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Full Description
This book explores the role of embedded AI in revolutionizing industries such as healthcare, transportation, manufacturing, and retail. It begins by introducing the fundamentals of AI and embedded systems and specific challenges and opportunities. A key focus of this book is developing efficient and effective algorithms and models for embedded AI systems, as embedded systems have limited processing power, memory, and storage. It discusses a variety of techniques for optimizing algorithms and models for embedded systems, including hardware acceleration, model compression, and quantization.
Key features:
Explores security experiments in emerging post‑CMOS technologies using AI, including side channel attack‑resistant embedded systems
Discusses different hardware and software platforms available for developing embedded AI applications, as well as the various techniques used to design and implement these systems
Considers ethical and societal implications of embedded AI vis‑a‑vis the need for responsible development and deployment of embedded AI systems
Focuses on application‑based research and case studies to develop embedded AI systems for real‑life applications
Examines high‑end parallel systems to run complex AI algorithms and comprehensive functionality while maintaining portability and power efficiency
This reference book is for students, researchers, and professionals interested in embedded AI and relevant branches of computer science, electrical engineering, or artificial intelligence.
Contents
Section A: Overview of Embedded Systems and Artificial Intelligence 1. Unleashing Intelligence at the Edge: Exploring Machine Learning in Embedded Systems 2. Fusion of Edge Computing in AI-Enabled Embedded Technologies 3. Developing Edge AI for Embedded Systems 4. AI at the Edge: Merging Intelligence and Distributed Computing SECTION B: Case Studies and Practical Applications of AI-enabled Embedded Systems 5. Transformative Impact of AI-Enabled Embedded Systems in Financial Services: Case Studies and Practical Applications 6. Embedded AI Approaches for Multi-organ Critical Care Diagnostics Support and Decision making - Current trends and Emerging Scenarios 7. Embedded AI-Based Approaches for Skin Cancer Detection: Machine Learning Techniques and Applications 8. Artificial Intelligence and Automated Deep Learning for Medical Imaging 9. A comprehensive review on Embedded systems security using
Machine Learning 10. Embedding Business Analysis for Successful AI-Powered Digital Transformation 11. AI in Implementation of EDEEC protocol for 4-Level Scalable
Heterogeneous Wireless Sensor Networks 12. Side Channel Attack-Resistant Embedded Systems 13. Smart Irrigation System using IoT-based devices 14. Revolutionizing Healthcare from Inside - Leveraging Expanded Reality with Ingestible Sensors 15. Object Detection using Opencv 16. Analytical Study of Dominating features of Intelligent Controller over Conventional Controller SECTION C: Ethical Considerations in Embedded AI 17. Data Security and Ethical Considerations in Embedded AI Systems 18. Security of Social Media Content for AI-Embedded Systems: Comparative Analysis
