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The three-dimensional (3D) vectorial nature of electromagnetic waves of light has not only played a fundamental role in science but also driven disruptive applications in optical display, microscopy, and manipulation. However, conventional optical holography can address only the amplitude and phase info...
Holography offers a vital platform for optical information storage and processing, which has a profound impact on many photonic applications, including 3D displays, LiDAR, optical encryption, and artificial intelligence. In this review, we provide a comprehensive overview of optical holography, moving fr...
The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component....
power on-device artificial intelligence acceleration chips, our CNN also runs interactively on mobile (iPhone 11 Pro at 1.1 hertz) and edge (Google Edge TPU at 2.0 hertz) devices, promising real-time performance in future-generation virtual and augmented-reality mobile headsets. We enable this ...
scientists have attempted to create algorithms to address both of these problems. Wetzstein and his colleagues also developed algorithms but did so usingneural networks, a form of artificial intelligence that attempts to mimic the way the human brain learns information. They call this "neural hologr...
Gu & S. Lin (1990): Holography in artificial neural networks. Natiire 343, 325-330.D. Psaltis, D. Brady, X.-G. Gu, and S. Lin, "Holography in artificial neural networks," Nature 343, 325 (1990).D. Brady, S. Lin, X. G. Gu, and D. Psaltis, "Holography in artificial ...
Near-eye displays are fundamental technology in the next generation computing platforms for augmented reality and virtual reality. However, there are remaining challenges to deliver immersive and comfortable visual experiences to users, such as compact f
the deep neural network has learned the statistical image transformation between a holographic imaging system and an incoherent bright-field microscope; therefore, we refer to this approach as “Bright-field Holography”. In some sense, deep learning brings together the best of both worlds by fusing...