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https://www.selleckchem.com/products/etc-159.html Utilizing the hybridization from two independent geometrical dimensions of rectangular-antennas, our approach ingeniously transforms the polarization-multiplexing into the dual-directional channels. A series of calculations and experimental results demonstrate that our asymmetric approach simultaneously constructs completely independent imaging encryptions for both forward and backward directions. Additionally, our proposed approach becomes a practical scheme with broadband visible-frequency operation and great simplicity in design and nanofabrication. We believe the universal scheme could facilitate to increase the information encoding capacity and holographic multiplexing channels by expanding the illumination wavevector to the full-space (+/-), and it paves the route toward the potential applications in on-chip integration, telecommunications, encryption, information processing, and communication.Environmental interference and blocked light-emitting diodes (LEDs) often happen in the received signal strength (RSS)-based indoor visible light positioning (VLP) systems, while few solutions to these problems exist. In this paper, we proposed a novel deviation-correction algorithm named memory-artificial neural network (M-ANN) in the 3-dimensional (3D) indoor RSS-VLP system. By memorizing and utilizing the features of signal strength conversion between adjacent test moments, M-ANN can adapt to different test environments in the positioning process. Also, with the help of a designed genetic algorithm (GA) module, M-ANN can efficiently search and retrieve the missing data from an offline simulation database to prevent the VLP outage caused by the blocked LED. The experimental results in a test region of 0.6×0.6×0.8 m3 demonstrate that the proposed M-ANN can significantly mitigate the impact of environmental interference, and it can still maintain relatively high-precision positioning even in the case of blocked LEDs. The

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