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    Using Microwave Metamaterials in Machine Learning Speeds Object Recognition

    Article obtained from Photonics RSS Feed.

    A new approach to object identification, developed by researchers at Duke University and the Institut de Physique de Nice (INPHYNI), enables joint learning of optimal measurement strategies and a matching processing algorithm, and uses inferred knowledge about task, scene, and measurement constraints to improve the accuracy of object recognition tasks while using a limited number of measurements. The approach uses microwave patterns to increase accuracy and reduce computing time and power requirements.

    The researchers used a metamaterial antenna that can sculpt a microwave wavefront into various shapes. The metamaterial is composed of an 8 × 8 grid of squares. Each square contains electronic structures that allow the square to…

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    Jan, 13 2020 |

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