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https://www.selleckchem.com/products/rp-102124.html Binary hologram generation based on deep learning is proposed. The proposed method can reduce the severe effect of quality degradation from binarizing gray-scaled holograms by optimizing the neural network to output binary amplitude holograms directly. In previous work on binary holograms, the calculation time for generating binary holograms was long. However, in the proposed method, once the neural network is trained enough, the neural network generates binary holograms much faster than previous work with comparable quality. The proposed method is more suitable for opportunities to generate several binary holograms under the same condition. The feasibility of the proposed method was confirmed experimentally.We present the Gaussian design of a two-conjugate zoom system, which does not require any mechanical compensation. The device works in two stages. First, with fixed optical power, a lens images the pupil aperture, forming a pair of conjugate planes. Then, we invert the conjugate planes for setting the two-conjugate condition. At the second stage, two varifocal lenses generate a tunable magnified virtual image, at the fixed object plane. The varifocal lenses have fixed interlens separation, and they work with zero-throw. We specify the optical powers of the composing elements and the equivalent optical power as functions of the variable magnification.IWe have designed, simulated, and experimentally tested a broadband metamaterial absorber loaded with lumped resistors in the microwave range. Compared with an electric resonator structure absorber, the composite absorber loaded with lumped resistors has stronger absorptivity over an extremely extended bandwidth. The simulated results show that an effective absorption bandwidth covers from 7.12 to 8.61 GHz with the absorption rate more than 90% under normal incidence. For oblique incidence, the proposed absorber displays an absorption rate above 90% from 7.55 to 8.6

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