An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.

Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topology and attribute information of the graph for symmetric normalized Laplacian transform. Then, through the feature-structured enhanced module, the node features are expanded into regular structured data, such as images and sequences, which are input to DGCGAN as positive samples, thus expanding the sample capacity. In addition, the feature-enhanced (FE) module is adopted to enhance the typicality and discriminability of node features, and to obtain richer and more representative features, which is helpful for facilitating accurate classification. Finally, additional constraints are added to the network model by introducing DCGAN, thus enhancing the robustness of the model. Through extensive empirical studies on several standard benchmarks, we find that DGCGAN outperforms state-of-the-art baselines on semi-supervised node classification and remote sensing image classification.

Intrinsic conductive ionic hydrogels, endowed with excellent mechanical properties, hold significant promise for applications in wearable and implantable electronics. However, the complexity of exercise and athletics calls for mechanical tunability, facile processability and high conductivity of wearable sensors, which remains a persistent challenge. In this study, we developed a mechanically tunable and high ionic conductive hydrogel patch to approach multi-gesture or motion monitoring. Through adjustment of the ratio of amino trimethylene phosphonic acid (ATMP) and poly(vinyl alcohol) (PVA), the composite hydrogel attains tunable mechanical strength (varying from 50 kPa to 730 kPa), remarkable stretchability (reaching up to 1900% strain), high conductivity (measuring 15.43 S/m), and strong linear sensitivity (with a gauge factor of 2.34 within 100% strain). Benefitting with the tunable mechanical sensitivity, the composite hydrogel patch can perform subtle movement monitoring, such as epidermal pulses or pronounced muscle vibrations; meanwhile, it can also recognize and detect major motions, such as hand gestures. The mechanically tunable composite hydrogel contributes a versatile sensing platform for health or athletic monitoring, with wide and sensitive adoptability.