Magnetic pattern recognition sensor arrays using CCD readout
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Magnetic encoding is currently widely employed in cheques, transaction cards, access cards and bank notes because of its robustness, economy, security, and ease of updating coded information. Coded magnetic information is currently read using either inductive metal-in-gap (MIG) or magnetoresistive (MR) heads.1)Due to various loss mechanisms, the signal-to-noise ratio of MIG heads peaks at around 100 kHz, decreasing rapidly at higher frequencies. The fabrication of both the MIG head2)as well as the accompanying signal processing circuitry3)is also non-trivial. MR heads provide higher SNR and signals that are independent of spatial frequency. They are however fragile, non-linear, and have a high temperature coefficient In cheques and bank notes, human-readable magnetic ink character recognition (MICR) characters are employed. Each MICR character has been designed to produce a distinct inductive head signal pattern. Unlike magnetic stripes, MICR characters signals are not binary when read using conventional read heads, resulting in increased read error rates. To avoid costly misreads, a closely spaced array of magnetic sensors can be utilized. Fabrication of read head arrays is, however, difficult in both technologies. A silicon magnetic sensor array fabricated using the charge-coupled device (CCD) technology has been designed to overcome these limitations. The magnetic sensor pixels are buried-channel MOSFET's with geometries designed to optimize magnetic sensitivity. The use of buried-channel, as opposed to surface-channel, MOSFET's results in enhanced sensitivity, lower noise, and higher signal resolution.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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