Pattern Recognition Techniques in Gas Sensing

Erscheinungsjahr 2026
ISBN: 978-0-443-44279-7
Verlag: Elsevier Science

Buch, Englisch, 325 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

Pattern Recognition Techniques in Gas Sensing overviews 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 and future directions.

Autoren/Hrsg.

Bhadola, Pradeep

Chaudhary, Vishal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Naturwissenschaften Chemie Chemie Allgemein
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Geowissenschaften Geologie Geologie

Inhaltsverzeichnis

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

Über Autor(innen)

Bhadola, Pradeep
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.

Chaudhary, Vishal
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.