Micro-Structured and Telecom Fibers for Acoustic and Ultrasound Sensing and Imaging
Bibliographic record
Abstract
Ultrasound and acoustic sensors have been widely used in medical imaging, structural health monitoring (SHM) and non-destructive testing (NDT) in civil/mechanical structures. Detection of high frequency acoustic wave (tens of MHz to 1 GHz) enables high spatial resolution imaging, the acoustic wave can be generated by surface acoustic waves-electromechanical systems (MEMS) with high signal-to noise-ratio (SNR) are difficult, as the conversion from mechanical wave to electrical signal is low due to the larger dimension of the electrical device compared to the spatial period of the acoustic wave. This difficulty can be overcome by coupling acoustic/mechanical to optical wave within one SAW wavelength in a few micrometers via optomechanical interaction. The sensing signal is measured in optical domain at high sampling rate, instead of electrical domain as micro-wave. Novel micro-structured optical fiber from conventional SiO2to As2Se3tapered fiber have brought major advancements in high frequency and high sensitivity detection by SAW generation and detection. This tutorial paper discusses basic principles of sound waves, acoustic waves and ultrasound, the photo-acoustic effect, the effect of electrical-strain and vice-versa. The acoustic generation by PZT, mechanical wave via a pencil break, laser ultrasound generation, and the SAW can be detected by micro-structured fibers and telecom fibers from kHz to 1 GHz are demonstrated with SNR of greater than 40 dB at ∼100 MHz and greater than 20 dB at ∼1 GHz. This sensing technology opens a new on chip sensing platform that combines electrical, mechanical and optical components on one chip for acoustic-optical sensing and imaging probe with high spatial resolution.
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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) | 0.004 | 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".