The first distributed-mass high-performance programmable optoelectromechanical steerable motion-wave sensors focused on sophisticated biomedical applications
Bibliographic record
Abstract
Abstract This paper introduces the first high-performance distributed-mass acoustic sensor made of cascaded differential phase shift suspended slot waveguide sections in a Mach–Zehnder interferometer optical transducer circuit. The heavyweight seismic mass used in traditional optoelectromechanical sensors is replaced by an fg lightweight coupling arm yielding an extra compact fast responding structure enabling utilizing over $$64$$ 64 cascaded sections and resulting in enhancing the performance by hundreds of times. The transducer operation relies on converting the acoustic vibration into phase modulation of the light for a splendid performance. The novel sensor architecture challenges achieving optical sensitivities higher than $$33\times {10}^{3} \%/\mathrm{g}$$ 33 × 10 3 % / g (33 times supersensitive), operating at ultrasonic acoustic speeds higher than 27 MHz, and recognizing resolutions in the 1 ng order. The programmable sensor is voltage-controlled supporting the operation in multimodes. Four operation modes are elucidated including the natural aspiration force, turbo electrostatic force open-loop voltage control, zero-force closed-loop voltage control, and dynamic turbo-force closed-loop voltage control. Wide dynamic ranges for controlling the optical sensitivity and maximum measurable acceleration up to 115.5 dB are reported. Steering capabilities of the acoustic beam in the azimuth plane are demonstrated utilizing two-spoke and three-spoke directional architectures supporting $$116.64^\circ$$ 116 . 64 ∘ and $$360^\circ$$ 360 ∘ of respective steering angles. Potential acoustic biosignal-based applications in the medical field are outlined.
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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 | 0.001 |
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".