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几何深度学习光学传感。

Geometric deep optical sensing.

机构信息

Department of Electrical Engineering, Yale University, New Haven, CT, USA.

Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.

出版信息

Science. 2023 Mar 17;379(6637):eade1220. doi: 10.1126/science.ade1220.

Abstract

Geometry, an ancient yet vibrant branch of mathematics, has important and far-reaching impacts on various disciplines such as art, science, and engineering. Here, we introduce an emerging concept dubbed "geometric deep optical sensing" that is based on a number of recent demonstrations in advanced optical sensing and imaging, in which a reconfigurable sensor (or an array thereof) can directly decipher the rich information of an unknown incident light beam, including its intensity, spectrum, polarization, spatial features, and possibly angular momentum. We present the physical, mathematical, and engineering foundations of this concept, with particular emphases on the roles of classical and quantum geometry and deep neural networks. Furthermore, we discuss the new opportunities that this emerging scheme can enable and the challenges associated with future developments.

摘要

几何,一门古老而充满活力的数学分支,对艺术、科学和工程等诸多学科有着重要而深远的影响。在这里,我们介绍一个新兴的概念,称为“几何深度学习光学传感”,它基于一些最近在先进的光学传感和成像中的演示,其中可重构传感器(或其阵列)可以直接破译未知入射光束的丰富信息,包括其强度、光谱、偏振、空间特征,以及可能的角动量。我们介绍了这个概念的物理、数学和工程基础,特别强调了经典和量子几何以及深度神经网络的作用。此外,我们讨论了这个新兴方案可以带来的新机遇以及未来发展所面临的挑战。

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