Branch-cut is a classical algorithm in phase unwrapping algorithm based on path-following approach. Goldstein’s branch-cuts algorithm is prone to generate large enclosed areas and produce longer branch-cuts. Models inspired by biosystems can provide new insights into complex computing problems in the real-world. A unicellular and multi-headed slime mold, named as Physarum polycephalum, has been a research hotspot over the last few years. According to the two characteristics of Physarum: the adaptive shortest path finding and adaptive network formation, researchers combined the Hagen-Poiseuille and Kirchhoff law to establish bionic mathematical model: the maze-solving model to get shortest path between two points and multiply sources model for designing efficient network. In this paper, based on these bionic models, combined with the foraging characteristics of Physarum, a biologically inspired algorithm called Physarum Foraging Algorithm (PFA) is proposed for allocation problems of branch-cuts. Firstly, according to the distribution characteristics of residues in the interference fringes map, the residues were processed; secondly, using the bionic model to build a branch-cuts network between residues; finally, the Physarum exhibits a characteristic that the critical tubes are reserved in the process of foraging, using this unique feature to optimize the branch-cuts network, then complete reconstruction of branch-cuts. PFA cannot only significantly cut down the overall length of branch-cuts but also effectively overcome the ‘isolated island phenomenon’ in the unwrapping process. Experimental result showed that the algorithm implements optimal allocation problems of branch-cuts which greatly improves the accuracy of phase unwrapping.
Choosing a reasonable examination way is beneficial to the cultivation of high quality talents. Recently, the conventional college examination methods involve writing and oral test, which is extremely focused on academic performance and caused the separation between teachers and examinations. Optoelectronic detection technology is a specialized course with strong applicability. Therefore, we proposed a diverse form and scientific content method. It is proved that the students receive better learning effect and improve learning and engineering practice ability compared with the traditional assessment methods.