Visual solution to minimum spanning tree problem based on DNA origami

DNA origami is a highly precise nanometer material based on DNA molecular. In the current study, we present a visual computing model of minimum spanning tree that combines advantages of DNA origami, hybridization chain reaction and nano-gold particles. Nano-gold particles were used to represent vertices and molecular beacons with fluorescent labels were used as anchor strands, which were fixed on origami substrate with staple strands according to the shape in graph. We then induced hybridization chain reaction using initiator strands and fuel strands. Lastly the problem was detected using fluorescence. The model provides a visualized calculation model of minimum spanning tree by using hybridization chain reaction and fluorescence labeling on origami bases. This model utilizes their advantages and demonstrates effectiveness of the model through case simulation. It also reduces computational complexity of the problem and improve the way of solution reading.

Fine-Grained Visual Computing Based on Deep Learning

With increasing amounts of information, the image information received by people also increases exponentially. To perform fine-grained categorization and recognition of images and visual calculations, this study combines the Visual Geometry Group Network 16 model of convolutional neural networks and the vision attention mechanism to build a multi-level fine-grained image feature categorization model. Finally, the TensorFlow platform is utilized to simulate the fine-grained image classification model based on the visual attention mechanism. The results show that in terms of accuracy and required training time, the fine-grained image categorization effect of the multi-level feature categorization model constructed by this study is optimal, with an accuracy rate of 85.3% and a minimum training time of 108 s. In the similarity effect analysis, it is found that the chi-square distance between Log Gabor features and the degree of image distortion show a strong positive correlation; in addition, the validity of this measure is verified. Therefore, through the research in this study, it is found that the constructed fine-grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.