Abstract-A spectrum handoff model and optimal channel decision method based on Extenics have been proposed in order to resolve the optimal channel decision problem of spectrum handoff in Cognitive Radio Sensor Networks. The method of matter-element Extenics is used to analyses the spectrum handoff process. The channel state through the spectrum sensing and status information of the primary users is analyzed and transformed by matter-element Extenics. Then the parameters to determine the best channel are calculated on the basis of Extenics correlation function. Finally the optimal channel is selected. The simulation results show that the proposed method can improve spectrum utilization efficiency of Cognitive Radio Sensor Networks and can reduce the interrupt probability of second users.

This paper presents a flexible control method for snake-like robots to adapt to the environment autonomously. This method enables the snake-like robot to sense the environment and adjust autonomously according to the changes of the environment. For example, snake-like robots climb pipes with varying diameters. In order to achieve efficient and flexible motion, we adopt closed-loop gait control. This control method uses a parameterized sine wave (gait function) and long short-term memory network (LSTM) model. Because of the structure of LSTM is suitable for prediction of time series data, we use LSTM to predict the changes of joint angles that can best represent the shape change of snake-like robot, and integrate the predicted joint angle values with the gait values to realize the control of robot. Because this control method will eventually allow the snake-like robot to move autonomously in the changing environment, we can achieve the flexible adaptive behavior of the robot.

This paper proposes a cooperative censoring spectrum sensing scheme based on dependent function of extension theory for Cognitive Radio Sensor Networks (CRSN). The scheme uses the dependent function of Extension theory to identify the presence or absence of the licensed user's (LU) signal, and then calculate the related degree through dependent function to identify the initial test results of licensed users, and then send these results to the fusion center. Use a trust evaluation scheme based on noise jamming and channel attenuation for each node, and then this trust evaluation result of each node is sent to the fusion center. The fusion center makes the final decision by the K-M rule. Simulation results show that the proposed scheme could improve the detect probability effectively.