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010   $a3-319-91053-1
024 7 $a10.1007/978-3-319-91053-6
035   $a(CKB)4100000004243982
035   $a(DE-He213)978-3-319-91053-6
035   $a(MiAaPQ)EBC5398382
035   $a(PPN)227399382
035   $a(EXLCZ)994100000004243982
100   $a20180518d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplication of FPGA to Real?Time Machine Learning $eHardware Reservoir Computers and Software Image Processing /$fby Piotr Antonik
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XXII, 171 p. 68 illus., 8 illus. in color.) 
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
311   $a3-319-91052-3 
320   $aIncludes bibliographical references.
327   $aIntroduction -- 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.
330   $aThis book lies at the interface of machine learning ? a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail ? and photonics ? the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aLasers
606   $aPhotonics
606   $aOptical data processing
606   $aComputational intelligence
606   $aArtificial intelligence
606   $aOptics, Lasers, Photonics, Optical Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/P31030
606   $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aLasers.
615  0$aPhotonics.
615  0$aOptical data processing.
615  0$aComputational intelligence.
615  0$aArtificial intelligence.
615 14$aOptics, Lasers, Photonics, Optical Devices.
615 24$aImage Processing and Computer Vision.
615 24$aComputational Intelligence.
615 24$aArtificial Intelligence.
676   $a621.36
700   $aAntonik$b Piotr$4aut$4http://id.loc.gov/vocabulary/relators/aut$0833854
906   $aBOOK
912   $a9910392721503321
996   $aApplication of FPGA to Real?Time Machine Learning$92518841
997   $aUNINA