In order to enhance the robustness to initial contour as well as improve the segmentation efficiency for images with intensity inhomogeneity or noise, a region-based active contour model was proposed. First, a global intensity fitting force and a local intensity fitting force were designed separately. Then, the model's fitting term was obtained by the linear combination. And the weight between the two fitting forces were adjusted to improve the robustness of the model to the initial contour. Finally, the length term of evolution curve was employed to keep the smoothness of the curve. Experimental results show that compared with Region-Scalable Fitting(RSF) model and Selective Local or Global Segmentation(SLGS) model, the proposed model has the number of iterations reduced by about 57% and 31%, and the segmentation time reduced by about 62% and 14%. The proposed model can quickly and accurately segment noisy images and images with intensity inhomogeneity without initial contour. Besides, it has good segmentation effect on some practical images such as medical images and infrared images.

Aiming at the problem that biped robots may easily lose their motion stability when walking on uneven ground, a value-based deep reinforcement learning algorithm called Deep Q-Network (DQN) gait control method was proposed, which is an intelligent learning method of posture adjustment. Firstly, an off-line gait for a flat ground environment was obtained through the gait planning of the robot. Secondly, instead of implementing a complex dynamic model compared to traditional control methods, a bipedal robot was regarded as an agent to establish robot environment space, state space, action space and Reward-Punishment (RP) mechanism. Finally, through multiple rounds of training, the biped robot learned to adjust its posture on the uneven ground and ensures the stability of walking. The performance and effectiveness of the proposed algorithm was validated in a V-Rep simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is improved obviously, which achieves the robot's posture adjustment behavior learning and proves the effectiveness of the method.

Mobile sink can efficiently collect data and extend the network lifetime. However, the existing researches about data collection based on mobile sink mainly focus on path-constrained mobile sink. Hence, a path-controlled traversal model for mobile sink data collection was constructed, and a data collection algorithm for mobile sink based on optimal-path traveling was proposed. The algorithm discretized the continuous path problem by local Voronoi grid, used the amount of data collected and system energy consumption as performance metric, combined taboo search algorithm to achieve the maximum amount of data collected and the minimum of network energy consumption traversing. Theoretically and experimentally, it is concluded that the proposed algorithm is able to solve the optimal-path traveling of data collection problem using path-controlled mobile sink.