In this paper an automated data labeling (ADL) neural network was proposed to streamline dataset collecting for real-time predicting the continuous motion of hand and wrist, these gestures are only decoded from a surface electromyography (sEMG) array of eight channels. Unlike collecting both the bio-signals and hand motion signals as samples and labels in supervised learning, this algorithm only collects the unlabeled sEMG into an unsupervised neural network, in which the hand motion labels are auto-generated. The coefficient of determination (r2) for three DOFs, i.e. wrist flex/extension, wrist pro/supination, hand open/close, was 0.86, 0.89 and 0.87 respectively. The comparison between real motion labels and auto-generated labels shows that the latter has earlier response than former. The results of Fitts' law test indicate that ADL has capability of controlling multi-DOFs simultaneously even though the training set only contains sEMG data from single DOF gesture. Moreover, no more hand motion measurement needed which greatly helps upper-limb amputee imagine the gesture of residual limb to control a dexterous prosthesis.

Several transformer-based methods for change detection (CD) in remote sensing images have been proposed, with Siamese-based methods showing promising results due to their two-stream feature extraction structure. However, these methods ignore the potential of the cross-attention mechanism to improve change feature discrimination and thus, may limit the final performance. Additionally, using either high-frequency-like fast change or low-frequency-like slow change alone may not effectively represent complex bi-temporal features. Given these limitations, we have developed a new approach that utilizes the dual cross-attention-transformer (DCAT) method. This method mimics the visual change observation procedure of human beings and interacts with and merges bi-temporal features. Unlike traditional Siamese-based CD frameworks, the proposed method extracts multi-scale features and models patch-wise change relationships by connecting a series of hierarchically structured dual cross-attention blocks (DCAB). DCAB is based on a hybrid dual branch mixer that combines convolution and transformer to extract and fuse local and global features. It calculates two types of cross-attention features to effectively learn comprehensive cues with both low- and high-frequency information input from paired CD images. This helps enhance discrimination between the changed and unchanged regions during feature extraction. The feature pyramid fusion network is more lightweight than the encoder and produces powerful multi-scale change representations by aggregating features from different layers. Experiments on four CD datasets demonstrate the advantages of DCAT architecture over other state-of-the-art methods.