An optical neural network developed at the University of California, Los Angeles (UCLA) Henry Samueli School of Engineering that concurrently works with multiple wavelengths of light could potentially lead to devices that instantly recognize objects without additional computer processing, with potential applications for robots and autonomous vehicles.
The network is a maze with an array of translucent wafers made of different materials like plastic or glass, engineered at a smaller scale than the wavelength of light to split beams into various directions.
Said UCLA's Aydogan Ozcan, “There is richer information when you can see colors through different wavelengths of light. Most scenes naturally contain information in vivid color, so the more wavelengths that a network can ‘see,’ the more it increases the amount of information it can process.”
From UCLA Newsroom
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