Note: Intro -- Preface -- Organization -- Contents -- Searching Similar Trajectories Based on Shape -- 1 Introduction -- 2 Preliminaries -- 3 Align-Based Trajectory Match -- 3.1 Trajectory Preprocessing -- 3.2 Align-Based Trajectory Match -- 3.3 Distance Calculation -- 4 Symbolic Trajectory Match -- 4.1 Symbolic Representation for Trajectory -- 4.2 Symbol Distance Calculation -- 4.3 Multiple Balanced Symbol Trajectory -- 4.4 Query Processing -- 5 Experimental Study -- 5.1 Experiment Setup -- 5.2 Experiment Results -- 6 Related Work -- 6.1 Trajectory Similarity Measurement -- 6.2 Symbolic Representation -- 7 Conclusion -- References -- Unsupervised Discovery of Disentangled Interpretable Directions for Layer-Wise GAN -- 1 Introduction -- 2 Related Works -- 2.1 Generative Adversarial Networks -- 2.2 Semantic Discovery for GANs -- 2.3 Disentanglement Learning with Orthogonal Regularization -- 3 Methods -- 3.1 Overview -- 3.2 Layer-Wise Semantic Discovering Model -- 3.3 Orthogonal Jacobian Regularization -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Non-trivial Visual Effects -- 4.3 Comparison -- 4.4 Ablation Studies -- 5 Conclusion -- References -- ASNN: Accelerated Searching for Natural Neighbors -- 1 Introduction -- 2 Related Work -- 2.1 K-Nearest Neighbor -- 2.2 Reverse k-Nearest Neighbor -- 2.3 Natural Neighbor -- 3 Proposed Method -- 3.1 Motivation and Theory -- 3.2 Accelerated Search of Natural Neighbors Algorithm -- 3.3 Complexity Analysis -- 4 Experimental Evaluation -- 4.1 Results on Synthetic Datasets -- 4.2 Results on Real-World Datasets -- 5 Conclusions -- References -- A Recommendation Algorithm for Auto Parts Based on Knowledge Graph and Convolutional Neural Network -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Knowledge Graph -- 3.2 KGCNUI Layer -- 3.3 Learning Algorithm -- 4 Experiment -- 4.1 Datasets. , 4.2 Baselines -- 4.3 Experiments Setup -- 4.4 Results -- 5 Conclusion -- References -- Identifying Urban Functional Regions by LDA Topic Model with POI Data -- 1 Introduction -- 2 Related Works -- 3 Region Functions Based on LDA Topic Model -- 3.1 Problem Statement -- 3.2 Embedded Representation of Blocks -- 3.3 Functional Region Identification -- 4 Experiment -- 4.1 Experimental Settings -- 4.2 Experimental Results and Analysis -- 5 Conclusion -- References -- A Data-to-Text Generation Model with Deduplicated Content Planning -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Word Embedding -- 3.2 Content Planning -- 3.3 Text Generation -- 3.4 Training and Inference -- 4 Experiment -- 4.1 Dataset -- 4.2 Training Configuration -- 5 Conclusion -- References -- Clustering-Enhanced Knowledge Graph Embedding -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 Methodology -- 4.1 KGE Models with Shared Cluster Embeddings -- 4.2 Loss Function -- 5 Experiment -- 5.1 Data Sets -- 5.2 Baseline -- 5.3 Link Prediction -- 5.4 Triplet Classification -- 6 Conclusion -- Appendix A Model parameters -- References -- FCI: Feature Cross and User Interest Network -- 1 Introduction -- 2 Related Work -- 2.1 Feature Cross Mining -- 2.2 User Behavior Sequence Mining -- 3 Our Approach -- 3.1 Problem Statement -- 3.2 Input and Output -- 3.3 Model Structure -- 3.4 Output Layer -- 3.5 Complexity Analysis -- 4 Experiment -- 4.1 Data Set -- 4.2 Evaluation Metrics -- 4.3 Model Training Details -- 4.4 Hyperparameter Settings -- 4.5 Experimental Comparison -- 4.6 Ablation Experiment -- 5 Conclusion -- References -- Author Index.

