Robust Ultra-High Resolution Microwave Planar Sensor Using Fuzzy Neural Network Approach
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Bibliographic record
Abstract
In this paper, we develop a robust and fault-tolerant approach to microwave-based sensitive measurements using fuzzy neural network (FNN). Microwave chemic-identification, recently, is employing active planar ring resonators to enhance the resolutions significantly. However, in practice, when the technology of resolution improves, the results become more prone to minor variations in the measurement setup and user error. In order to eliminate these unwanted and uncontrollable deviations from the final allocations, we propose a novel and robust approach that uses more than one parameter out of measurements and incorporates FNN as a machine learning architecture at the post processing stage of sensing to obtain fault-tolerant classification. We have compared different membership functions used in the FNN and shown improvement in assigning accuracy from 49% (single parameter-dependent) up to 81.5% (three parameters-dependent) on an average of four materials, such as isopropanol-2 (IPA), ethanol, acetone, and water.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it