Algorithms to Enhance Machine Olfaction Performance:

The field of artificial olfaction, electronic noses (e-noses), has been steadily developed over the last three decades in a wide range of applications e.g., medical diagnostics, environmental control, quality assessment of food and beverage products. Many new technologies involved in this field have been developed by a number of researchers and commercial organizations around the world. Still, many researchers have spent a great deal of effort improving e-nose performance and also extended the use of the e-nose devices, not only for discriminating or classifying different odor samples, but also for quantifying an ingredient of a given odor sample. The goal of this book is to address mainly on two technical areas. First, an implementation of an e-nose signal processing system is developed to improve classification performance for small portable e-nose devices with fast response times and optimal number of array sensor for cost reduction. Second, the combination between advance signal processing and learning machine is developed to odor mixture analysis. This book is intended to readers who have basic background in this area to delve into the more specific advance topics.

Autorentext

Ekachai Phaisangittisagul is currently a lecturer at the department of electrical engineering, Kasetsart University, Bangkok, Thailand. His research interests include machine olfaction, pattern recognition/classification, machine learning, and signal processing.