Note: Cover -- Copyright -- Contents -- Contributors -- Acknowledgements -- 1. Agriculture and the environment -- 2. Land-use management in China -- 3. The Conversion of Cropland to Forest and Grassland Program -- 4. Are farmers better off? -- 5. Economic efficiency impacts -- 6. Valuing run-off reductions -- 7. Non-market values of environmental changes -- 8. An overall assessment of the CCFGP and policy recommendations -- 9. The way ahead -- Index.

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Session C hairs -- Energy Technologies and Carbon Dioxide Management -- Session I -- Reduction of CO2 Emissions by Chemical Synthesis Processes in the Cement Industry -- Research on Greenhouse Gas Emission of Solid Dust Recovery Using Rotary Hearth Furnace Process in China -- Thermodynamic Analysis of Hydrogen Production from COG-Steam Reforming Process Using Blast Furnace Slag as Heat Carrier -- CO2 Gasification of Catalysts-Loaded Petroleum Coke at Different Grinding M edium -- Session II -- Heat Recovery from High Temperature Slags: Chemical Methods -- Corrosion Fatigue of X46Cr13 in CCS Environment -- Power Generation by Organic Rankine Cycle from Low Temperature Waste Heat of Metallurgical Industry -- Preparation of Ti-Al-V Alloys by Aluminothermic Reaction -- Long Term Prediction of Linz-Donawitz Converter Gas (LDG) in Steel Making Process -- Coke Modification Using Hydrothermal Oxidation Treatment -- Optimization and Management of Byproduct Gas Distribution in Steel Mills Under Time-of-Use (TOU) Electricity Price -- Session III -- Preparation and Characterization of Stearic Acid/SiO2 Nano-encapsulated Phase Change Materials via Sol-gel Method -- Session IV -- Reduction of GHG Emissions through the Conversion of Dairy Waste to Value-Added Materials and Products -- Study on Preparing Ti6Al4V Alloys from V-Ti Bearing Beach Placers -- Particles Flow Behavior Around Tubes in Moving Bed -- Poster Session -- Effect of Microwave Irradiation on Graphitization of Carbon Matrix in Pulverized Coal -- Effect of Microwave Irradiation on Improving Coal Grindability -- High-Temperature Systems for Energy Conversion and Storage -- Ceramic Reliability I -- Thermomechanical Properties of Bilayer La2Zr2O7 Thermal Barrier C oatings. , Recent Advancements in Solid Oxide Fuel Cell Technology II -- An Improvement of SOFC Durability by the Mass Transport Analysis at the Interfaces -- Systems for Energy Conversion and Storage I -- CH4 Reforming by CO2 and O2 Using Ni-M (M= Cu, Fe, Co, Mn, Zn, Cr) Bimetallic Aerogel C atalysts -- Electro-spraying and Combustion of Ethanol in a Micro-scale Combustor under Combined Electric Field -- Author Index -- Subject Index.

Note: Title -- Preface -- Contents -- The Understanding of Meaningful Events in Gesture-Based Interaction -- Introduction -- Events for Spotting and Detecting Gestures -- Location-Based Events -- Posture-Based Events -- Tap and Touch-Based Events -- Custom Events -- Gestures as Events in the Human-Computer Dialogue -- Conclusion -- References -- Apply GPCA to Motion Segmentation -- Introduction -- Previous Works -- Motion Segmentation by GPCA-PDA -- Post-Processing Procedure -- Experiments and Analysis -- Conclusion -- References -- Gait Analysis and Human Motion Tracking -- Introduction -- An Overview of the Proposed Approach -- Phase 1: Establishing the Gait Model -- Phase 2: Recovering Ego-Motion and Scene Structure Using a Dynamic Gait Model -- Estimating Motion Parameters by a MAP Strategy -- EM Algorithm -- Experimental Results -- Synthetic Checked Target: Accuracy Tests -- Synthetic Checked Target: Changing Sample Rates -- Synthetic Human Motion Sequences -- Real Image Sequences -- Summary of the Experimental Results -- Conclusions and Future Work -- References -- Spatio-temporal Dynamic Texture Descriptors for Human