Atmospheric turbulence is a major challenge for practical orbital angular momentum (OAM)-based free-space optical (FSO) communication systems that causes intermodal crosstalk and degrades the performance of the system. Herein, we propose a hybrid input–output algorithm (HIOA)-based adaptive optics (AO) system to compensate for distorted OAM beams. The principle and parameters of the HIOA-based AO system in an OAM-based FSO system are analyzed, and the performance is discussed. The simulation results indicate that the HIOA-based AO system can effectively correct distorted OAM beams and that the HIOA improves the compensation performance and convergence speed compared to the traditional Gerchberg–Saxton algorithm. Moreover, we analyze the compensation performance based on different probe beams. Using an OAM beam with state $l=1$ as a probe beam can yield better correction effects than a Gaussian beam. This work verifies the feasibility of using an HIOA for adaptive turbulence compensation and provides new insights into OAM communication systems.

Recently, orbital angular momentum (OAM) beams have been applied in underwater optical communication (UWOC) to build a high-capacity communication link. However, a wave-front-sensitive OAM beam suffers significant distortion due to oceanic turbulence (OT), resulting in considerable intermodal crosstalk that degrades the UWOC performance. Herein, we propose and demonstrate an adaptive optics (AO)-based correction approach with a phase retrieval algorithm (PRA) to compensate for the distorted OAM beams induced by OT. In a simulation, an OT model with the random phase screen method is utilized. Two PRAs, the Gerchberg–Saxton algorithm (GSA) and the hybrid input–output algorithm (HIOA), are utilized to reconstruct the distorted phase-front of the OAM beam. The simulation results illustrate that the PRA-based AO approach can effectively compensate for the distorted OAM beam and improve the bit error rate performance in an oceanic channel. Additionally, the compensation performance of HIOA-based AO is superior to that of GSA-based AO in terms of convergence performance. This work verifies the feasibility and validity of a PRA-based AO approach in underwater turbulence optical communication and provides new insights into the OAM underwater communication system.

Atmospheric turbulence (AT) tends to impair the performance of free space optical (FSO) communication systems. Detecting AT strength is significant for turbulence effect mitigation, which can appropriately guide the selection of turbulence mitigation techniques and modulation formats. Orbital angular momentum (OAM) beam aberrations received through the turbulence channel are closely related to the turbulence strength. In this paper, we experimentally detect the AT strength using an OAM beam based on a convolutional neural network (CNN). We collect 8 kinds of superposed OAM beam intensity images after 5 levels of turbulence in the laboratory as datasets and test the AT detector performance with respect to the number of pixels, mode number of OAM beams, different AT sets and training set size. The results show that the AT detection accuracy is near 100% for 3 kinds of ATs, and the accuracy remains at approximately 85% for 5 kinds of ATs. In addition, using data augmentation methods or a hybrid dataset can improve the AT detection accuracy. The CNN-based method in this paper can help detect the AT strength in atmospheric channels and provide references for choosing appropriate techniques to mitigate turbulence effects and then enhance the OAM-FSO system performance.

Adaptive optics (AO) can efficiently compensate for turbulence-induced distortion in orbital angular momentum (OAM)-based optical wireless communication (OWC) systems. In this paper, we design a modified phase diversity algorithm (MPDA)-based wavefront sensor to enhance the reconstruction accuracy of distorted OAM wavefront information. Aiming to further strike a compelling trade-off between AO system complexity and compensation accuracy, we first construct a novel AO system that applies a quickly and electronically controlled focus-tunable lens (FTL). It decontaminates distorted OAM signaling beams while having a low systemic complexity and superior convergence performance. Furthermore, we propose the 3-modified phase diversity algorithm (3-MPDA) AO scheme relying upon a Fourier intensity and two defocused intensities as the prior information, which beneficially balances the compensation effect and the number of defocused intensities and exhibits good noise robustness against charge-coupled device (CCD) detectors. In summary, this paper provides new insight for designing AO schemes with high compensation performance in communication links.

Accurate staging of liver fibrosis (LF) is essential for clinical management in chronic liver disease. While non-contrast MRI (NC-MRI) yields valuable information for liver assessment, its effectiveness in predicting LF remains underexplored. This study aimed to develop and validate artificial intelligence (AI)-powered models utilizing NC-MRI for staging LF. A total of 1726 patients from Shengjing Hospital of China Medical University, registered between October 2003 and October 2022, were retrospectively collected, and divided into development (n = 1208) and internal test (n = 518) cohorts. An external test cohort consisting of 337 individuals from six centers, registered between June 2015 and November 2022, were also included. All participants underwent NC-MRI (T1-weighted imaging, T1WI; and T2-fat-suppressed imaging, T2FS) and liver biopsies. Two classification models (CMs), named T1 and T2FS, were trained on respective image types using 3D contextual transformer networks and evaluated on both test cohorts. Additionally, three CMs—Clinic, Image, and Fusion—were developed using clinical features, T1 and T2FS scores, and their integration via logistic regression. Classification effectiveness of CMs was assessed using the area under the receiver operating characteristic curve (AUC). A comparison was conducted between the optimal models (OMs) with highest AUC and other methods (transient elastography, five serum biomarkers, and six radiologists). Fusion models (i.e., OM) yielded the highest AUC among the CMs, achieving AUCs of 0.810 for significant fibrosis, 0.881 for advanced fibrosis, and 0.918 for cirrhosis in the internal test cohort, and 0.808, 0.868, and 0.925, respectively, in the external test cohort. The OMs demonstrated superior performance in AUC, significantly surpassing transient elastography (only for staging ≥ F2 and ≥ F3 grades), serum biomarkers, and three junior radiologists for staging LF. Radiologists, with the aid of the OMs, can achieve a higher AUC in LF assessment. AI-powered models utilizing NC-MRI, including T1WI and T2FS, accurately stage LF. 10.13039/501100001809 National Natural Science Foundation of China (No. 82071885); General Program of the Liaoning Provincial Department of Education (LJKMZ20221160); Liaoning Province Science and Technology Joint Plan (2023JH2/101700127); the Leading Young Talent Program of Xingliao Yingcai in Liaoning Province (XLYC2203037).

Adaptive optics (AO) has the potential to mitigate the effect of atmospheric turbulence and improve the performance of orbital angular momentum (OAM)-based optical wireless communication (OAM-OWC) links. Here, we propose a single-intensity-measurement phase retrieval algorithm (SPRA)-based AO technique of compensating for the distortion of the OAM beam. The only parameter required by the SPRA wave-front sensor is the intensity of the probe beam in the Fourier domain, which substantially simplifies the AO system. We first derive an analytical expression to characterize the expansion of probe beam in OAM-OWC links and then determine the diameter constraints as the apriori information of the SPRA required for guaranteeing a certain compensation performance. The simulation results illustrate that the SPRA-AO approach can indeed correct a distorted OAM beam both in a single-channel scenario and in multiplexed OAM-OWC systems. The bit error rate can be improved by orders of magnitude with the aid of SPRA-AO compensation. Furthermore, we establish noise models of AO-based OAM-OWC systems and analyze the robustness of the SPRA-AO technique. In a nutshell, this paper provides new insights for the applications of AO and forms the theoretical basis of employing probe beams in OAM-OWC systems.