As the most driven force of future 6G communications, Internet of Everything (IoE) requires energy-constrained IoE devices (IDs) to realize interconnection among users, information, and things. Wireless-powered orbital angular momentum (OAM) communications can be used for IDs to achieve simultaneous wireless power transfer and multiple independent information transmissions by different OAM modes. However, the practical blockage and access capability enhancement impose crucial challenges for wireless-powered OAM communications. Therefore, for blocked line-of-sight scenario, this paper proposes non-orthogonal multiple access (NOMA)-enhanced intelligent reflecting surface (IRS) for wireless-powered OAM communications by jointly optimizing power allocation and passive beamforming. First, we formulate the information capacity and harvested energy optimization problem under the constraints of unit-modulus reflecting phase shift, minimum power transfer requirement, and minimum information transmission demand. Then, the harvested energy is converted to the achievable capacity by the energy utilization for information transmission, thus forming the sum capacity maximization problem. Finally, we decompose the sum capacity maximization problem into three subproblems, and obtain the optimal power allocation and passive beamforming by iteration. Simulation results validate the proposed NOMA-enhanced IRS of wireless-powered OAM communications, and demonstrate that the joint power allocation and passive beamforming achieves better capacity performance than the other optimization schemes.

Volume rendering is a costly but widely used technique in scientific visualization. Various methods were proposed to accelerate volume rendering, most of which make use of empty brick skipping to avoid sampling the emtpy space of volumetric data, whereas the empty voxels in the valid bricks are still treated as valid. We introduce Depth-Box VDB, a simple but effective method based on GPU-based voxel database (GVDB) to accelerate volume rendering by further reducing the number of samplings. With the help of depth maps attached to the valid bricks and the displacement mapping technique, our method can skip part of the empty voxels in the valid bricks and has achieved significant improvement in the performance of volume rendering. According to the performance tests on different datasets, the rendering efficiency increases by up to 121.74%, while the use of memory increases by around 18.67%. Comparing with different shading models, we can conclude that our method tends to speed up more as the rendering requires more shading computing.