| 書目名稱 | Electronic Nose: Algorithmic Challenges | | 編輯 | Lei Zhang,Fengchun Tian,David Zhang | | 視頻video | http://file.papertrans.cn/307/306349/306349.mp4 | | 概述 | Provides for the first time efficient algorithmic solutions for dealing with the key challenges in electronic noses.Is a good example of how to make intelligent algorithms work well in hardware system | | 圖書封面 |  | | 描述 | .This book presents the key technology of electronic noses, and systematically describes how e-noses can be used to automatically analyse odours. Appealing to readers from the fields of artificial intelligence, computer science, electrical engineering, electronics, and instrumentation science, it addresses three main areas: First, readers will learn how to apply machine learning, pattern recognition and signal processing algorithms to real perception tasks. Second, they will be shown how to make their algorithms match their systems once the algorithms don’t work because of the limitation of hardware resources. Third, readers will learn how to make schemes and solutions when the acquired data from their systems is not stable due to the fundamental issues affecting perceptron devices (e.g. sensors). ..In brief, the book presents and discusses the key technologies and new algorithmic challenges in electronic noses and artificial olfaction. The goal is to promote the industrial application of electronic nose technology in environmental detection, medical diagnosis, food quality control, explosive detection, etc. and to highlight the scientific advances in artificial olfaction and artif | | 出版日期 | Book 2018 | | 關(guān)鍵詞 | Electronic Nose; Pattern Recognition; Drift Compensation; Odor Recognition; Machine Learning; Gas Sensing | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-2167-2 | | isbn_softcover | 978-981-13-4741-2 | | isbn_ebook | 978-981-13-2167-2 | | copyright | Springer Nature Singapore Pte Ltd. 2018 |
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