In this work, the authors propose a new centre including eccentricity algorithm, to define the fractal dimension of networks. The authors did the fractal analysis of the real Escherichia coli network and a model UV-flower network and confirmed that these networks are fractals . The fractal dimensions (D) of these networks are calculated and D = 2.485 for the real E. coli network and D = 2.1 for the UV-flower network is obtained. Also, the authors defined the fractal dimensions of real social networks and compared their method with Song's, Zhang's and Zheng's methods. Furthermore, the authors' algorithm can solve situations with single-node boxes at the edges of the network and can cover networks with minimum number of boxes. The authors believe that centre including eccentricity algorithm is competitive among existing algorithms and can be used to evaluate fractal properties of complex networks.

The classification of modulated signals under a low signal-to-noise ratio (SNR) environment has become a hot topic due to the complexity of the communication environment. Many relevant publications deal with signal recognition with stable SNR but are not applicable in time-varying SNR scenarios. To solve this problem, we propose a new method for determining the types of modulation based on entropy analysis. The proposed algorithm first extracts characteristics using different types of entropy and can separate the types of phase modulation (PSK): BPSK, QPSK, 8PSK, 16PSK, 32PSK, and 64PSK. In comparison with traditional feature extraction methods, the proposed algorithm increases the efficiency of signal classification. The results show that the algorithm can achieve better signal classification effects, even if SNR reaches -4 dB.