國立高雄大學圖資館 |

Language: English

Back

Application of FPGA to real-time mac...

Antonik, Piotr.

Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of FPGA to real-time machine learningby Piotr Antonik.
Reminder of title:	hardware reservoir computers and software image processing /
Author:	Antonik, Piotr.
Published:	Cham :Springer International Publishing :2018.
Description:	xxii, 171 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-91053-6
ISBN:	9783319910536$q(electronic bk.)

Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /
Antonik, Piotr.

Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /[electronic resource] :by Piotr Antonik. - Cham :Springer International Publishing :2018. - xxii, 171 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Online Training of a Photonic Reservoir Computer -- Backpropagation with Photonics -- Photonic Reservoir Computer with Output Feedback -- Towards Online-Trained Analogue Readout Layer -- Real-Time Automated Tissue Characterisation for Intravascular OCT Scans -- Conclusion and Perspectives.

This book lies at the interface of machine learning - a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail - and photonics - the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs) Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

ISBN: 9783319910536$q(electronic bk.)

Standard No.: 10.1007/978-3-319-91053-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .A586 2018

Dewey Class. No.: 006.31

Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /
LDR:02591nmm a2200325 a 4500 001 539072
003 DE-He213
005 20181206170949.0
006 m d
007 cr nn 008maaau
008 190122s2018 gw s 0 eng d
020 $a 9783319910536$q(electronic bk.)
020 $a 9783319910529$q(paper)
024 7 $a 10.1007/978-3-319-91053-6 $2 doi
035 $a 978-3-319-91053-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .A586 2018
072 7 $a TTBL $2 bicssc
072 7 $a TEC019000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .A635 2018
100 1 $a Antonik, Piotr. $3 816489
245 1 0 $a Application of FPGA to real-time machine learning $h [electronic resource] : $b hardware reservoir computers and software image processing / $c by Piotr Antonik.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xxii, 171 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
505 0 $a Introduction -- Online Training of a Photonic Reservoir Computer -- Backpropagation with Photonics -- Photonic Reservoir Computer with Output Feedback -- Towards Online-Trained Analogue Readout Layer -- Real-Time Automated Tissue Characterisation for Intravascular OCT Scans -- Conclusion and Perspectives.
520 $a This book lies at the interface of machine learning - a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail - and photonics - the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs) Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.
650 0 $a Machine learning. $3 188639
650 0 $a Field programmable gate arrays. $3 246658
650 1 4 $a Physics. $3 179414
650 2 4 $a Optics, Lasers, Photonics, Optical Devices. $3 758151
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer theses. $3 557607
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-91053-6
950 $a Physics and Astronomy (Springer-11651)