Motion Recognition -- Introduction -- Methodology -- Bag of Words Classification -- Feature Detection -- Feature Description -- Classification -- Feature Description: (CS)LBP-TOP and Its Extensions -- Modifications on LBP-TOP -- Experimental Results -- Conclusion -- References -- Efficient Object Localization with Variation-Normalized Gaussianized Vectors -- Introduction -- Gaussianized Vector Representation -- GMM for Feature Vector Distribution -- Discriminative Learning -- Robustness to Within-Class Variation -- Localization with Gaussianized Vector Representation -- Branch-and-Bound Search -- Quality Function -- Quality Bound -- Incorporating Variation-Normalization -- Experiments -- Dataset -- Metric -- Gaussianized Vectors. , Robustness to Within-Class Variation -- Results -- Conclusion -- References -- Fusion of Motion and Appearance for Robust People Detection in Cluttered Scenes -- Introduction -- Methodology -- Generating Hypothesis -- Motion Confidence -- Spatial Motion Descriptor -- Pyramid Bayesian Verification -- Experimental Results -- Data Set -- Detection Results -- Discussion and Conclusions -- References -- Understanding Sports Video Using Players Trajectories -- Introduction -- A Hierarchical Parallel Semi-Markovian Framework (HPaSMM) for Trajectory-Based Sport Video Understanding -- Upper Layer: Semi-Markovian Activity Modeling -- Lower Layer: Parallel Markovian Feature Modeling -- Model Parameter Estimation -- Activity Recognition by Log-Likelihood Maximization -- A Comparison Method: Hierarchical Parallel Hidden Markov Models -- HPaSMM Squash Activity Recognition -- Squash Invariant Feature Representation -- Squash Activity Modeling Using HPaSMM -- Considered Data Sets and Experiments -- Results -- HPaSMM Handball Activity Recognition -- Handball Invariant Feature Representation -- Handball Activity Modeling Using HPaSMM -- Integrating Audio Information: Recognition of Referees Whistles -- Considered Data Sets and Experiments -- Results -- Conclusions, Extensions and Perspectives -- References -- Real-Time Face Recognition from Surveillance Video -- Introduction -- Real-Time Video Surveillance -- Face Recognition -- Real-Time Face Recognition from Surveillance Video -- System Architecture -- Front-End -- Face Detection -- Face Tracking -- Feature Extraction -- Back-End -- Feature Vector -- Feature Vector and Matching -- Results -- Skin Detection -- Face Detection -- Feature Extraction -- Feature Vector and Recognition System -- Conclusions -- References -- Event Understanding of Human-Object Interaction: Object Movement Detection via Stable Changes. , Introduction -- Related Works -- Overview of the Proposed Method -- Attentive Region Detection -- Region-Level Background Subtraction for Object Movement Detection -- Pixel-Level State Detection by Layered Background Model -- Region Tracking -- Object Detection and Background Update -- Region State Detection -- Stable-Change Classification -- Object Detection and Background Update -- Results -- Experiments -- Limitations -- Conclusion -- References -- Survey of Dirac: A Wavelet Based Video Codec for Multiparty Video Conferencing and Broadcasting -- Background of Codecs -- Advantages of Video Event Analysis in Wavelet -- BBC Dirac Video Codec -- Overview of Dirac Video Codec -- Dirac Architecture -- Dirac Encoder -- Dirac Decoder -- Major Modules of Dirac -- Quantization -- Wavelet Transform -- Motion Estimation and Motion Compensation -- Entropy Coding -- Comparison of BBC Dirac Video Codec -- Compression Ratio Test -- PSNR Test -- SSIM Test -- Ongoing and Future Work Regarding BBC Dirac Video Codec -- Scalability -- Computational Complexity Reduction -- Region of Interest Coding (Foveated Image Processing) -- Video Conferencing System Based on Wavelet Transform -- References -- Author Index.

