| |  Online-Ressource |
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| Verfasst von: | Antonik, Piotr [VerfasserIn]  |
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| Titel: | Application of FPGA to Real‐Time Machine Learning |
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| Titelzusatz: | Hardware Reservoir Computers and Software Image Processing |
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| Verf.angabe: | by Piotr Antonik |
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| Verlagsort: | Cham |
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| Verlag: | Springer |
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| Jahr: | 2018 |
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| Umfang: | Online-Ressource (XXII, 171 p. 68 illus., 8 illus. in color, online resource) |
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| Gesamttitel/Reihe: | Springer Theses, Recognizing Outstanding Ph.D. Research |
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| | SpringerLink : Bücher |
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| | Springer eBook Collection |
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| Fussnoten: | "Doctoral thesis accepted by the Université libre de Bruxelles, Brussels, Belgium ; Includes bibliographical references |
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| Hochschulschrift: | Dissertation, Université libre de Bruxelles |
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| ISBN: | 978-3-319-91053-6 |
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| Abstract: | 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. |
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| | 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. |
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| DOI: | doi:10.1007/978-3-319-91053-6 |
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| URL: | Resolving-System ; Resolving-System: https://doi.org/10.1007/978-3-319-91053-6 |
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| | Volltext: http://dx.doi.org/10.1007/978-3-319-91053-6 |
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| | DOI: https://doi.org/10.1007/978-3-319-91053-6 |
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| Schlagwörter: | (s)Künstliche Intelligenz / (s)Maschinelles Lernen / (s)Echtzeitverarbeitung / (s)Photonik / (s)Optischer Computer / (s)Neuronales Netz / (s)Field programmable gate array / (s)Chaotisches System / (s)Bildgebendes Verfahren / (s)Optische Kohärenztomografie |
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| Datenträger: | Online-Ressource |
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| Dokumenttyp: | Hochschulschrift |
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| Sprache: | eng |
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| Bibliogr. Hinweis: | Erscheint auch als : Druck-Ausgabe |
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| K10plus-PPN: | 1657020312 |
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| Lokale URL UB: |  Zum Volltext |
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Application of FPGA to Real‐Time Machine Learning / Antonik, Piotr [VerfasserIn]; 2018 (Online-Ressource)
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