<title>Noise reduction in RF cavity wireless strain sensors</title>
Bibliographic record
Abstract
In this paper we will be describing noise reduction techniques for new type of wireless sensor for use in monitoring strain in civil structures. This strain sensor is a passive sensor that can be embedded and then interrogated through an attached antenna and hence has the advantage that is requires no permanent electrical or optical connection. The sensor is a metal coaxial cylindrical cavity embedded or attached to the object in which strain is to be measured. As the structure changes dimension in response to applied forces the electromagnetic cavity also changes dimension and hence its resonant frequency also changes. The sensor can then be interrogated via the antenna and the resonant frequency of the electromagnetic cavity determined. Once the resonance frequency is determined it can be used to calculate the strain in the structure. We will present results on the use of time domain gating to reduce environmental and instrumental noise. We will also present results using peak fitting techniques that make optimum use of signals to locate the resonance. These noise reduction techniques make the use of this type of sensor applicable in a wider range of environments. We have demonstrated a strain resolution of 8 microstrain in a noisy environment by using peak fitting techniques. These techniques were much less sensitive to environmental sources of noise than FM modulation and phase sensitive detection.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".