	US 12,106,556 B1
	Task-specific sensor optical designs
Gabriel Carlisle Birch, Albuquerque, NM (US); Brian John Redman, Albuquerque, NM (US); Charles Fredrick LaCasse, IV, Albuquerque, NM (US); Amber Lynn Dagel, Lafayette, CO (US); Meghan Anne Sahakian, Albuquerque, NM (US); Bryan James Kaehr, Albuquerque, NM (US); Tu-Thach Quach, Albuquerque, NM (US); Daniel Alvaro Calzada, Albuquerque, NM (US); Bryana Lynn Woo, Albuquerque, NM (US); and Jaclynn Javonna Stubbs, Albuquerque, NM (US)
Assigned to National Technology & Engineering Solutions of Sandia, LLC, Albuquerque, NM (US)
Filed by National Technology & Engineering Solutions of Sandia, LLC, Albuquerque, NM (US)
Filed on Apr. 4, 2022, as Appl. No. 17/712,316.
Int. Cl. G06V 10/00 (2022.01); G06V 10/147 (2022.01); G06V 10/30 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/82 (2022.01); G06V 10/46 (2022.01)

CPC G06V 10/82 (2022.01) [G06V 10/147 (2022.01); G06V 10/30 (2022.01); G06V 10/765 (2022.01); G06V 10/766 (2022.01); G06V 10/478 (2022.01)]

26 Claims

1. A method for designing a compressive sensing matrix for machine learning, comprising:

receiving an image associated with a classification task;

generating a sensing matrix, the sensing matrix comprising an array of nonzero elements of the image;

providing a prism array comprising a plurality of prism elements, the prism array in communication with the sensing matrix;

mapping a row of values corresponding with an input angle of the prism array to a respective column corresponding with a detector;

detecting via the detector an output angle of a prism element of the prism array associated with a respective input angle;

fabricating a physical model of the detector;

generating a compressed representation of the image via the physical model;

applying a machine learning classification algorithm to the compressed representation of the image; and

generating an optimized non-invertible final determination of the image.