Electronic nose : algorithmic challenges

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 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. Front Matter ....Pages i-xv Front Matter ....Pages 1-1 Introduction (Lei Zhang, Fengchun Tian, David Zhang)....Pages 3-9 E-Nose Algorithms and Challenges (Lei Zhang, Fengchun Tian, David Zhang)....Pages 11-20 Front Matter ....Pages 21-21 Heuristic and Bio-inspired Neural Network Model (Lei Zhang, Fengchun Tian, David Zhang)....Pages 23-46 Chaos-Based Neural Network Optimization Approach (Lei Zhang, Fengchun Tian, David Zhang)....Pages 47-60 Multilayer Perceptron-Based Concentration Estimation (Lei Zhang, Fengchun Tian, David Zhang)....Pages 61-77 Discriminative Support Vector Machine-Based Odor Classification (Lei Zhang, Fengchun Tian, David Zhang)....Pages 79-93 Local Kernel Discriminant Analysis-Based Odor Recognition (Lei Zhang, Fengchun Tian, David Zhang)....Pages 95-113 Ensemble of Classifiers for Robust Recognition (Lei Zhang, Fengchun Tian, David Zhang)....Pages 115-131 Front Matter ....Pages 133-133 Chaotic Time Series-Based Sensor Drift Prediction (Lei Zhang, Fengchun Tian, David Zhang)....Pages 135-146 Domain Adaptation Guided Drift Compensation (Lei Zhang, Fengchun Tian, David Zhang)....Pages 147-171 Domain Regularized Subspace Projection Method (Lei Zhang, Fengchun Tian, David Zhang)....Pages 173-191 Cross-Domain Subspace Learning Approach (Lei Zhang, Fengchun Tian, David Zhang)....Pages 193-208 Domain Correction-Based Adaptive Extreme Learning Machine (Lei Zhang, Fengchun Tian, David Zhang)....Pages 209-224 Multi-feature Semi-supervised Learning Approach (Lei Zhang, Fengchun Tian, David Zhang)....Pages 225-245 Front Matter ....Pages 247-247 Pattern Recognition-Based Interference Reduction (Lei Zhang, Fengchun Tian, David Zhang)....Pages 249-264 Pattern Mismatch Guided Interference Elimination (Lei Zhang, Fengchun Tian, David Zhang)....Pages 265-278 Self-expression-Based Abnormal Odor Detection (Lei Zhang, Fengchun Tian, David Zhang)....Pages 279-298 Front Matter ....Pages 299-299 Affine Calibration Transfer Model (Lei Zhang, Fengchun Tian, David Zhang)....Pages 301-321 Instrumental Batch Correction (Lei Zhang, Fengchun Tian, David Zhang)....Pages 323-333 Book Review and Future Work (Lei Zhang, Fengchun Tian, David Zhang)....Pages 335-339