No abstract

The traditional active contour models are sensitive to the speckle noise in the synthetic aperture radar (SAR) images. In this paper, the Markov random field (MRF) theory is incorporated into the fuzzy active contour model to detect the changes of multitemporal SAR images. In the proposed method, neighboring information is considered to modify the pointwise prior probability for exploiting the mutual and spatial information. In addition, we incorporate MRF into the fuzzy active contour model and get the resulting MRF-based energy function. Finally, we drive the associated first variation of the energy function to compute the fuzzy membership. Due to the introduction of MRF, the proposed MRF-based fuzzy active contour model is robust to the speckle noise in the SAR images and can achieve accurate change detection results. Experiments on four SAR image datasets demonstrate that the proposed MRF-based fuzzy active contour model is able to accurately segment the difference image and has better performance in comparison with other change detection techniques. INDEX TERMS fuzzy active contour model, Markov random field, change detection, synthetic aperture radar

A wireless sensor network integrates sensor technology, embedded computing technology, modern networking and wireless communication technology, distributed information processing technology to form a wireless network. This paper analyzes the architecture of the wireless sensor network and its routing features, analyzes current wireless sensor network routing protocols, and summarizes wireless sensor routing technology development prospects.

With the country’s policy support and the rapid development of Internet technology, the domestic consumption level has been escalating and the consumption structure has changed. The traditional retail industry cannot integrate all the relevant data due to data security and privacy protection concerns so that it is unable to adjust sales strategies in an accurate and timely manner. New retail has sounded the clarion call for the retail revolution. The supply chain demand forecasting is an important problem for the supply chain management. In this research, we propose a new retail supply chain commodity demand forecasting framework based on vertical federal learning, which solves the problems of data security and privacy faced by new retail theoretically and empirically. In experiments, we use datasets from different platforms (such as social platforms, e-commerce platforms, and retailers) in the same region for federated learning. The experiment results demonstrate the superiority of the proposed algorithm.

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.