In this study, a new fast algorithm for optimal design of block digital filters is proposed based on the skew circulant matrix, the Toeplitz and the skew shift cyclic matrices. The developed skew-cyclic filter, compared with the cyclic filter, not only achieves the same computational complexity of the design process and the memory requirements, but is more efficient with only approximately half of the computational cost for the real signals. † C: the skew cyclic matrix † π −1 :t h eM × M matrix of the skew cyclic right shift operator † F M : the M × M matrix corresponding to a M-point DFT; and F −1 M as its inverse † S: the L × M selection matrix, a binary matrix which selects the L values out of the M from the middle † A(:, j): an m × 1 vector composed of the elements of the jth column of an m × 1 matrix A www.ietdl.org

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. en, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. e results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

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This study presents a new humanoid robot control structure -Man-Function humanoid robot. The sensing devices worn on the human body, these devices will produce signals of joints' change when people move. Computer of the control system receiving the signals and processing them, then issue control signals to the servos of the robot at the same time, control the robot's behavior. For this reason, a control structure of human's behavior to determine the robot's behavior formed. The humanoid robot has 17 servos and two pressure sensors, the rotation of these servos' steering gears lead to the robot's behavior changes and 12 servos corresponding to the human body sensing devices, other 5 servos used for the stability control of the robot combined with the pressure sensors. Based on this control structure, some pilot tests of the sensing device or servo have been done, the closed-loop position control mode has been chosen and the Kalman filter smoothing optimization method been used, the initial static walking control of the robot been realized.