standard SUTs. To support multiple nodes, a smart directional antenna is necessary at the BS, which also improves the overall efficiency of the system.
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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) | 0.000 | 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 | 0.001 |
| 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".