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Full Description
"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:
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.
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.
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.
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.
Contents
Chapter 1
A reconfigurable FPGA based epileptic seizures detection system with 144 µs detection time
Swetha Annangi and Arun Kumar Sinha
Chapter 2
Hardware architecture for denoising of EOG signal using differential evolution algorithm V. H. Prasad Reddy, Gundugonti Kishore Kumar and Puli Kishore Kumar
Chapter 3
Implementation considerations for an intelligent embedded e-Health system and experimental results for EEG-based activity recognition
Stefan Oniga, Iuliu Alexandru Pap and Tamas Majoros
Chapter 4
Embedded and computational intelligence for diabetic healthcare: An overview
Anupama Namburu Aravapalli Rama Satish Bhanu Teja Veeramachaneni, Sneha Edupuganti
Kothamasu Venkata Naga Durga Sai Harshith
Chapter 5
A semi-definite programming based design of a robust depth control for a submersible autonomous robot through state feedback control
Vadapalli Siddhartha and Subhasish Mahapatra
Chapter 6
Embedded system with in-memory compute neuromorphic accelerator for multiple applications
Afroz Fatima and Abhijit Pethe
Chapter 7
Artificial intelligence driven radio channel capacity in 5G, 6G wireless communication system in the presence of vegetation: Prospect and challenges
Sachin Kumar, T. Senthil Siva Subramanian and Kapil Sharma
Chapter 8
Smart cabin for office using embedded systems and sensors
Anirban Dasgupta, Abhranil Das, parishmita Deka and Soham Das
Chapter 9
Wireless protocols for swarm of sensors: Sigfox, Lorawan, and Nb-IoT
Luiz Alberto Pasini Melek
Chapter 10
Design and test of thermal energy harvester for self-powered autonomous electronic load
Arun Kumar Sinha
Chapter 11
Managing concept drift in IoT health data streams: A dynamic adaptive weighted ensemble approach
M. Geetha Pratyusha and Arun Kumar Sinha
Chapter 12
GraLSTM: A distributed learning model for efficient IoT resource allocation in healthcare M. Geetha Pratyusha and Arun Kumar Sinha
