Danmaku data from an online course contains implicit information about the students, the teacher, and the course itself. To discover the information, we design a behavior-sentiment-topic mining procedure, and apply it on the danmaku from two electronics courses on Bilibili, a popular video sharing platform in China. The procedure enables us to obtain behavior patterns, text sentiments, and hidden topics, of those danmaku comments effectively. Results show similarities and differences between the danmaku from Fundamentals of Analog Electronics and that from Fundamentals of Digital Electronics. Some interesting observations are given according to the results. For example, students tend to experience an emotional upsurge right before the end of a course, which is due to their fulfilment for completing the course. Based on the observations, we make some suggestions for students, teachers, and platforms on how to improve the learning outcomes using the results of danmaku analysis.

Accurate recognition of orbital angular momentum (OAM) modes is a major challenge for OAM-based optical communications over atmospheric turbulence channels. The turbulence-induced distortions cause difficulties for the receiver to distinguish between adjacent OAM modes. Deep learning, such as convolutional neural networks (CNN), has been a promising technique in solving this problem. To improve the recognition performance, we propose a vortex modulation method that can magnify the subtle differences between closely adjacent OAM states. This allows the CNN to capture the image features more effectively and to recognize the topological charges more accurately. Numerical results show high recognition accuracy for both integer topological charges and fractional ones even under strong turbulence intensity and long propagation distance, which demonstrate the utility of the proposed method.