Application of FPGA to Real‐Time Machine Learning Hardware Reservoir Computers and Software Image Processing /

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 communication...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Antonik, Piotr (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Σειρά:Springer Theses, Recognizing Outstanding Ph.D. Research,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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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. 
250 |a 1st ed. 2018. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2018. 
300 |a XXII, 171 p. 68 illus., 8 illus. in color.  |b online resource. 
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490 1 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |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 Lasers. 
650 0 |a Photonics. 
650 0 |a Optical data processing. 
650 0 |a Computational intelligence. 
650 0 |a Artificial intelligence. 
650 1 4 |a Optics, Lasers, Photonics, Optical Devices.  |0 http://scigraph.springernature.com/things/product-market-codes/P31030 
650 2 4 |a Image Processing and Computer Vision.  |0 http://scigraph.springernature.com/things/product-market-codes/I22021 
650 2 4 |a Computational Intelligence.  |0 http://scigraph.springernature.com/things/product-market-codes/T11014 
650 2 4 |a Artificial Intelligence.  |0 http://scigraph.springernature.com/things/product-market-codes/I21000 
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776 0 8 |i Printed edition:  |z 9783319910529 
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830 0 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5053 
856 4 0 |u https://doi.org/10.1007/978-3-319-91053-6  |z Full Text via HEAL-Link 
912 |a ZDB-2-PHA 
950 |a Physics and Astronomy (Springer-11651)