Universal analyzer for measuring the orbital angular momentum spectrum of a randomly fluctuated beam.

The orbital angular momentum (OAM) of beams provides an additional degree of freedom and has been applied in various scientific and technological fields. Accurate and quantitative measurement of intensity distributions across different OAM modes, referred to as the OAM spectrum of a beam, is crucial. Here, we propose a straightforward and efficient experimental setup for measuring the OAM spectrum of a randomly fluctuating beam.

Quantitative determination of fractional topological charge based on the rotational Doppler effect.

The utilization of fractional-order vortex beams extends the diversity of optical field manipulation, permits for more flexible control over beam propagation, and provides novel applications in optical communications, edge enhancement imaging, and particle manipulation. However, compared with the integer-order vortex beams, the topological charge measurement techniques for fractional-order vortex beams are not well developed, impeding the further exploration of its applications. In this paper, the frequency signal of rotational Doppler effect and corresponding broadening behavior under the fractional-order vortex beam illumination were analyzed.

Coherence singularity and evolution of partially coherent Bessel-Gaussian vortex beams.

For a partially coherent Bessel-Gaussian (PCBG) vortex beam, information regarding the topological charge (TC) is hidden in the phase of the cross-spectral density (CSD) function. We theoretically and experimentally confirmed that during free-space propagation, the number of coherence singularities is equal to the magnitude of the TC. In contrast to the Laguerre-Gaussian vortex beam, this quantitative relationship only holds for the case with an off-axis reference point for the PCBG vortex beam.

Two-Branch network for brain tumor segmentation using attention mechanism and super-resolution reconstruction.

Accurate segmentation of brain tumor plays an important role in MRI diagnosis and treatment monitoring of brain tumor. However, the degree of lesions in each patient's brain tumor region is usually inconsistent, with large structural differences, and brain tumor MR images are characterized by low contrast and blur, current deep learning algorithms often cannot achieve accurate segmentation. To address this problem, we propose a novel end-to-end brain tumor segmentation algorithm by integrating the improved 3D U-Net network and super-resolution image reconstruction into one framework.