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Abstract Shaping the intensity profile of a laser beam is desired by various industrial applications. In this paper, a new approach is presented to design and fabricate liquid crystal (LC) micro‐optical elements (MOEs) with engineered Pancharatnam–Berry (PB) phases for beam shaping. By generalizing the Snell's law for spatially variant PB phases, molecular orientation patterns are designed for the liquid crystal MOEs to shape a Gaussian laser beam into flattop intensity profiles with circular and square cross‐sections, with the β parameter varied from 4 to 42. It is demonstrated that such liquid crystal beam shaping MOEs can be fabricated with high throughput and high resolution by using a photopatterning technique based on plasmonic metamasks and that they produce excellent beam quality, no zero‐order light leakage with a beam size from 10 to 600 µm. As the plasmonic metamasks allow for encoding arbitrary molecular orientations, i.e., arbitrary geometric phase profiles, the approaches presented here are widely applicable to large‐scale manufacturing of liquid crystal MOEs for any beam shapes.
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InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections
Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow patterns, etc. However, these techniques are computationally intensive, especially when the number of signal timing scenarios to be simulated are large. In this paper, we propose InterTwin, a Deep Neural Network architecture based on Spatial Graph Convolution and Encoder-Decoder Recurrent networks that can predict the MOEs efficiently and accurately for a wide variety of signal timing and traffic patterns. Our methods can generate probability distributions of MOEs and are not limited to mean and standard deviation. Additionally, GPU implementations using InterTwin can derive MOEs, at least four to five orders of magnitude faster than microscopic simulations on a conventional 32 core CPU machine.
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- Award ID(s):
- 1922782
- PAR ID:
- 10332843
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 11
- Issue:
- 24
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 11637
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app112411637
