Extracting moving targets from video accurately is of great significance in the field of intelligent transport.To some extent,it is related to video segmentation or matting.In this paper,we propose a non-interactive automatic segmentation method for extracting moving targets.First,the motion knowledge in video is detected with orthogonal Gaussian-Hermite moments and the Otsu algorithm,and the knowledge is treated as foreground seeds.Second,the background seeds are generated with distance transformation based on foreground seeds.Third,the foreground and background seeds are treated as extra constraints,and then a mask is generated using graph cuts methods or closed-form solutions.Comparison showed that the closed-form solution based on soft segmentation has a better performance and that the extra constraint has a larger impact on the result than other parameters.Experiments demonstrated that the proposed method can effectively extract moving targets from video in real time.
Membrane algorithms （MAs）, which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.
In this study,the DNA logic computing model is established based on the methods of DNA self-assembly and strand branch migration.By adding the signal strands,the preprogrammed signals are released with the disintegrating of initial assembly structures.Then,the computing results are able to be detected by gel electrophoresis.The whole process is controlled automatically and parallely,even triggered by the mixture of input signals.In addition,the conception of single polar and bipolar is introduced into system designing,which leads to synchronization and modularization.Recognizing the specific signal DNA strands,the computing model gives all correct results by gel experiment.
In this paper, a logic computing model was constructed using a DNA nanoparticle, combined with color change technology of DNA/Au nanoparticle conjugates, and DNA computing. Several important technologies are utilized in this molecular computing model: DNA self-assembly, DNA/Au nanoparticle conjugation, and the color change resulting from Au nanoparticle aggregation. The simple logic computing model was realized by a color change, resulting from changing of DNA self-assembly. Based on this computing model, a set of operations computing model was also established, by which a simple logic problem was solved. To enlarge the applications of this logic nanocomputing system, a molecular detection method was developed for H1N1 virus gene detection.
