UltraLow Noise, High Bandwidth, Low Latency, No Overload 6-Axis Optical Force Sensor
Bibliographic record
Abstract
In this article, we present a novel six-axis optical sensor that employs pairs of light-emitting diodes (LEDs) and bicell photodetectors, and corresponding slits that modulate the projected LED light onto the photodetectors in response to external forces. The sensor can be clamped on and off a structure and relies upon the compliance of the structure for force estimation; it has no flexible components, and therefore, it is robust to overload. The mechatronic design features low noise, wide dynamic range opto-electronics, and signal conditioning, coupled with colocated digital electronics based on a FPGA that samples all sensing channels synchronously, enabling very low noise displacement sensing with resolution of 1.62 nm, low measurement signal latency of 100$\mu$s, high measurement bandwidth of 500 Hz, and high data transfer rates in excess of 11.5 kHz for transmission of six axis transducer data to a host computer. The transducer's resolution is better than 0.0001% of the full-scale. A sensor model has been derived and can be used to explore design tradeoffs. A calibration approach based on an external reference sensor and an approach to temperature compensation are presented and validated. A video is attached.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".