Cheng-Yang Liu

Accurate three-dimensional (3D) fingerprint imaging is essential for next-generation biometric systems requiring high spatial resolution and microfeature fidelity. This study presents a novel, rapid, non-contact 3D fingerprint imaging system that integrates a seven-step phase-shifting fringe projection technique with a microscope-based optical configuration and structured RGB illumination. The system achieves lateral resolution better than 40 μm and vertical resolution below 5 μm, enabling precise reconstruction of ridge-valley structures and third-level features, such as sweat pores and ridge-edge contours. A comparative analysis of illumination wavelengths shows that green light provides the highest measurement accuracy, yielding a wrapped-phase standard deviation of 0.07 rad, a signal-to-noise ratio (SNR) exceeding 25 dB, and a root mean square error of 1.8 pixels in the unwrapped phase. Surface profiling results confirm the system’s capability to resolve ridge heights in the range of 45–65 μm and pore diameters between 80–150 μm. Compared to existing techniques, the proposed method offers enhanced spatial resolution, improved preservation of fine surface features, and a novel assessment of spectral effects on 3D reconstruction performance. With an acquisition time of less than one second, this system demonstrates a strong potential for applications in high-security biometric authentication, forensic investigation, and spoof-resistant technologies. Future research will focus on enabling real-time data processing, system miniaturization, and improved performance under dynamic or uncontrolled environmental conditions.

Rapid optical differentiation of bacterial species remains challenging in turbid biological media due to strong scattering and absorption. Here, we present a physics‐informed, label‐free biophotonic framework for bacterial identification based on refractive index (RI) spectral signatures. An opto‐microfluidic PDMS chip integrating air mirrors, microlenses, and fiber‐optic probes was developed to measure transmission spectra from eight clinically relevant ESKAPEE pathogens. To address the inverse problem of optical parameter retrieval, a hybrid modeling approach combining Monte Carlo photon transport, Mie scattering theory, and a genetic algorithm was implemented to reconstruct wavelength‐dependent RI spectra. The reconstructed spectra reveal clear interspecies differences, with higher RI values for Escherichia coli and Klebsiella pneumoniae and lower values for Staphylococcus aureus . These physics‐derived RI features were subsequently used for classification using a one‐dimensional convolutional neural network, achieving 97.75% accuracy. This approach demonstrates the potential of RI spectral fingerprints for label‐free microbial identification and on‐chip biophotonic diagnostics.

A micro-scale fiber-optic sensor based on the Fabry-Pérot interferometric principle is proposed and experimentally demonstrated. The device comprises a single-mode optical fiber, a hollow-core capillary, and an ultrathin metallic diaphragm, forming a compact sensor structure with advantages of low fabrication cost, simple manufacturing, and ultra-small dimensions. The dual-parameter sensing strategy exploits time-scale separation and complementary demodulation domains, using intensity-based demodulation at a fixed quadrature point to capture fast acoustic-induced phase perturbations, while temperature sensing is achieved by tracking the slow spectral drift of the interference fringes caused by thermal expansion and thermo-optic effects. Comprehensive multiphysics experiments were conducted to evaluate both acoustic and thermal sensing capabilities, with emphasis on the effect of diaphragm thickness on overall performance. Acoustic characterization revealed a wide frequency response from 10 Hz to 20 kHz, a maximum signal-to-noise ratio of 46.1 dB, a minimum total harmonic distortion of 0.051%, and a minimum detectable pressure of 12.9 μPa/√Hz at 1 kHz. Thermal measurements confirmed reliable operation over 50 °C - 300 °C, achieving a sensitivity of 13.3 pm/°C with excellent linearity (R² > 0.99). These results demonstrate that the proposed Fabry-Pérot fiber-optic sensor provides high-performance and cost-effective solutions for applications in acoustic detection, temperature monitoring, and wearable sensing technologies.

