Liquid crystal on silicon (LCoS) phase modulators spatially modulate the phase of light across the panel. The orientation order and elongated molecules of nematic liquid crystals (LCs) means that traditional LCoS phase modulators are inherently polarization-dependent, resulting in either complicated polarization manipulation systems or high optical power loss for unpolarized incident light. We propose a polarization-independent LCoS (PI-LCoS) device that combines existing technologies, which allows for cost-effective fabrication. Our PI-LCoS phase modulator is suitable for a wide variety of applications in telecommunication, adaptive optics, and display technologies. We have demonstrated the feasibility of the proposed PI-LCoS device by fabricating polarization-independent LC cells using a thin-film quarter-wave plate composed of a photoalignment layer and a layer of LC polymer. We show experimentally that the proposed design can efficiently modulate the phase of light with arbitrary input polarization.
Automatic target recognition (ATR) is an ongoing topic of research for the Air Force. In this effort we develop, analyze and compare template matching and deep learning algorithms for use in the task of classifying occluded targets in light detection and ranging (LiDAR) data. Specifically, we analyze convolutional sparse representations (CSR) and convolutional neural networks (CNN). We explore the strengths and weaknesses of each algorithm separately, then improve the algorithms, and finally provide a comprehensive comparison of the developed tools. To conduct this final comparison, we improve the functionality of current LiDAR simulators to include our occlusion creator and parallelize our data simulation tools for use on the DoD High Performance Computers. Our results demonstrate that for this problem, a DenseNet trained with images containing representative clutter outperforms a basic CNN and the CSR approach.
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