Detection of Off-normal Images for NIF Automatic Alignment PDF Download
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Author: S. W. Ferguson
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
One of the major purposes of National Ignition Facility at Lawrence Livermore National Laboratory is to accurately focus 192 high energy laser beams on a nanoscale (mm) fusion target at the precise location and time. The automatic alignment system developed for NIF is used to align the beams in order to achieve the required focusing effect. However, if a distorted image is inadvertently created by a faulty camera shutter or some other opto-mechanical malfunction, the resulting image termed ''off-normal'' must be detected and rejected before further alignment processing occurs. Thus the off-normal processor acts as a preprocessor to automatic alignment image processing. In this work, we discuss the development of an ''off-normal'' pre-processor capable of rapidly detecting the off-normal images and performing the rejection. Wide variety of off-normal images for each loop is used to develop the criterion for rejections accurately.
Author: S. W. Ferguson
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
One of the major purposes of National Ignition Facility at Lawrence Livermore National Laboratory is to accurately focus 192 high energy laser beams on a nanoscale (mm) fusion target at the precise location and time. The automatic alignment system developed for NIF is used to align the beams in order to achieve the required focusing effect. However, if a distorted image is inadvertently created by a faulty camera shutter or some other opto-mechanical malfunction, the resulting image termed ''off-normal'' must be detected and rejected before further alignment processing occurs. Thus the off-normal processor acts as a preprocessor to automatic alignment image processing. In this work, we discuss the development of an ''off-normal'' pre-processor capable of rapidly detecting the off-normal images and performing the rejection. Wide variety of off-normal images for each loop is used to develop the criterion for rejections accurately.
Author:
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 254
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Book Description
Author:
Publisher:
ISBN:
Category : Imaging systems
Languages : en
Pages : 798
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Book Description
Author:
Publisher:
ISBN:
Category : Laser materials
Languages : en
Pages : 760
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Book Description
Author:
Publisher: Lulu.com
ISBN: 9291316695
Category : Bank capital
Languages : en
Pages : 294
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Book Description
Author: U.S. Nuclear Regulatory Commission
Publisher:
ISBN:
Category : Nuclear energy
Languages : en
Pages : 48
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Book Description
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 444
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Book Description
Author: Stacy Palen
Publisher:
ISBN: 9780393631760
Category :
Languages : en
Pages : 576
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Author: National Research Council
Publisher: National Academies Press
ISBN: 0309059917
Category : Technology & Engineering
Languages : en
Pages : 358
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Book Description
Optical science and engineering affect almost every aspect of our lives. Millions of miles of optical fiber carry voice and data signals around the world. Lasers are used in surgery of the retina, kidneys, and heart. New high-efficiency light sources promise dramatic reductions in electricity consumption. Night-vision equipment and satellite surveillance are changing how wars are fought. Industry uses optical methods in everything from the production of computer chips to the construction of tunnels. Harnessing Light surveys this multitude of applications, as well as the status of the optics industry and of research and education in optics, and identifies actions that could enhance the field's contributions to society and facilitate its continued technical development.
Author: Florian Knoll
Publisher: Springer Nature
ISBN: 3030338436
Category : Computers
Languages : en
Pages : 274
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Book Description
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.
