Development of Robust Sensing System for Puncture Needle-Type Ultrasonography
Bibliographic record
Abstract
In this paper, we propose a robust sensing system that protects a thin rod sensor used in the measurements of acoustic impedance in puncture needle-type ultrasonography. In this ultrasonograpy, an ultrasonic interference method using the thin rod sensor is applied. Since the thin rod made of fused quartz is not robust, a protector is required for the thin rod sensor in in vivo measurement. Therefore, the sensing system has consisted of a thin rod sensor and a hollow pipe with a top cover to protect the sensor. For the observation of a low-impedance material such as biological tissue, the measurement method requires an impedance-transforming layer as the top cover. However, since the optimum thickness of the layer decreases as the measurement frequency increases, the decrease in the thickness causes deformation of the layer. The deformation introduces an error in the measurement. To avoid such a problem, we developed a robust sensing system. The system consists of a quarter-wavelength layer that functions as an impedance transformer and a half-wavelength (or multiple times of a half-wavelength) layer that provides the robustness of the system. We confirmed experimentally the effectiveness of the robust sensing system for acoustic impedance measurement of a tissue sample by the acoustic impedance difference method. The experimental results show that the robust sensing system is useful for puncture needle-type ultrasonography.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".