Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Optical ultrasound detection enables greater miniaturization than conventional piezoelectric transducers while preserving high sensitivity. Although sub-micron silicon photonics detectors have been demonstrated, image artifacts caused by surface acoustic waves interference remain a key challenge. Polymer detectors offer better acoustic coupling, yet they have been limited to tens of micrometers in size because of optical confinement requirements. Here we overcome that limit with the smallest polymer resonator built on an optical fiber, using a 6 μm thick polymer cavity on a tapered single mode fiber tip. The detector achieved a bandwidth of about 150 MHz and a noise equivalent pressure density of about 1.5 mPa.Hz-1/2. Imaging experiments yielded 7 μm axial and 17 μm lateral resolution, with high fidelity performance that surpassed piezoelectric and state of the art optical detectors. This combination of broad bandwidth, artifact free imaging, and manufacturability makes the detector ideal for optoacoustic mesoscopy (OptAM) applications....
Objectives: This study aimed to develop a method for extracting acoustic features to assess left anterior descending artery (LAD) stenosis severity. Methods: Heart sound data were collected from 75 participants (10 diastoles per participant) using a high- signal- to- noise ratio micro- electro- mechanical systems stethoscope. The diastolic signals were preprocessed, and empirical wavelet transform was applied to decompose their power spectra into three modes (0–150, 150–500, and > 500 Hz). The spectral energies (e(1), e(2), e(3)) of these modes were analyzed, and support vector machine (SVM) and extreme gradient boosting (XGBoost) machine learning algorithms were used to classify LAD stenosis into mild (< 50%), moderate (50%–75%), and severe (> 75%). Results: Spectral energies e(2) and e(3) significantly increased with stenosis severity, and XGBoost outperformed SVM, achieving a test accuracy of 0.8133 and areas under the curve of 0.9358, 0.9644, and 0.9580 for mild, moderate, and severe stenosis, respectively. Conclusion: Empirical wavelet transform- extracted spectral energies of e(2) and e(3), combined with XGBoost, effectively determine LAD stenosis degree, offering a non- invasive screening tool....
We present a long-lasting, multilayered tissue mimicking phantom model that mimics the optical and acoustic properties of the skin and radial artery at the wrist. The silicone-based phantom is fabricated with tunable properties by varying concentrations of India ink, titanium dioxide, and silicone oil. To assess the longevity of the phantom material, the mechanical, optical, and acoustic properties of the individual layers are characterized over multiple weeks. Comparisons of characterization measurements to physiological reference values demonstrate that the phantom material closely approximates the mechanical properties, while optical properties are also comparable, especially from 700 to 1100 nm. Acoustic properties are less optimally matched, with higher attenuation and lower acoustic velocities than biological tissue. The phantom material exhibits optical and acoustic stability during characterization of the properties over time. A cost analysis of the fabrication technique demonstrates that this is a low-cost and easily accessible phantom model, which could be implemented at any laboratory. This phantom serves as a validation tool for new optical and acoustic sensors in the first stages of translational research and prototype development. We present a verification of the stability of silicone-based phantom materials over time, thus enabling long-term optical and acoustic measurements....
This paper presents a surrogate-based uncertainty quantification (UQ) framework for coupled structural–acoustic systems subject to material and geometric variability. The proposed methodology integrates the Finite Element Method (FEM) with two metamodeling techniques—the Quadratic Response Surface (QRS) and Kriging—and Monte Carlo Simulations (MCS), to efficiently characterize the probabilistic behavior of the acoustic response. Two accuracy metrics (cross-validation error and prediction error) are used to validate the surrogate models. Numerical experiments demonstrate that the Kriging metamodel trained with 30 Latin Hypercube Sampling (LHS) points achieves superior predictive accuracy, with a Relative Maximum Error of 4.125 × 10−7. Monte Carlo Simulations conducted via the Kriging surrogate reduce the computational cost by more than six orders of magnitude compared to direct FEM-based MCS, while maintaining high accuracy. The proposed framework is validated on a rectangular cavity coupled with two flexible aluminum plates, and provides an efficient and accurate tool for vibro-acoustic UQ in complex engineering systems....
Impact-based acoustic inspection provides a rapid non-destructive approach for screening metallic components by analyzing the sound radiated after a controlled mechanical excitation. However, the limited availability of labeled data from defective parts remains a major challenge for deploying deep learning classifiers in production. This paper proposes a complete pipeline that converts raw impact-response audio recordings into magnitude log-spectrogram images and trains a semi-supervised Wasserstein GAN with gradient penalty (SS-WGAN-GP) designed to operate under extreme data scarcity. The architecture couples a shared convolutional backbone with two output heads: aWasserstein critic for unsupervised discrimination between real and generated samples, and a binary classification head for supervised quality labeling, jointly optimized through a combined loss that balancesWasserstein distance, gradient penalty, and cross-entropy. A key property of the design is that the generator acts as a source of synthetic training samples, producing progressively more realistic spectrograms as training advances. These samples, in turn, enrich the feature representations learned by the shared backbone and improve the performance of the classification head. The classification head of the trained discriminator is deployed directly as the quality classifier, without requiring external data or post hoc retraining....
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