Optical Fiber Sensors and AI

Exploring the Fusion
Erscheinungsjahr 2025
ISBN: 978-981-966157-2
Verlag: Springer Nature Singapore

Buch, Englisch, 196 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 487 g

Reihe: Progress in Optical Science and Photonics

This book highlights the exciting developments in optical fiber sensors and how artificial intelligence (AI) is boosting their performance and applications. It starts with an easy-to-understand introduction to the basics of optical fiber sensors and their many uses. Then, it moves on to the latest technological advancements, showing how AI is making these sensors smarter and more efficient. The book contains chapters demonstrating how machine learning contributes to real-time data analysis and how deep learning enhances sensor systems. There is also a focus on designing better sensor networks with the help of AI. The book explains how combining AI with the Internet of Things (IoT) and optical fiber sensors can create smart infrastructure solutions. Real-world case studies illustrate how AI-enhanced fiber-optic sensors are benefiting fields like healthcare and environmental monitoring. The book wraps up with a look at future trends and challenges in the world of AI-powered optical fiber sensing. This book is perfect for researchers, engineers, and anyone interested in the powerful combination of AI and optical fiber technology. It provides valuable insights into how these technologies can work together to create innovative and practical solutions.

Zielgruppe

Research

Autoren/Hrsg.

Bhardwaj, Vanita

Rai, Amit

Kumar, Santosh

Kishor, Kamal

Fachgebiete

• Naturwissenschaften Chemie Analytische Chemie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Lichtwellenleiter

Inhaltsverzeichnis

1. Introduction to Optical Fiber Sensors: Fundamentals and Applications.- 2. Advancements in Optical Fiber Sensor Technology.- 3. The Role of Artificial Intelligence in Enhancing Optical Fiber Sensors.- 4. AI Techniques for Signal Processing in Optical Fiber Sensors.- 5. Machine Learning for Real-Time Data Analysis in Fiber Optic Sensing.

Über Autor(innen)

Dr. Vanita Bhardwaj is Assistant Professor at the Vivekananda Institute of Professional Studies. She earned her Ph.D. from IIT ISM Dhanbad. Her research focuses on developing innovative optical fiber sensors using interferometer principles. Dr. Bhardwaj’s research extends to the development of optical fiber sensors encompassing pH sensing, refractive index detection, and temperature measurement. Her multidisciplinary approach explores novel applications in fields such as environmental monitoring. 

Dr. Santosh Kumar received his Ph.D. from IIT (ISM) Dhanbad, India. He was with Liaocheng University, Liaocheng, China, from 2018 to 2023. He is currently Professor with the Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation,  Vaddeswaram, India. With expertise in electronics, communications engineering, and physics, his research focuses on WaveFlex biosensors, fiber-optic sensors, photonics and plasmonic devices, nano and biophotonics, waveguides, interferometers, and the Internet of Things.

Dr. Kamal Kishor is currently working as Assistant Professor in the Applied Physics Department at Delhi Technological University. He received his Ph.D. from the University of Delhi, India. He is currently working in the areas of optical fiber communication system, photonics crystal fibers, metamaterials based antenna, sensors, nano photonics devices, etc.

Dr. Amit Rai is Distinguished Researcher currently working in the Intelligent System Laboratory at Pukyong National University, Busan, South Korea. He obtained his Ph.D. in electrical engineering from the Indian Institute of Technology (ISM) Dhanbad. His research primarily focuses on deep learning, machine learning, and their applications in renewable energy forecasting and Hydrogen production analysis. His notable contributions include developing innovative hybrid deep learning models for solar and wind power forecasting, evidenced by numerous publications in high-impact international journals and conferences.