Adaptive Focusing for Source Localization in EMI Sensing of Metallic Objects: A Preliminary Assessment
Bibliographic record
Abstract
Abstract This paper considers a technique to deal with the problem of detecting and localizing objects in the data processing of electromagnetic induction (EMI) sensing. The technique is formulated using the concept of source power, which in our case is defined as the averaged sum of squared elements of a dipolar polarizabiltiy tensor over a measured time window for a transient electromagnetic (TEM) system. Under the valid dipole approximation to an EMI target, the source is point-like and therefore should occupy a small volume in space. This is the fundamental basis of the energy focusing technique for localizing a source. To achieve a focusing effect on a specified source, a focusing operator is constructed by minimizing the total output power subject to a unity response constraint for that assumed source. A closed-form expression is derived for source power as a function of a source location and can be used blindly without knowledge of the number of objects. The source power is related to data via a data covariance matrix, which in practice is computed with enough data samples. The experiments were conducted with the simulated and real data collected by a standard Geonics EM-63 system. The results, which we regard as a proof-of-concept, show that the focusing technique, under adequate signal-to-noise ratio (SNR), is able to accurately localize sources and is promising in EMI array processing.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".