Study on diffusion behaviors, such as contagion of virus, adoption of production, and information propagation, is one of the hottest topics of social network research. The conventional models usually focus on a particular factor that influences the contagion process. A novel model of diffusion based on multiple behavior evidence fusion is proposed on the analysis of prevalent social network data set, which can simulate the information propagation process with the fitting goodness of 0.79. In this model, impact factors, i.e. the influence between neighborhood, and the activity of user, are taken into account together and fused into combined evidence mass function, which can be used to infer users' forwarding behavior by D-S theory reasoning. Series of experiments tested on the published Enron email data set show that the multiple evidence fusion based diffusion model (MEFDM) is reasonable and feasible in demonstrating the cascading of information in the social network.

Multiple evidence fusion based information diffusion model for social network

Phase acquisition and carrier tracking scheme for π/4-DQPSK high rate burst communication systems

This paper describes an efficient coherent demodulation algorithm aiming at the receiver in Terrestrial Trunked Radio (TETRA) communication system. The algorithm proposes frequency offset error twice-estimation and sectioned phase offset compensation to achieve the high precision in carrier synchronization. Odd-even decision is then proposed in the algorithm to avoid signal noise ratio (SNR) loss in differential demodulation. The simulation has confirmed that the precision of frequency offset error twice-estimation is better than that of data-aided (DA) estimation and that compared with differential demodulation, the bit error rate (BER) performance of coherent demodulation is much better.

Wang Bo

Papers

An efficient coherent demodulation algorithm for TETRA communication system