LEADER 04143nam  22005655  450 
001 9910299303703321
005 20181207061330.0
010   $a981-13-2167-1
024 7 $a10.1007/978-981-13-2167-2
035   $a(CKB)4100000006519844
035   $a(MiAaPQ)EBC5515235
035   $a(DE-He213)978-981-13-2167-2
035   $a(EXLCZ)994100000006519844
100   $a20180911d2018      u|             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aElectronic Nose: Algorithmic Challenges$b[electronic resource] /$fby Lei Zhang, Fengchun Tian, David Zhang
205   $a1st ed. 2018.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2018.
215   $a1 online resource (339 pages)
311   $a981-13-2166-3 
327   $aPart 1 : Overview -- Chapter 1. Introduction -- Chapter 2. Literature Review -- Part 2 : E-nose Odor Recognition and Prediction: Challenge I -- Chapter 3. Heuristic and Bio-inspired Neural Network Model -- Chpater 4. Chaos based Neural Network Optimization Approach -- Chapter 5. Multilayer Perceptrons based Concentration Estimation, etc.
330   $aThis 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 artificial intelligence. The book offers a good reference guide for newcomers to the topic of electronic noses, because it refers to the basic principles and algorithms. At the same time, it clearly presents the key challenges ? such as long-term drift, signal uniqueness, and disturbance ? and effective and efficient solutions, making it equally valuable for researchers engaged in the science and engineering of sensors, instruments, chemometrics, etc.
606   $aOptical pattern recognition
606   $aBiometrics
606   $aBioinformatics
606   $aMedical records$xData processing
606   $aPattern Recognition$3http://scigraph.springernature.com/things/product-market-codes/I2203X
606   $aBiometrics$3http://scigraph.springernature.com/things/product-market-codes/I22040
606   $aComputational Biology/Bioinformatics$3http://scigraph.springernature.com/things/product-market-codes/I23050
606   $aHealth Informatics$3http://scigraph.springernature.com/things/product-market-codes/I23060
615  0$aOptical pattern recognition.
615  0$aBiometrics.
615  0$aBioinformatics.
615  0$aMedical records$xData processing.
615 14$aPattern Recognition.
615 24$aBiometrics.
615 24$aComputational Biology/Bioinformatics.
615 24$aHealth Informatics.
676   $a612.86
700   $aZhang$b Lei$4aut$4http://id.loc.gov/vocabulary/relators/aut$0328167
702   $aTian$b Fengchun$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aZhang$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299303703321
996   $aElectronic Nose: Algorithmic Challenges$92088454
997   $aUNINA