Two-dimensionally periodic Janus structures possess identical structural periods along the x- and y-directions while exhibiting asymmetric contour profiles within each unit cell. This unique combination of periodic isotropy and contour anisotropy enables unusual optical responses that cannot be achieved by conventional periodic structures or randomly rough surfaces. However, systematic experimental investigations of their anisotropic diffraction behavior remain limited due to fabrication challenges and the difficulty of measuring spatial scattering distributions. In this study, a scalable fabrication process is developed to produce square-centimeter-scale two-dimensionally periodic Janus microstructures on silicon wafers. A custom-built high-resolution scatterometer is employed to measure diffraction efficiency and anisotropic energy distributions under various incident conditions, including different azimuth angles, zenith angles, and polarization states. The results reveal pronounced anisotropic diffraction behavior and strong sensitivity of diffraction efficiency to both incident polarization and azimuth angle. These findings demonstrate that two-dimensionally periodic Janus structures provide an effective platform for tailoring directional optical responses and may enable advanced optical components for sensing and beam-control applications.

The viscosity coefficient is a fundamental parameter in fluid dynamics and soft matter physics; however, conventional measurement techniques, including falling-ball, capillary flow, and vibrational methods, generally require large, homogeneous samples and are incompatible with microscale or optically scattering systems. Here, we report a single-fiber optical tweezers platform that utilizes a microcone-shaped fiber probe for stable, non-contact manipulation of microscale particles and quantitative viscosity probing. The tapered fiber tip, fabricated via controlled chemical etching and optimized through numerical simulations, promotes photonic nanojet formation at the fiber output, resulting in enhanced optical gradient forces and robust single-beam trapping. Experimental measurements demonstrate efficient trapping of 6 μm polystyrene microspheres, achieving a maximum trapping force of 24.9 pN and a trapping efficiency of 46.7%, in excellent agreement with theoretical predictions, thereby validating the underlying optical force model. In addition, the same platform was employed for in situ viscosity extraction from glycerol–water mixtures and whole blood. The measured values deviate by less than 4% from commercial viscometers across a broad dynamic range extending to 6.12 mPa s. These results demonstrate that nanojet-enhanced optical forces provide a compact, energy-efficient route for probing fundamental fluid properties at the microscale, while offering insight into light–matter interactions in complex fluidic environments.

Objective: Pediatric scoliosis is the most prevalent spinal disorder, often leading to abnormal curvature and deformation of the spine. Early detection is essential for timely intervention, particularly in growing adolescents. In this study, we present a novel, fully automated, radiation-free method for Cobb angle evaluation, combining fringe projection profilometry with deep learning technologies. Materials and methods: A three-dimensional reconstruction of the participant’s back surface is achieved using a seven-step phase-shifting algorithm. Convolutional neural networks are then utilized to extract asymmetry features from the 3D surface and predict the Cobb angle, a key clinical indicator of scoliosis severity. A total of 48 participants clinically diagnosed with scoliosis based on radiographic imaging were recruited from the hospital. Results: The experimental results demonstrate a strong correlation between the predicted and actual Cobb angles, with a correlation coefficient of 0.94 and a coefficient of determination of 0.8796 during Adam’s forward bend test. The mean time required from scanning to Cobb angle prediction is approximately 3.3 s. Conclusions: The proposed evaluation method exhibits excellent discriminative capability and shows significant potential as an alternative to the traditional scoliometer for large-scale Cobb angle screening programs in schools.

Purpose: This study aims to address the limitations of radiographic imaging and single-task learning models in adolescent idiopathic scoliosis assessment by developing a noninvasive, radiation-free diagnostic framework. Methods: A multi-task deep learning model was trained using structured back surface data acquired via fringe projection three-dimensional imaging. The model was designed to simultaneously predict the Cobb angle, curve type (thoracic, lumbar, mixed, none), and curve direction (left, right, none) by learning shared morphological features. Results: The multi-task model achieved a mean absolute error (MAE) of 2.9° and a root mean square error (RMSE) of 6.9° for Cobb angle prediction, outperforming the single-task baseline (5.4° MAE, 12.5° RMSE). It showed strong correlation with radiographic measurements (R = 0.96, R² = 0.91). For curve classification, it reached 89% sensitivity in lumbar and mixed types, and 80% and 75% sensitivity for right and left directions, respectively, with an 87% positive predictive value for right-sided curves. Conclusion: The proposed multi-task learning model demonstrates that jointly learning related clinical tasks allows for the extraction of more robust and clinically meaningful geometric features from surface data. It outperforms traditional single-task approaches in both accuracy and stability. This framework provides a safe, efficient, and non-invasive alternative to X-ray–based scoliosis assessment and has the potential to support real-time screening and long-term monitoring of adolescent idiopathic scoliosis in clinical practice.