In order to reduce the influence of noise in internet of things based on electric power line, an algorithm of improved robust independent component analysis (robustICA) in blind source signal processing was used. The ...
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ISBN:
(纸本)9781510814059
In order to reduce the influence of noise in internet of things based on electric power line, an algorithm of improved robust independent component analysis (robustICA) in blind source signal processing was used. The receiver of communication adopts multiplex signals. The correlation of multiple signals was reduced by pre-whitening matrix. Multiple signals were separated by separation matrix estimated by Based on the relaxation factor of robust independent component analysis algorithm. In the end, the data signal was found by the decision threshold. The experiment of simulation of power line channel noise shows that the algorithm can remove noise effectively, SNR get up 2–3 times and the optimized algorithms improve the convergence.
In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate tha...
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In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.
In this paper, we construct a Lattice Boltzmann scheme to simulate the well known total variation based restoration model, that is, ROF model. The advantages of the Lattice Boltzmann method include the fast computatio...
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In this paper, we construct a Lattice Boltzmann scheme to simulate the well known total variation based restoration model, that is, ROF model. The advantages of the Lattice Boltzmann method include the fast computational speed and the easily implemented fully parallel algorithm. A conservative property of the LB method is discussed. The macroscopic PDE associated with the LB algorithm is derived which is just the ROF model. Moreover, the linearized stability of the method is analyzed. The numerical computations demonstrate that the LB algorithm is efficient and robust. Even though the quality of the restored images is slightly lower than those by using the ROF model, the restored images of the LB method are satisfactory. Furthermore, computational speed of the LB method is much faster than ROF model. In general, CPU time of the LB method for restored images is about one tenth of ROF model.
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