Description

Book Synopsis

Quality Electronic Design (QED)’s landscape spans a vast region where territories of many participating disciplines and technologies overlap. This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.



Table of Contents

Chapter 1. NAND Flash Memory Devices Security Enhancement Based on Physical Unclonable Functions.- Chapter 2. ReRAM based Neuromorphic Computing.- Chapter 3. Flash Technology for VLSI Design.- Chapter 4. Non-volatile memory Technologies:Characteristics,Deployment and Research Challenges.- Chapter 5. Data Analytics and Machine Learning for Coverage Closure.- Chapter 6. Cell-Aware Model Generation by Using Machine Learning.- Chapter 7. Neuromorphic Computing: A Path to Artificial Intelligence through Emulating Human Brains.- Chapter 8. AI for Cybersecurity in Distributed Automotive IoT Systems.- Chapter 9. Ultra-low Power Implementation of Neural Networks Using Inverter-based Memristive Crossbars.- Chapter 10. AI based Hardware Security Methods for Internet-of-Things Applications.- Chapter 11. Enabling Edge Computing Using Emerging Memory Technologies: From Device to Architecture.- Chapter 12. IoT Commercial, Industrial Applications and AI-powered IoT.- Chapter 13. Hardware and System Security - Attacks and Coun- termeasures Against Hardware Trojans.- Chapter 14. FPGA Security: Security Threats from Untrusted FPGA CAD Toolchain.- Chapter 15. DoS Attack Models and Mitigation Frameworks for NoC-based SoCs.- Chapter 16. Defense against security threats with regard to SoC Life Cycle.- Chapter 17. Defect Diagnosis Techniques for Silicon Customer Returns.

Frontiers of Quality Electronic Design (QED): AI,

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A Paperback / softback by Ali Iranmanesh

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    View other formats and editions of Frontiers of Quality Electronic Design (QED): AI, by Ali Iranmanesh

    Publisher: Springer International Publishing AG
    Publication Date: 12/01/2024
    ISBN13: 9783031163463, 978-3031163463
    ISBN10: 303116346X

    Description

    Book Synopsis

    Quality Electronic Design (QED)’s landscape spans a vast region where territories of many participating disciplines and technologies overlap. This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.



    Table of Contents

    Chapter 1. NAND Flash Memory Devices Security Enhancement Based on Physical Unclonable Functions.- Chapter 2. ReRAM based Neuromorphic Computing.- Chapter 3. Flash Technology for VLSI Design.- Chapter 4. Non-volatile memory Technologies:Characteristics,Deployment and Research Challenges.- Chapter 5. Data Analytics and Machine Learning for Coverage Closure.- Chapter 6. Cell-Aware Model Generation by Using Machine Learning.- Chapter 7. Neuromorphic Computing: A Path to Artificial Intelligence through Emulating Human Brains.- Chapter 8. AI for Cybersecurity in Distributed Automotive IoT Systems.- Chapter 9. Ultra-low Power Implementation of Neural Networks Using Inverter-based Memristive Crossbars.- Chapter 10. AI based Hardware Security Methods for Internet-of-Things Applications.- Chapter 11. Enabling Edge Computing Using Emerging Memory Technologies: From Device to Architecture.- Chapter 12. IoT Commercial, Industrial Applications and AI-powered IoT.- Chapter 13. Hardware and System Security - Attacks and Coun- termeasures Against Hardware Trojans.- Chapter 14. FPGA Security: Security Threats from Untrusted FPGA CAD Toolchain.- Chapter 15. DoS Attack Models and Mitigation Frameworks for NoC-based SoCs.- Chapter 16. Defense against security threats with regard to SoC Life Cycle.- Chapter 17. Defect Diagnosis Techniques for Silicon Customer Returns.

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