Biblio

The non-stationary nature of the human Electroencephalogram (EEG) has caused problems in EEG based biometrics. Stationary signals analysis is done simply with Discrete Fourier Transform (DFT), while it is not possible to analyze non-stationary signals with DFT, as it does not have the ability to show the occurrence time of different frequency components. The Fractional Fourier Transform (FrFT), as a generalization of Fourier Transform (FT), has the ability to exhibit the variable frequency nature of non-stationary signals. In this paper, Discrete Fractional Fourier Transform (DFrFT) with different fractional orders is proposed as a novel feature extraction technique for EEG based human verification with different number of channels. The proposed method in its' best performance achieved 0.22% Equal Error Rate (EER) with three EEG channels of 104 subjects.

In cognitive radio network, the primary user (PU) network and the secondary user (SU) network interfered with each other because of sharing the spectral resource. Also interference among multi-downlinks in SU network decreased the sum rate in SU network and the eavesdropper in PU network decreased the secrecy rate in PU network. Focusing on above problem, this paper raised two channel selection and beamforming methods based on singular value decomposition (SVD) and uplink-downlink duality respectively, and then analyzed the performance of them in physical layer security.