Optimizing a Compressive Imager for Machine Learning Tasks

• Published in: 2019 53rd Asilomar Conference on Signals, Systems, and Computers pp. 1000 - 1004
• Main Authors: Redman, Brian J., Calzada, Daniel, Wingo, Jamie, Quach, Tu-Thach, Galiardi, Meghan, Dagel, Amber L., LaCasse, Charles F., Birch, Gabriel C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2019
• Subjects: compressive sensing; machine learning; optics
• ISSN: 2576-2303
• DOI: 10.1109/IEEECONF44664.2019.9048763

Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.