Bibliographic record
Abstract
Ultrasound imaging is a widely used noninvasive imaging technique for biomedical and other applications. Piezoelectric devices are commonly used for the generation and detection of ultrasound in these applications. However, implementation of two-dimensional arrays of piezoelectric transducers for 3D ultrasound imaging is complex and expensive. Optical Fabry-Perot interferometry is an attractive alternative to the piezoelectric devices for detection of ultrasound. In this method a thin film etalon is constructed and used. Light reflected from the two surfaces of this thin film produces an intensity which depends on the film thickness. When ultrasound is incident on the film, it changes the thickness of the film and consequently modulates the light intensity on the film. In our work, we made two types of etalon (Finesse 2) for our experiment. We detected lower frequency ultrasound (0.5 MHz or 1 MHz) using the build etalon. We determined a linear relationship between the strength of the optical signals and the exerted pressure on a film by the ultrasound. The dependence of the etalon performance on the light wavelength was demonstrated indirectly by measuring the signal at various light incidence angle. Simulation results are also presented. Lastly, we proposed the optimum design of this detection system based on the simulation results. This method of ultrasound detection can be a potential low-cost approach for 3D ultrasound imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".