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In order to solve the problem of high communication error rate caused by inter symbol interference in high-speed visible light communication signal blind equalization algorithm, a high-speed visible light communication signal blind equalization algorithm is designed based on machine learning. The visible light communication channel model is constructed, and a fusion length limited coding mode is set to make the signal transmitted by visible light communication more in line with the overall response of the channel. The decision feedback equalizer is selected as the equalizer, and the channel cascade impulse response condition is set to compensate the distorted output of the channel. The blind equalization algorithm is designed to classify the data by unsupervised learning, and the extreme point of the error function is found through iterative calculation to optimize the equalizer. The simulation results show that under the same SNR test conditions, the bit error rate of the proposed algorithm is significantly lower than that of the blind equalization algorithm based on LMS and BP neural network. Under the condition of SNR = 20dB, the bit error rate is significantly reduced to the order of 10-5, with high communication quality. The VLC system uses an LED backlight display with a CMOS camera. In general, this panel is used to display marketing information. By adjusting the brightness of the CMOS camera's rolling shutter effect (RSE), additional data, such as dynamic content, may be wirelessly sent. NR (noise to signal) ratio of VLC can't keep up with the variety of material being shown on the screen. It is possible to significantly enhance the CMOS RSE pattern decomposition by using a grayscale value distribution (GVD) and a machine learning method (MLA).

This study explores the relationship between adolescents' perceptions of epidemic risk and their emotions through three follow-up surveys during the early stages of the COVID-19 pandemic on February 11th (T1), 18th (T2), and 25th (T3), 2020. Three hundred and four adolescents in different academic stages (junior high middle school, senior high middle school, and university) participated in the online survey, and cross-lag analysis was used to examine the causal relationship between epidemic risk perceptions and positive and negative emotions. The results found that the individual's positive emotions were significantly higher than the negative emotions in T1, T2 and T3. Cross-lag analysis found that for positive emotions, T2 positive emotions could negatively predict T3 epidemic risk perceptions, and T2 epidemic risk perceptions could negatively predict the individual's T3 positive emotions. For negative emotions, risk perceptions at T1 could positively predict negative emotions at T2, and at the same time, negative emotions at T1 could also positively predict epidemic risk perceptions at T2. This indicates that during the early stages of the COVID-19 pandemic, there was a causal relationship between the perceptions of epidemic risk and the emotions of adolescents, and this relationship had high stability among groups of different genders and academic stages.