Note: Title -- Preface -- Organization -- Table of Contents -- Biological and Quantum Computing -- Revisiting the Power and Equivalence of One-Way Quantum Finite Automata -- Introduction -- Preliminaries -- Bilinearizing an MM-1QFA -- Regularity of Languages Recognized by MM-1QFA -- Equivalence of Quantum Finite Automata -- Conclusions -- References -- SysMicrO: A Novel Systems Approach for miRNA Target Prediction -- Introduction -- Method -- An Indirect Regulation Consequence Model -- Scheme of SysMicrO -- Formulation -- Result -- Data Derivation -- Testing -- Conclusion and Future Work -- References -- Intelligent Computing in Bioinformatics -- The Exist Local Analytic Solutions of an Iterative Functional Differential Equation -- Introduction -- Analytic Solutions of the Auxiliary Equation -- Analytic solution of Eq. (1.1) -- References -- Xor Perfect Phylogeny Haplotyping in Pedigrees -- Introduction -- Preliminary Definitions -- Perfect Phylogeny Model -- Xor Haplotyping in Population -- Methods -- Results and Discussion -- Conclusion -- References -- Inferring the Transcriptional Modules Using Penalized Matrix Decomposition -- Introduction -- Discover Transcriptional Modules Using PMD -- The Algorithm of PMD -- Identification of Gene Modules Using PMD -- Experimental Results -- Conclusion -- References -- A Novel Computational Method for Predicting Disease Genes Based on Functional Similarity -- Introduction -- Materials and Methods -- Outline -- Measuring Functional Similarity of Two GO Terms -- Evaluating the Relation of Candidate Genes to a Disease -- Test and Comparison -- Test and Results -- Comparison with Existing Methods -- Discussion -- Conclusion and Prospect -- References -- Weighted Locally Linear Embedding for Plant Leaf Visualization -- Introduction -- Local Linear Embedding -- Weighted Local Linear Embedding Algorithm. , Experiments and Results -- Conclusions -- References -- A New Ontological Probabilistic Approach to the Breast Cancer Problem in Semantic Medicine -- Introduction -- Semantic Web Implementation -- RDF and RDFS -- LOD (LINKED OPEN DATA) -- Triplestore, DR2Q, SPARQL -- RDF Application in Medicine -- RDF for Knowledge -- RDF for Life -- Conclusions -- References -- A Comparative Study on Feature Selection in Regression for Predicting the Affinity of TAP Binding Peptides -- Introduction -- Material and Methods -- Dataset and Peptide Representation -- Support Vector Regression and kNN Regression -- Feature Selection Methods -- Results and Discussion -- Performance Comparison on Different Datasets -- The Six Regression Methods for kNNReg and SVR -- References -- Prediction of Protein-Protein Interaction Sites by Using Autocorrelation Descriptor and Support Vector Machine -- Introduction -- Materials and Methods -- Dataset Preparation -- Physicochemical Properties -- Autocorrelation Descriptor -- Performance Measures -- Results and Discussion -- Effect of Different Physicochemical Properties -- Performance Comparison of Autocorrelation Descriptor and Auto Covariance -- Conclusion -- References -- Intelligent Computing in Neuroinformatics andCheminformatics -- Optimal Selection of Support Vector Regression Parameters and Molecular Descriptors for Retention Indices Prediction -- Introduction -- Methods -- Datasets -- Regression Model -- Genetic Algorithm Encoding and Parameters Setup -- The Evaluation Criteria of Regression Performance -- Results and Discussion -- Conclusion -- References -- Intelligent Computing in Computational Biology and Drug Design -- PCOPM: A Probabilistic CBR Framework for Obesity Prescription Management -- Introduction -- Probabilistic Case Based Reasoning -- Integrating Bayesian Networks and CBR -- Leaning -- Retrieval. , PCFOM: A Probabilistic CBR Framework for Obesity Prescription Management -- Conclusion -- References -- Computational Genomics and Proteomics -- Measuring Protein Structural Similarity by Maximum Common Edge Subgraphs -- Introduction -- Method -- Problem Formation -- Constructing P-Graphs -- Constructing Line Graphs -- Constructing Modular Graphs -- Maximum Clique Detection -- Similarity Measure -- Results -- Program and Environment -- Conclusion -- References -- Comparison of DNA Truncated Barcodes and Full-Barcodes