Range, direction, and velocity determination with a diffractive optic for robotic vision

1994 ◽  
Author(s):  
Denise M. Lyons
Keyword(s):  
Robotic Vision ◽  
Diffractive Optic

Comparative Surface Imaging Study of Multifocal Diffractive Intraocular Lenses

2021 ◽  
Author(s):  
Hyeck Soo Son ◽  
Jung Min Lee ◽  
Ramin Khoramnia ◽  
Chul Young Choi
Keyword(s):  
Imaging Study ◽  
The Other ◽  
Intraocular Lenses ◽  
Optical Surface ◽  
Diffractive Optic ◽  
Atomic Force ◽  
Acceptable Range ◽  
Different Types ◽  
Concentric Pattern

Abstract Purpose To analyse and compare the surface topography and roughness of three different types of diffractive multifocal IOLs. Methods Using scanning electron microscope (SEM, Inspect F, 5.0 KV, maximum magnification up to 20,000) and atomic force microscope (AFM, Park Systems, XE-100, non-contact, area profile comparison, 10 × 10 µm, 40 × 40 µm), the surface quality of the following diffractive IOLs was studied: the AcrySof IQ PanOptix (Alcon, USA), the AT LARA 829MP (Carl Zeiss Meditec, Germany), and Tecnis Symfony (Johnson&Johnson Vision, USA). The measurements were made over three representative areas (central non-diffractive optic, central diffractive optic, and diffractive step) of each IOL. Roughness profile in terms of mean arithmetic roughness (Ra) and root-mean-squared roughness (Rq) values were obtained and compared statistically. Results In SEM examination, all IOLs showed a smooth optical surface without any irregularities at low magnification. At higher magnification, Tecnis Symfony showed unique highly regular, concentric, and lineate structures in the diffractive optic area which could not be seen in the other studied diffractive IOLs. The differences in the measured Ra and Rq values of the Tecnis Symfony were statistically significant compared to the other models (p < 0.05). Conclusion Various different topographical traits were observed in three diffractive multifocal IOLs. The Ra values of all studied IOLs were within an acceptable range. Tecnis Symfony showed statistically significant higher surface Ra values at both central diffractive optic and diffractive step areas. Furthermore, compared to its counterparts, Tecnis Symfony demonstrated highly ordered, concentric pattern in its diffractive surfaces.

Physical and Technological Limitations of Optical Information Processing and Computing

MRS Bulletin ◽  
1988 ◽  
Vol 13 (8) ◽  
pp. 36-41 ◽  
Author(s):  
Armand R. Tanguay
Keyword(s):  
Information Processing ◽  
Spread Spectrum ◽  
Beam Steering ◽  
Optical Processing ◽  
Robotic Vision ◽  
Wide Range ◽  
Simple Processing ◽  
Technological Barriers ◽  
Processing Architectures ◽  
Technological Limitations

Over the past four decades, the growth of information processing and computational capacity has been truly remarkable, paced to a large extent by equally remarkable progress in the integration and ultra-miniaturization of semiconductor devices. And yet it is becoming increasingly apparent that currently envisioned electronic processors and computers are rapidly approaching technological barriers that delimit processing speed, computational sophistication, and throughput per unit dissipated power. This realization has in turn led to intensive efforts to circumvent such bottlenecks through appropriate advances in processor architecture, multiprocessor distributed tasking, and software-defined algorithms.An alternative strategy that may yield significant computational enhancements for certain broad classes of problems involves the utilization of multidimensional optical components capable of modulating and/or redirecting information-carrying light wave-fronts. Such an optical processing or computing approach relies for its competitive advantage principally on massive parallelism in conjunction with relative ease of implementation of complex (weighted) interconnections among many (perhaps simple) processing elements. A wide range of computational problems exist that lend themselves quite naturally to optical processing architectures, including pattern recognition, earth resources data acquisition and analysis, texture discrimination, synthetic aperture radar (SAR) image formation, radar ambiguity function generation, spread spectrum identification and analysis, systolic array processing, phased array beam steering, and artificial (robotic) vision.

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