Crack detection is essential for structural safety inspection but remains challenging due to noise, illumination variations, and complex backgrounds. In this paper, we propose CrackNet, a segmentation network specifically designed for concrete crack ... read more
Transparent ultrasonic transducer enables compact coaxial photoacoustic microscopy, but the trade-off between optical transparency and sensitivity and electrode nonuniformity results in reduced SNR and resolution. Here, we propose a deep learning-bas... read more
Diffractive deep neural network (D2NN), as a novel optical machine learning framework, has attracted much attention due to its superior advantages in inference speed and power consumption. Here, we demonstrate a new, to the best of our knowledge, fra... read more
Activation functions are indispensable in optical neural networks (ONNs). However, existing solutions often require high optical power for nonlinearity or suffer from limited tunability that restricts deployable architectures. Achieving diverse and t... read more
On-chip photonic computing shows promise for tasks such as neural networks due to its parallelism and low latency. However, the impact of the nonlinear transfer curve of photonic modulators on the accuracy of photonic computing is underexplored. Curr... read more
Refractive index sensing traditionally relies on high-Q resonances in precisely fabricated metastructures, making performance vulnerable to fabrication imperfections, limited spectral resolution, and environmental instability. Here, we introduce a fu... read more
In coherent optical communication across turbulent atmospheric channels, reference beacons can be multiplexed with information-encoded signals during transmission. In this case, it is commonly assumed that the wavefront distortion of the two is equiv... read more
Longitudinal particle manipulation by beams relies on the guidance of particle motion through propagation trajectory design. However, a single trajectory cannot satisfy the requirements for longitudinal particle manipulation in different directions, ... read more
Diffractive neural networks are a promising framework for all-optical processing of visual data, with the potential to drastically reduce the computational burden and energy consumption that is currently associated with running neural networks on dig... read more
BACKGROUND: Accurate tumor node metastasis (TNM) staging is fundamental for treatment planning and prognosis in non-small cell lung cancer (NSCLC). However, its complexity poses significant challenges. Traditional rule-based natural language processi... read more
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