Computer-generated hologram (CGH) is recently expanding its application fields. However, the calculation cost is very high, in particular, in the generation of CGH streams for three-dimensional movies. This paper proposes a small-calculation-cost method to generate CGH streams based on a coherent neural network (CNN) that deals with complex-amplitude information with generalization ability in the carrier-frequency domain. After carrier-frequency-dependent learning, we can generate a CGH stream, by sweeping a virtual carrier frequency in the CNN, with neural interpolation thanks to the frequency-domain generalization. Experiments demonstrate a successful stream generation with 1/6 the conventional calculation time.
We analyze a circularly-polarized slot pair on a hollow rectangular waveguide of finite length with corrugations on the bottom by the method of moments. The analysis is for designing a radial line slot antenna for high-power or space applications. The reflection from the end of the corrugation array gives ripples in the slot coupling for the relative position of the slot pair but the ripples are so small that averaging of the slot coupling is acceptable for practical design of the antenna.