The wireless remote iterative learning control (ILC) system with random data dropouts is considered. The data dropout is viewed as a binary switching sequence which obeys the Bernoulli distribution. In order to eliminate the effect of data dropouts on the convergence property of output error, the signal at the same time with the lost one but in the last iteration is used to compensate the data dropout at the actuator. With the dropout compensation, the convergence property of output error is analyzed by studying the element values of system transition matrix. Finally, some simulation results are given to illustrate the validity of the proposed method.

The Versatile Video Coding (H.266/VVC) standard has developed by Joint Video Exploration Team (JVET). Compared with the previous generation video coding standard, the H.266/VVC is more outstanding. Since the H.266/VVC introduces multi-type tree (MTT) structure including binary tree (BT) and ternary tree (TT), which brings the significant coding efficiency but increases coding complexity. Moreover, the intra prediction modes have increased from 35 to 67, which can provide more accurate prediction than H.265/High Efficiency Video Coding (HEVC). Therefore, these can improve the encoding quality, but increase computational complexity. To reduce the computational complexity, this paper designs a fast coding unit (CU) partition and intra mode decision algorithm, which includes fast CU partition based on random forest classifier (RFC) model and fast intra prediction modes optimization based on texture region features. Simulation results indicate that the proposed scheme can save 54.91% encoding time with only 0.93% increase in BDBR. INDEX TERMS H.266/VVC, fast CU partition, intra mode decision, random forest, texture feature

In the current high-efficiency video coding-based three-dimensional video coding (3D-HEVC) design, new depth intra modes including depth modelling modes and region boundary chain coding are applied for depth map coding. These partition-based intra modes achieve the highest possible coding efficiency, but result in extremely large encoding time which obstructs the 3D-HEVC from practical applications. An efficient early termination algorithm for depth map coding in 3D-HEVC is proposed. It makes use of the coding information from the spatial neighbouring depth map treeblock and the co-located texture video treeblock to predict the depth map intra mode treeblock and terminate its mode decision process early. Experimental results show that the proposed algorithm can achieve an average computational saving of about 40% with negligible loss of rate distortion performance in the 3D-HEVC encoder.

The accumulated effect of channel noise, including sensor-to-controller (SC) noise and controller-to-actuator (CA) noise, has a significant impact on the convergence performance of iterative learning control (ILC) systems over wireless networks. In this study, the relation between input error, channel noise and learning gain is derived, which reveals the fact that the contribution of the SC noise and the CA noise to the input error are all influenced by the learning gain. Based on this discovery, a method is proposed to improve the convergence performance of the ILC system when the SC noise and CA noise are independent and Gaussian distributed. Specifically, this method adaptively selects the learning gain through minimising the trace of input error covariance matrix. With the adaptively selected learning gain, the convergence performance of the ILC system is improved significantly. Moreover, the effect of channel noise variance on the convergence speed of the ILC system with the proposed method is analysed theoretically. Finally, numerical experiments are given to illustrate the effectiveness of the proposed method and corroborate the theoretical analysis, respectively.