As a semi-active control device, the magnetorheological (MR) damper has received much praise for its great controllability, quick response, and lower input power. However, the problem of liquid leakage arises after its long-term service, severely affecting its performance and service life. To solve this problem, this paper proposes fully vulcanizing and consolidating viscoelastic material, the cylinder barrel and the piston rod, which replaces the traditional dynamic seal with a static seal, and developing a new leak-proof MR damper. Considering that viscoelastic material has a certain energy dissipation and fluid-structure interaction with MR fluid, the correction factor of the pressure gradient is introduced to establish the mechanical model of the leak-proof MR damper which is to be tested. The testing result shows that warpage deformation of viscoelastic material weakens the damping force of the MR damper and the weakening effect increases with current intensity. Given the influence of current intensity on the correction factor, the complete mechanical model of the leak-proof MR damper is obtained on the basis of the verified model. Through the comparison between the theoretical calculation curve and the test curve, the revised mechanical model is found to well reflect the mechanical properties of the leak-proof MR damper.

Fiber-bundle endomicroscopy has several recognized drawbacks, the most prominent being the honeycomb effect. We developed a multi-frame super-resolution algorithm exploiting bundle rotation to extract features and reconstruct underlying tissue. Simulated data was used with rotated fiber-bundle masks to create multi-frame stacks to train the model. Super-resolved images are numerically analyzed, which demonstrates that the algorithm can restore images with high quality. The mean structural similarity index measurement (SSIM) improved by a factor of 1.97 compared with linear interpolation. The model was trained using images taken from a single prostate slide, 1343 images were used for training, 336 for validation, and 420 for testing. The model had no prior information about the test images, adding to the robustness of the system. Image reconstruction was completed in 0.03 s for 256 × 256 images indicating future real-time performance is within reach. The combination of fiber bundle rotation and multi-frame image enhancement through machine learning has not been utilized before in an experimental setting nut could provide a much-needed improvement to image resolution in practice.

With the advantages of long-time operation and stable power, fiber lasers have been widely used in the industrial field, and show great potential in laser-induced breakdown spectroscopy (LIBS). However, the...

Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed image reconstruction U-Net (IRU-Net) outperforms the state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59, while the OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%).

Optical coherence tomography angiography (OCTA) has successfully demonstrated its viability for clinical applications in dermatology. Due to the high optical scattering property of skin, extracting high‐quality OCTA images from skin tissues requires at least six‐repeated scans. While the motion artifacts from the patient and the free hand‐held probe can lead to a low‐quality OCTA image. Our deep‐learning‐based scan pipeline enables fast and high‐quality OCTA imaging with 0.3‐s data acquisition. We utilize a fast scanning protocol with a 60 μm/pixel spatial interval rate and introduce angiography‐reconstruction‐transformer (ART) for 4× super‐resolution of low transverse resolution OCTA images. The ART outperforms state‐of‐the‐art networks in OCTA image super‐resolution and provides a lighter network size. ART can restore microvessels while reducing the processing time by 85%, and maintaining improvements in structural similarity and peak‐signal‐to‐noise ratio. This study represents that ART can achieve fast and flexible skin OCTA imaging while maintaining image quality.