Magnetic gradient full-tensor fingerprints for metallic objects detection of a security system based on anisotropic magnetoresistance sensor arrays
Bibliographic record
Abstract
Concealed metallic object detection is one of the critical tasks for any security system. It has been proved that different objects have their own magnetic fingerprints, which are a series of magnetic anomalies determined by shape, size, physical composition, etc. This study addresses the design of a low-cost power security system for the detection of metallic objects according to their response to the magnetic field. The system consists of three anisotropic magnetoresistance (AMR) sensor arrays, detection circuits, and a microcontroller. A magnetic gradient full-tensor configuration, utilizing four AMR sensors arranged on a planar cross structure, was employed to construct a two-dimensional image from the obtained data, which can further suppress the background noise and reduce the orientation and orthogonality errors. The performance of the system is validated by data validation and multiple object feature segmentation. Numerous magnetic fingerprinting results demonstrate that the system can configure metallic objects more than 50cm clearly and identify multiple objects separated by less than 20 cm, which indicates the feasibility of using this magnetic gradient tensor fingerprint method for metallic object 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 | 0.000 | 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 | 0.000 |
| 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".