Pattern Recognition Techniques in Gas Sensing
- 1st Edition - October 1, 2026
- Latest edition
- Authors: Ajit Khosla, Pradeep Bhadola, Vishal Chaudhary
- Language: English
Pattern Recognition Techniques in Gas Sensing provides a comprehensive overview of the methods and technologies used to detect and analyze gases through advanced pattern recogn… Read more
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Description
Description
Pattern Recognition Techniques in Gas Sensing provides a comprehensive overview of the methods and technologies used to detect and analyze gases through advanced pattern recognition approaches. The book begins by introducing the fundamentals of gas sensors and their unique data characteristics, laying the groundwork for understanding the complexities involved in gas detection. It then explores the basics of pattern recognition, detailing various statistical methods that have been traditionally employed to interpret sensor data. The text looks into Bayesian and probabilistic methods, offering insights into their applications for improving gas sensing accuracy. Cluster analysis techniques are examined as tools for grouping sensor responses to identify specific gas patterns. The integration of machine learning in gas sensing is thoroughly discussed, highlighting how these algorithms enhance detection capabilities by learning from complex datasets. Further, the book presents deep learning techniques, showcasing their power in handling large volumes of sensor data and extracting meaningful features for precise gas identification. Data processing techniques essential for preparing and refining sensor outputs are also covered, providing readers with practical knowledge for real-world applications. The book concludes with a forward-looking perspective on emerging trends and future directions in gas sensing, emphasizing the continuous evolution of pattern recognition technologies. This book serves as an essential resource for researchers, engineers, and professionals seeking to advance the field of gas sensing through innovative pattern recognition techniques, fostering more accurate and reliable detection systems.
Key features
Key features
- Incorporates practical examples, codes, and exercises designed to help readers implement the techniques and algorithms using basic programming skills
- Analyses a variety of case studies to demonstrate the use of pattern recognition techniques in a variety of fields, including environmental monitoring, industrial safety, and medical diagnostics
- Examines emerging technologies and trends preparing readers for the future of the field
Readership
Readership
Post-graduate students, researchers, academics, and industry professionals in data science, environmental science, environmental monitoring, engineering, sensor technology, and industrial safety
Table of contents
Table of contents
1. Introduction
2. Sensors and Their Data characteristics
3. Basics of Pattern Recognition
4. Statistical Methods in Gas Sensing
5. Bayesian and Probabilistic Methods
6. Cluster Analysis
7. Machine Learning in Gas Sensing
8. Deep Learning Techniques
9. Data Processing Techniques
10. Future Directions
2. Sensors and Their Data characteristics
3. Basics of Pattern Recognition
4. Statistical Methods in Gas Sensing
5. Bayesian and Probabilistic Methods
6. Cluster Analysis
7. Machine Learning in Gas Sensing
8. Deep Learning Techniques
9. Data Processing Techniques
10. Future Directions
Product details
Product details
- Edition: 1
- Latest edition
- Published: October 1, 2026
- Language: English
About the authors
About the authors
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Ajit Khosla
Ajit Khosla is a distinguished professor in the School of Advanced Materials and Nanotechnology at Xidian University, and Yamagata University. In 2012, he received his doctorate degree in Microelectronics stream from School of Engineering Science, Simon Fraser University (SFU), Canada, where he was awarded the 2012 Dean of Graduate Studies Convocation Medal as one of SFU’s most outstanding graduate students. He is a fellow of the Royal Statistical Society (RSS), the Royal Society of Arts (RSA), Royal Society of Chemistry (RSC), and the Electrochemical Society (ECS). He is the author of over 195 peer reviewed journal papers, 9 books and 4 patents with an h-index of 52 and over 11000 citations. His research network transvers over 51 institutions globally. On more than one occasion he has been invited to speak at United Nations on Data Driven Sensing Systems for Sustainability.
He is founding Editor-in-Chief of Electrochemical Society’s first gold open access journals, ECS Sensors Plus and Associate editor of IEEE Access and IET Nanobiotechnology. He has organized over 50 conferences, mini colloquiums and workshops along with IEEE, ECS, ACS and IoP. His research program is interdisciplinary area of “materials, interphases, and intelligent turnkey systems for sustainability” that bridge over disciplines. He has organized over 50 conferences, mini colloquiums and workshops along with IEEE, ECS, ACS and IoP. He is a long-standing member of IEEE and has been serving as associate editor for IEEE Access. His research program is interdisciplinary in nature that bridges over disciplines, with a focus on development micro-nano-fabricated chemical and biological sensors Turnkey systems with applications in healthcare, environmental monitoring and restoration (fauna and fauna), additive manufacturing, sustainable design and manufacturing, and clean water that address to real world problems, thereby improving quality of life.
Ajit Khosla's extensive expertise in microelectronics and sensor technology, combined with his distinguished academic career and industrial experience, makes him the ideal author for this book. His interdisciplinary research, focused on practical applications in healthcare and environmental monitoring, aligns perfectly with the goals of the book. Additionally, his vast editorial experience, leadership in major conferences, and hands-on industrial experience in developing sensor systems further solidify his role as an authoritative voice in the field.
Affiliations and expertise
Distinguished Professor, School of Advanced Materials and Nanotechnology, Xidian University, and Yamagata University, ChinaPB
Pradeep Bhadola
Pradeep Bhadola is a computational physicist and researcher at the Centre for Theoretical Physics and Natural Philosophy, Mahidol University, Nakhonsawan Campus, Thailand. With over eight years of research experience and a strong teaching background at the postgraduate and doctoral levels, he brings expertise in Statistical Mechanics, Information Theory, Graph Theory, and Data Science. As the leader of the Complexity and Data Science Group, his work focuses on developing computational and mathematical models for complex systems. His research integrates advanced techniques in computational physics and machine learning, making him a valuable contributor to interdisciplinary fields. His extensive experience in Python programming and data-driven modeling aligns seamlessly with the goals of this book. His deep understanding of theoretical and practical approaches to pattern recognition and sensor data analysis ensures a comprehensive perspective on applying modern data science techniques to gas sensing challenges.
Affiliations and expertise
Computational Physicist and Researcher, Centre for Theoretical Physics and Natural Philosophy, Mahidol University, Nakhonsawan Campus, ThailandVC
Vishal Chaudhary
Vishal Chaudhary recently joined Mahidol University, Thailand, after serving as an assistant professor of physics at the University of Delhi since 2015. An expert in sensors for One Health, he focuses on breath-based diagnosis, environmental sensors, and wastewater remediation. He primarily focuses on the interdisciplinary science of complex natural systems linked to sensors, which work on interfaces of physics, chemistry, biology, environment and programming. He has published over 100 peer-reviewed articles, authored four books, and edited three. Recognized among the world's top 2% scientists (2023, 2024), he received the 2023 Materials Today Agents of Change Award founded by Elsevier Foundation. His current work aims to address mental health in academia through research, biosensors, and stigma reduction, especially for LGBTQI+ communities.
Affiliations and expertise
Mahidol University, Thailand