Optical Processing And Computing

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Optical Computing

Author	: Dror G. Feitelson
Publisher	: MIT Press (MA)
Total Pages	: 430
Release	: 1988
ISBN-10	: UOM:39015012770635
ISBN-13	:
Rating	: 4/5 ( Downloads)

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Book Synopsis Optical Computing by : Dror G. Feitelson

Download or read book Optical Computing written by Dror G. Feitelson and published by MIT Press (MA). This book was released on 1988 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optical Computers provides the first in-depth review of the possibilities and limitations of optical data processing.

Optical Computing Related Books

Language: en
Pages: 430

Optical Computing

Authors: Dror G. Feitelson

Categories: Computers

Type: BOOK - Published: 1988 - Publisher: MIT Press (MA)

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Optical Computers provides the first in-depth review of the possibilities and limitations of optical data processing.

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Fundamentals of Optical Computing Technology

Authors: Xiujian Li

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Type: BOOK - Published: 2018-05-12 - Publisher: Springer

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This book presents the principles, experimental technologies, up-to-date research findings and applications of various optical-computing technologies and device

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Optical Processing and Computing

Authors: Henri Arsenault

Categories: Technology & Engineering

Type: BOOK - Published: 2012-12-02 - Publisher: Elsevier

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Optical Processing and Computing is a collection of research from the USA, Canada, Russia, and Poland on the developments in the fields of digital optical compu

Language: en
Pages: 345

Optical Computing Hardware

Authors: Jürgen Jahns

Categories: Technology & Engineering

Type: BOOK - Published: 2014-05-10 - Publisher: Academic Press

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Optical Computing Hardware provides information pertinent to the advances in the development of optical computing hardware. This book discusses the two applicat

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Photonic Reservoir Computing

Authors: Daniel Brunner

Categories: Science

Type: BOOK - Published: 2019-07-08 - Publisher: Walter de Gruyter GmbH & Co KG

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Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously fa