Biometric based on Electroencephalogram have proved to be unique enough between subjects for applications. A new method on identifying the individuality of persons by using parametric was used for identification of motor imagery. In this paper, autoregressive mode, phase synchronization, Energy Spectral Density and linear complexity value were used as EEG features. Neural network was employed for identification of individual differences. Then, identification rate was analyzed by different data length and wave band. The result shows that high identification ratio was tongue movement and that perfect accuracy depends on the Paradigm of motor imagery and wave band.

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SUMMARYIn multi-radio multi-channel wireless mesh networks, the design of logical topology is different from that in single channel wireless mesh networks. The same channel assignment algorithm used for various logical topologies will lead to diverse network performance. In this paper, we study the relationship between k-connected logical topology and the maximum number of assigned channels. Meanwhile, we analyze the issues affecting channel assignment performance, and present the lower and upper bounds of the maximum allowable number of assigned channels for k-connected logical topology. We then develop a k-connected logical topology design algorithm based on shortest disjoint paths and minimum interference disjoint paths for each node-pair. In addition, we propose a static channel assignment algorithm according to minimum spanning tree search. Extensive simulations show that our proposed algorithm achieves higher throughput and lower end-to-end delay than fault tolerant topology control algorithms, which validates the involved trade-off between path length and nodal interference. Moreover, numerical results demonstrate that our proposed channel assignment further improves network performance under the context of limited radio interfaces.

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The recently emerging cyber-physical-social system (CPSS) can enable efficient interactions between the social world and cyber-physical system (CPS). The wireless sensor network (WSN) with physical and social sensor nodes plays an important role in CPSS. The integration of the social sensors and physical sensors in CPSS provides an advantage for smart services in different application areas. However, the dynamics of social mobility for social sensors pose new challenges for implementing the coordination of transmission. Furthermore, the integration of social and physical sensors also faces the challenges in term of improving energy efficiency and increasing transmission range. To solve these problems, we integrate the model of social dynamics with collaborative beamforming (CB) technique to formulate the transmission optimization problem as a dynamic game. A novel transmission scheme based on reinforcement learning is proposed to solve the formulated problem. The corresponding implementation of the proposed transmission scheme in CPSS is presented by the design of message exchange processes. The extensive simulation results demonstrate that the proposed transmission scheme presents lower interference to noise ratio (INR) and better signal to noise ratio (SNR) performance in comparison with the existing schemes. The results also indicate that the proposed method has effective adaptation to the dynamic mobility of social sensor nodes in CPSS.