No abstract

To prevent any negative electromagnetic influence of high-density integrated circuits, an insulation package needs to be specially designed to shield it. Aiming at the low efficiency and material waste in traditional packaging methods, a printed circuit board (PCB) selective packaging system based on a multi-pattern solder joint simultaneous segmentation algorithm and three-dimensional printing technology is introduced in this paper. Firstly, the structure of PCB selective packaging system is designed. Secondly, to solve the existing problems, such as multi-pattern solder joints which are located densely in small welding areas and are hard to be extracted in the small-area integrated circuit board, a multi-pattern solder joint simultaneous segmentation algorithm is developed based on (geometrical) neighborhood features to extract and locate the optimal PCB solder joint areas. Finally, tests using three actual PCB are carried out to compare the proposed method with traditional multi-threshold solder joint extraction methods. Test results indicate that the proposed algorithm is simple and effective. Diverse solder joints can be optimally located and simultaneously extracted from the collected PCB image, which greatly improves the filling rate of the solder joint areas and filters out false pixels. Thus, this method provides a reliable location-finding tool to help place solder points in PCB selective packaging systems.

The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods.