Machine Learning Support Fault (29 results)

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  • Language: English

    Published by Springer, 2023

    3031196406 / 9783031196409

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    Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections

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    Condition: New. In English.

  • Language: English

    Published by Springer, 2024

    3031196414 / 9783031196416

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    Condition: New. 2023rd edition NO-PA16APR2015-KAP.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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    Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK

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  • Language: English

    Published by Springer Mär 2024, 2024

    3031196414 / 9783031196416

    • Softcover

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Taschenbuch. Condition: Neu. Neuware - This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

    • Hardcover

    Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.

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  • Language: English

    Published by Springer, 2024

    3031196414 / 9783031196416

    • Softcover

    Seller: preigu, Osnabrück, Germanypreigu

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    Taschenbuch. Condition: Neu. Machine Learning Support for Fault Diagnosis of System-on-Chip | Patrick Girard (u. a.) | Taschenbuch | xi | Englisch | 2024 | Springer | EAN 9783031196416 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Language: English

    Published by Springer Nature, 2023

    3031196384 / 9783031196386

    • Hardcover

    Seller: Revaluation Books, Exeter, United KingdomRevaluation Books

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    Hardcover. Condition: Brand New. 327 pages. 9.25x6.10x9.21 inches. In Stock.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

    • Hardcover

    Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore

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  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

    • Hardcover

    Seller: Buchpark, Trebbin, GermanyBuchpark

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    Condition: Hervorragend. Zustand: Hervorragend | Seiten: 328 | Sprache: Englisch | Produktart: Bücher | This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

    • Hardcover

    Seller: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermanyBUCHSERVICE / ANTIQUARIAT Lars Lutzer

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    Hardcover. Condition: gut. 2023. Machine Learning Support for Fault Diagnosis of System-on-Chip In deutscher Sprache. pages.

  • Language: English

    Published by Springer Nature B.V., 2023

    3031196406 / 9783031196409

    • Softcover
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    PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Language: English

    Published by Springer Nature B.V., 2023

    3031196406 / 9783031196409

    • Softcover
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    Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK

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    PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Language: English

    Published by Springer, 2024

    3031196414 / 9783031196416

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    • Print on Demand

    Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand

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  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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    Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand

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  • Language: English

    Published by Springer, Berlin, Springer International Publishing, Springer, 2024

    3031196414 / 9783031196416

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    Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques. 316 pp. Englisch.

  • Language: English

    Published by Springer, 2024

    3031196414 / 9783031196416

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    Seller: Majestic Books, Hounslow, United KingdomMajestic Books

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  • Language: English

    Published by Springer, 2024

    3031196414 / 9783031196416

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    Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios

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  • Language: English

    Published by Springer, Berlin|Springer International Publishing|Springer, 2024

    3031196414 / 9783031196416

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    Seller: moluna, Greven, Germanymoluna

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    Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after m.

  • Language: English

    Published by Springer International Publishing, Springer International Publishing Mär 2023, 2023

    3031196384 / 9783031196386

    • Hardcover
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    Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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    Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques. 328 pp. Englisch.

  • Language: English

    Published by Springer, Springer Mär 2024, 2024

    3031196414 / 9783031196416

    • Softcover
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    Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

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    Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 328 pp. Englisch.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

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    Seller: Majestic Books, Hounslow, United KingdomMajestic Books

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  • Language: English

    Published by Springer, Berlin|Springer International Publishing|Springer, 2023

    3031196384 / 9783031196386

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    Seller: moluna, Greven, Germanymoluna

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    Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after m.

  • Language: English

    Published by Springer, 2023

    3031196384 / 9783031196386

    • Hardcover
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    Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios

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  • Language: English

    Published by Springer, Springer Mär 2023, 2023

    3031196384 / 9783031196386

    • Hardcover
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    Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

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    Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 328 pp. Englisch.