RESUMEN
In this paper, we demonstrate a holographic polymer-stabilized blue-phase liquid crystal grating fabricated using a visible laser. As blue phase is stabilized by the interfered light, polymer-concentration gradient is achieved simultaneously. With the application of a uniform vertical electric field, periodic index distribution is obtained due to polymer-concentration gradient. The grating exhibits several attractive features such as polarization-independency, a broad temperature range, sub-millisecond response, simple fabrication, and low cost, thus holding great potential for photonics applications.
RESUMEN
Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost.