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In a remarkable stride for the field of wireless communication, Prof. Rupesh Kumar from the Department of Electronics and Communication Engineering has authored a pivotal book that promises to redefine our understanding of radar and RF systems. The book, entitled “Radar and RF Front End System Designs for Wireless Systems,” is the latest gem in the prestigious “Advances in Wireless Technologies and Telecommunication (AWTT)” series.

With his profound expertise, Prof. Kumar navigates through the complexities of designing state-of-the-art front-end systems, offering readers a treasure trove of knowledge that bridges theory and practical application. This book is set to become an essential read for aspiring engineers and seasoned professionals alike, enriching the academic and industry landscape with its innovative approach.
Join us in celebrating Prof. Kumar’s exceptional contribution to the world of electronics and communication engineering. Dive into the depths of this masterful work and emerge with insights that could shape the future of wireless systems.

About the Book:
Radar and RF Front End System Designs for Wireless Systems delves into the intricate world of wireless technologies, particularly focusing on radar and RF front-end systems. The advent of wireless communication has ushered in a new era of connectivity, revolutionising various sectors including healthcare, smart IoT systems, and sensing applications. In this context, the role of RF front-end systems, with their reconfigurable capabilities, has become increasingly vital. The impetus behind this book stems from the remarkable surge in innovation witnessed in RF front-end systems. Researchers and practitioners alike have contributed a plethora of new configurations and design architectures, paving the way for unprecedented advancements in wireless systems. Our aim with this publication is to provide a platform for researchers to explore both theoretical insights and practical applications, thereby facilitating the dissemination of the latest trends and developments in the field. The contents of this book cover a wide spectrum of topics, ranging from RF frontend antenna systems to the impact of artificial intelligence and machine learning in system design. With contributions from experts in academia and industry, readers can expect a comprehensive exploration of radar and antenna system design, modeling, and measurement techniques. We envision this book serving as a valuable resource for students, researchers, scientists, and industry professionals seeking to deepen their understanding of RF front-end antenna and radar system designs. Whether it’s exploring reconfigurable antenna systems for 5G/6G networks, or delving into radar modelling and signal processing techniques, this book offers insights that are both timely and relevant.

Co-author of the Book:

Shilpa Mehta, a co-author of the book, holds a PhD from Auckland University of Technology, New Zealand. Presently, she serves as a Teaching Assistant at AUT, Auckland. Shilpa received the Summer Doctoral Research Scholarship for her PhD work. Her research spans across projects such as Radio Frequency Integrated Circuits, RF front ends, Optimization, Internet of Things, Wireless Communication, Artificial Intelligence, Healthcare, Radars, Software-defined Radios, and Smart Cities.

For the Book Chapter Publication, Click the Link

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The Department of Computer Science and Engineering is proud to announce the acceptance of the book chapter titled, Dielectric Characterization of Ovine Heart Tissues at Terahertz Frequencies via Machine Learning: A Use Case for in-vivo Wireless Nano-Communication in the book, “Edge-Enabled 6G Networking: Foundations, Technologies, and Applications.” The book chapter by Dr Manjula R and her students, Ms NSK Sarayu, Ms N Sai Sruthi, Ms D Samaya, and Mr K Tarun Teja from the department caters to UG/PG and PhD students, educational institutions, and medical healthcare sectors. Dr Manjula’s research doesn’t just underscore the significance of understanding the dielectric properties of heart tissues but also highlights the transformative potential of machine learning in predicting, diagnosing and offering therapeutic interventions equipped with real-time monitoring capabilities. The research also lays the groundwork for future advancements in this field, facilitating the development of more efficient and reliable in-vivo sensing technologies.

Abstract of the Book Chapter:

A new generation of sensing, processing, and communicating devices at the size of a few cubic micrometers are made possible by nanotechnology. Such tiny devices will transform healthcare applications and open up new possibilities for in-body settings. A thorough understanding of the in-vivo channel characteristics is essential to achieve efficient communication between the nanonodes floating in the circulatory system (here, it is the heart) and the gateway devices fixed in the skin. This entails one to have accurate knowledge on the dielectric properties (permittivity and conductivity) of cardiac tissues in terahertz band (0.1 to 10 THz). This research examines the strength of the machine learning models in accurate calculation of the dielectric properties of the cardiac tissues. Initially, we generate the data using 3-pole Debye Model and then use machine learning models (Linear Regression, Polynomial Regression, Gradient Boosting, and KNN), on this data, to estimate the dielectric properties. We compare the values predicted by machine learning models with those given by the analytical model. Our investigation shows that the Gradient Boosting method has better prediction performance. Further, we have also validated these results using Origin software employing curve fitting technique. In addition, the research also contributes to the study of data expansion by predicting unknown data based on available experimental data, emphasizing the broader applicability of machine learning in biomedical research. The study’s conclusions enhance areas like non-invasive sensing in the context of 6G, which may improve data and monitoring in a networked healthcare environment.

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