Current signal processing algorithms excel at impairment compensation when the parameters of optical fiber systems are precisely defined. However, their effectiveness diminishes considerably in the presence of imprecise parameters. By integrating ‘knowledge application from physics to neural networks (NN)’ with ‘information feedback from NNs to physics’, this paper proposes a physics-regulated digital backpropagation (PR-DBP) algorithm, which shows great promise for impairment compensation with imprecise fiber parameters. The PR-DBP employs an optimization-estimation-initialization loop structure. The optimization process provides the network’s adaptability by minimizing the loss value as in conventional NNs. The estimation process dynamically tracks physical parameters by extracting information from NNs to the physical domain. The initialization process offers a physics-based global control over the neural network, thereby mitigating the risk of overfitting by preventing an excessive focus on loss minimization. Moreover, a phase-shift weight function is applied to further improve algorithmic efficiency. Numerical analyses indicate that the PR-DBP significantly outperforms conventional methods in optical fiber systems with imprecise parameters, achieving a bit error rate reduction by an order of magnitude. As a mutually reinforcing part of impairment compensation, a high-accuracy and adaptive fiber parameter estimation is also demonstrated.

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

Nonlinearity is one of the key issues that hinder the development of high-capacity optical short reach systems. This paper proposes three variants of kernel affine projection (KAP) algorithms, all of which combine kernel mapping and affine projection in a reproducing kernel Hilbert space for compensating nonlinear impairments in optical short reach systems. An intensity modulation/direct detection system with a single digital-to-analogue converter, a packaged externally modulated laser and a packaged InP photo-detector is used for experimental demonstration, achieving 238-Gbps (net rate 222-Gbps). Experimental results show that the KAP algorithms can mitigate nonlinear impairments in short-reach communications while maintaining low complexity in reproducing kernel Hilbert space.