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In this paper, we systematically analyzed the phase rotation caused by discrete eigenvalues fluctuation for polarization-division-multiplexed nonlinear frequency division multiplexing systems, and introduced an advanced k-means method to implement phase rotation equalization. As demonstrated in numerical Montel-Carlo simulation, compared with the original k-means algorithm, by using the advanced k-means method, the initial centroids and global optimal solution could be obtained quickly, which brings a certain bit error rate gain.

In this paper, we established a mathematical model for noise interference in discrete b-coefficients modulated nonlinear frequency division multiplexing (NFDM) system. Based on this model, through a reasonable design of input and output, we proposed a novel artificial neural network (ANN) model as equalizer to suppress noise. As shown in numerical simulation, the anti-noise ability of the discrete b-coefficients modulated NFDM system has been significantly improved by using the proposed ANN equalizer.

In this paper, we systematically discussed channel estimation method for nonlinear frequency division multiplexing system with discrete spectrum modulation. It is shown in the simulation results that the proposed method improved the system performance, whether in a single eigenvalue or multiple eigenvalues transmission.

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node’s own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node’s own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

The physical adsorption performance of methane molecule on the circular pore surface of Si (110) is investigated using the density functional theory based on first principle calculation. The adsorption energy and density of states of system is calculated during methane absorbing on three different adsorption positions. By analyzing the change of the model band structure and density of states before and after adsorption, the adsorption characteristics corresponding to three adsorption positions are achieved. The results show that methane is located on the top position of circular pore surface of Si (110) is the most stable adsorption position. When CH 4 adsorbed on this site, the electron peak shift of the absorbing system which moves to the direction of lower energy and adsorption energy is -17.5eV. The obtained data and conclusion in this paper will be applied in the fields of the sensitive to methane of silicon surface and gas sensor technology.