Smart Embedded Systems

Advances and Applications

Arun Sinha, Abhishek Sharma, Luiz Alberto Pasini Melek, Daniele Caviglia, Arun Sinha, Abhishek Sharma, Luiz Alberto Pasini Melek, Daniele Caviglia

• Publisher: CRC Press
• Year: 2023
• Print ISBN: 9781032404172
• eBook ISBN: 9781003810353
• Topic: Technology & Engineering
• Subtopic: Computer Engineering
• Index: Technology & Engineering

"Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology. The book journeys through a wide range of topics from healthcare to energy management, autonomous robotics, and wireless communication, showcasing the transformative potential of intelligent embedded systems in these fields. This concise volume introduces readers to innovative techniques and their practical applications, offers a comparative analysis of wireless protocols, and provides efficient resource allocation strategies in IoT-based ecosystems. With real-world examples and in-depth case studies, it serves as an invaluable resource for students and professionals seeking to harness the power of embedded technology to shape our digital future.

Salient Features:

1. The book provides a comprehensive coverage of various aspects of smart embedded systems, exploring their design, implementation, optimization, and a range of applications. This is further enhanced by in-depth discussions on hardware and software optimizations aimed at improving overall system performance.
2. A detailed examination of machine learning techniques specifically tailored for data analysis and prediction within embedded systems. This complements the exploration of cutting-edge research on the use of AI to enhance wireless communications.
3. Real-world applications of these technologies are extensively discussed, with a focus on areas such as seizure detection, noise reduction, health monitoring, diabetic care, autonomous vehicles, and communication systems. This includes a deep-dive into different wireless protocols utilized for data transfer in IoT systems.
4. This book highlights key IoT technologies and their myriad applications, extending from environmental data collection to health monitoring. This is underscored by case studies on the integration of AI and IoT in healthcare, spanning topics from anomaly detection to informed clinical decision-making. Also featured is a detailed evaluation and comparison of different system implementations and methodologies

This book is an essential read for anyone interested in the field of embedded systems. Whether you're a student looking to broaden your knowledge base, researchers looking in-depth insights, or professionals planning to use this cutting-edge technology in real-world applications, this book offers a thorough grounding in the subject.

Table of contents

• Cover Page
• Half Title page
• Title Page
• Copyright Page
• Contents
• Preface
• Editors
• Contributors
• Chapter 1 A reconfigurable FPGA-based epileptic seizures detection system with 144 μs detection time
• Chapter 2 Hardware architecture for denoising of EOG signal using a differential evolution algorithm
• Chapter 3 Implementation considerations for an intelligent embedded E-health system and experimental results for EEG-based activity recognition
• Chapter 4 Embedded and computational intelligence for diabetic healthcare: An overview
• Chapter 5 A semi-definite programming-based design of a robust depth control for a submersible autonomous robot through state feedback control
• Chapter 6 Embedded system with in-memory compute neuromorphic accelerator for multiple applications
• Chapter 7 Artificial intelligence-driven radio channel capacity in 5G and 6G wireless communication systems in the presence of vegetation: Prospect and challenges
• Chapter 8 Smart cabin for office using embedded systems and sensors
• Chapter 9 Wireless protocols for swarm of sensors: Sigfox, Lorawan, and Nb-IoT
• Chapter 10 Design and test of thermal energy harvester for self-powered autonomous electronic load
• Chapter 11 Managing concept drift in IoT health data streams: A dynamic adaptive weighted ensemble approach
• Chapter 12 GraLSTM: A distributed learning model for efficient IoT resource allocation in healthcare
• Index