In order to solve the problem of insufficient samples of near-shore synthetic aperture radar data in ship detection, a ship synthetic aperture radar (SAR) image data augmentation model based on generative adversarial network was designed in this study. Specifically, this study combines image fusion and data enhancement to design an Image Fusion Concurrent-Single-Image-GAN model (IF-SinConGAN).This model first fuses offshore ship images with nearshore scenes, employing a dual-threshold sea-land segmentation method to seamlessly integrate offshore ships into nearshore water regions. These fused images are then used as input for training the ConSinGAN model. Compared to the original model, IF-SinConGAN significantly improves both the diversity and quality of generated SAR images.
Brain-computer interface technology (BCI) enables users to directly control external devices by establishing an information transmission path between the brain and external devices. Brain-computer interfaces based on the motor imagination paradigm have also begun to enter various fields. Therefore, the research on the brain-computer interface encoding and decoding algorithm of the motor imagination paradigm is particularly important. This paper proposes a model based on attention mechanism CBAM and EEGNet to classify motor imagination electroencephalogram signals (MI-EEG), and verified it on a public data set. Compared with a single EEGNet model, it improved by 3.7%, which is 8.1% higher than the traditional FBCSP model. The experimental results show the effectiveness of the new CBAM-EEGNet model on the four classification tasks of motor imagery.
Acupuncture and moxibustion act on acupoint areas with different frequencies, evoking a large number of responding activity of neurons to achieve the purpose of regulating human body functions. In the process of acupuncture, different frequencies of acupuncture evoked different neuronal spiking activity. In order to study the mechanism of acupuncture with different frequencies, Bayesian statistical model is used to optimize the results of the traditional classification algorithm based on spiking waveforms, which greatly reduces the missed detection rate of acupuncture responding activity. Then, the spiking events evoked by acupuncture at different frequencies were statistically analyzed, and the results showed that the number of neuronal spikes gradually increased with the increase of frequency. However, when the stimulation was increased to 120 times/min, the increase in the stimulation frequency will not evoke more spikes due to the saturation of frequency adaptation of the neurons. Finally, a probabilistic statistical model was used to encode the neuronal responding activity evoked by different acupuncture, and the maximum likelihood estimation method was used to fit the model parameters. The results show that the coupling parameters of stimulus are significantly smaller than the coupling parameters of spike-history, and the more the historical spikes, the smaller the coupling parameters of stimulus. This suggests that since acupuncture is a low-frequency mechanical stimulation, a large number of historical spikes in the spiking activity are the main factors that evoke the neuronal response. Thus, revealing the responding mechanism of different acupuncture frequencies.
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