This paper investigates a covert communication system enhanced by a reconfigurable intelligent surface (RIS), where an unmanned aerial vehicle (UAV) serves as the transmitter while a Warden moves freely within a certain range. To enhance both communication covertness and energy efficiency, we jointly optimize the UAV trajectory and RIS phase shifts to maximize system energy efficiency. The optimization problem is formulated as a Markov decision process (MDP), and a reinforcement learning-based approach, LSTM-DDQN, is proposed to exploit deep reinforcement learning (DRL) in dynamic environments. By integrating long short-term memory (LSTM) to capture temporal dependencies and double deep Q-network (DDQN) to mitigate Q-value overestimation, the proposed algorithm enhances stability and decision-making effectiveness. Simulation results demonstrate that LSTM-DDQN significantly improves energy efficiency while effectively reducing the risk of detection compared to conventional DDQN, validating its superiority in RIS-assisted UAV covert communication.

Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its predecessor standard High Efficiency Video Coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of the coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving a desirable trade-off between complexity and RD performance. The experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%~66.88% with a negligible Bjøntegaard delta bit-rate (BD-BR) of 1.322%~3.188%, significantly outperforming other state-of-the-art approaches.