Calibration Stability and Localization Accuracy of a Low-Cost and Portable Breast Microwave Sensing Device
Bibliographic record
Abstract
Breast cancer detection is limited in low- and middle-income countries and low-income and remote commu-nities in developed countries. Screening limitations contribute to disproportionally high mortality rates. To address these realities, a portable breast microwave sensing system was constructed using low-cost equipment. The device features a cylindrical array of twenty-four thin patch antennas each connected to a small, inexpensive Nano Vector Network Ana-lyzer (NanoVNA). Due to limitations with the NanoVNA, an enhanced response 2-port calibration method was used instead of a full 2-port calibration. The accuracy of the enhanced calibration model was compared to the full 2-port calibration using a Copper Mountain C1209 VNA. The NanoVNA cali-bration model agreed with the full 2-port model within 0.67 %. Experimental S11measurements were performed on metal rods from 0.6 - 4.4 GHz. A radar image reconstruction method was used to extract the apparent location of the metal rods. The stability of the Nano Vnacalibration was monitored over 24 weeks, showing insignificant changes in the extracted rod positions. Correcting for positioning errors reduced the average localization error from 1.6 ± 0.9 mm to 1.1 ± 0.7 mm.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".