Coherent measurement of orbital angular momentum (OAM) spectrum of light fields plays a key role in many important applications, such as OAM-based multiplexing in optical communications. The existing methods for measuring the OAM spectrum by spatially separating OAM components or interferometric technique suffer from poor efficiency and interferometric stability requirements. Here, we propose a novel technique to measure the OAM spectrum of light fields in a single shot manner by exploiting a scattering optical element. Our technique enables to directly extract the OAM spectrum from a recorded single-shot speckle pattern using algorithms based on the speckle-correlation scattering matrix and spatial mode decomposition method. As a proof of concept, we built a robust measurement system based on a fast digital micromirror device to demonstrate the feasibility of the proposed technique.
A new compound eyes model is designed to track the fast-moving object and detect its position with high precision in
complex background. The compound eyes that consist of several eyelet channels have good nature of wide fields of view
and high update rate for vision systems. Adjacent eyelet fields of view have certain degree of overlapping for detecting
3D coordinates of an object. Gradient lenses, instead of fiber bundles, are used below each eyelet lens in order to transfer
eyelet image onto a re-image structure in simulation. And the eyelet image is projected onto a Charge Coupled Device
(CCD) detector array through the gradient lens, avoiding expensive fiber bundles and complex assembly. Besides,
simulation and analyze of the compound eyes model is performed. Neural network calibration algorithm has been
adapted to build the relationship between the object points and corresponding image points for each eyelet. This
calibration algorithm provides a highly accurate prediction of object data points from their corresponding image points.
After getting the intrinsic and extrinsic parameters of each eyelet channel, 3D coordinates of an object can be calculated
from its image points. Preliminary experimental results for Neural Network calibration are presented and evaluated,
showing residual errors between actual and predicted direction angles of around 10-3~10-4 rad. And errors between
actual and calculated coordinates of position are within 3%.
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