UAV technology has developed rapidly in recent years, Images extracted by UAV are widely used in urban division, crop classification, land monitoring etc. However, there are problems in UAV image segmentation such as image category imbalance, object scale variation, and insufficient utilization of contextual information, etc. To address the above problems, this paper uses optimized deeplabv3+ network model, and cross-entropy loss function for balancing the dataset samples in the experimental process for image semantic segmentation research. The results show that the algorithm of this paper has a high accuracy rate for semantic segmentation of UAV images, and can recognize each category of UAV images better, and the segmentation effect is better.
Surface Electromyographic(sEMG) is a kind of complex bioelectrical signal, and it is very important to select an appropriate feature extraction method. In this paper, the time domain analysis method, frequency domain analysis method and time-frequency domain analysis method are compared through experimental data, and the results of feature extraction of time-frequency domain analysis method are more representative, higher separation degree, and greatly reduce the one-sidedness of feature extraction. Finally, the energy eigenvalue of wavelet packet coefficient in timefrequency domain method is selected as the feature vector of signal pattern recognition to provide theoretical basis for real-time and accuracy of multi-motion pattern recognition.
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