for Species Identification -- Introduction -- Methods -- Sampling -- Truncating for DNA Barcodes Sequences -- Analyses for the Statistics of DNA Barcodes -- Ananlysis for Accordance -- Results and Discussion -- Conclusion -- References -- Intelligent Computing in Signal Processing -- Target Extraction from the Military Infrared Image with Complex Texture Background -- Introduction -- Mean-Shift Method -- Mean-Shift Iteration Algorithm -- Infrared Image Smooth According to Mean-Shift -- Target Extraction by Eight Directions Difference Clustering -- Experimental Results -- Conclusion -- References -- The Intelligent Music Editor:Towards an Automated Platform for Music Analysis and Editing -- Introduction -- Related Work -- Analysis: Score-Assisted Music Transcription -- Accurate Onset Detection -- Pitch Estimation -- Automatic Editing -- Reschedule Timing and Determine Pitch -- Time Adjustment and Pitch Shifting -- Evaluation -- Conclusions and Future Work -- References -- A New Hierarchical Key Frame Tree-Based Video Representation Method Using Independent Component Analysis -- Introduction -- Hierarchical Key Frame Tree-Based Video Representation Method -- ICA-Based Feature Extraction -- KD-Tree-Based Key Frame Extraction -- Hierarchical Agglomerative Clustering-Based Hierarchical Key Frame Tree -- Experimental Results -- Conclusion. , References -- An Auditory Oddball Based Brain-Computer Interface System Using Multivariate EMD -- Introduction -- Method of EEG Data Analysis -- Existing EMD Algorithm -- The Proposed n-Variate EMD Algorithm -- Experiment and Results -- Auditory Oddball Experiment -- Averaged Multi-trials Data Result -- Multivariate EMD for Single-Trial Analysis -- Conclusion and Further Works -- References -- HOG-Based Approach for Leaf Classification -- Introduction -- Extraction of Leaf Features -- Histogram of Oriented Gradients (HOG) -- Dimensionality Reduction -- Dataset -- Methodology and Result -- Conclusions -- References -- A Method for ICA with Reference Signals -- Introduction -- Methods -- Overview of Methods for ICA with Reference -- Our Method -- Experiment Results -- Conclusions -- References -- Intelligent Computing in Pattern Recognition -- Fuzzy Algorithm Based on Diffusion Maps for Network Partition -- Introduction -- Framework for Fuzzy Partition of Networks -- The Algorithm -- Numerical Examples -- Ad Hoc Network with 128 Nodes -- Sample Network Generated from the Gaussian Mixture Model -- Karate Club Network -- Conclusions -- References -- Fast ISOMAP Based on Minimum Set Coverage -- Introduction -- ISOMAP -- Minimum Set of Overlapping Neighborhoods -- Experiment -- Conclusions -- References -- A Robust Fusion Method for Vehicle Detection in Road Traffic Surveillance -- Introduction -- Background Initializing and Updating -- Background Initializing -- Background Updating -- Detection of Vehicle -- Background Subtraction -- Inter-frame Difference -- Edge-Based Background Subtraction -- Foreground Fusion Based on D-S Evidence Theory -- Experiment Result -- Conclusions -- References -- A GMM-Based Robust Incremental Adaptation with a For getting Factor for Speaker Verification -- Introduction -- Robust Incremental Adaptation (RIA) Based GMM. , Speaker Registration (SR) -- Speaker Verification (SV) -- Experimental Results -- Conclusions -- References -- Application Oriented Semantic Multi-touch Gesture Description Method -- Introduction -- Related Works -- Application Oriented Semantic Multi Touch Gesture Description Framework -- Application Oriented Semantic Multi Touch Gesture Description Method -- Atomic Gestures -- Atomic Gesture Relationship Operators -- Combined Gestures -- Semantic Interactions -- Brief Summary -- Experiments and Analysis -- Conclusion and Future Work -- References -- Using a Priori Knowledge to Classify in Vivo Images of the Lung -- Introduction -- Feature Extraction and Classification -- Feature Extraction -- Classifier -- Experimental Protocol -- Results -- Conclusions -- References -- A Reversible Data Hiding Scheme Using Even-Odd Embedding Method -- Introduction -- Related Work -- Lossless Compression Technique -- Difference Expansion Technique -- Histogram Shifting Technique -- The Proposed Scheme -- Embedding Phase -- Extraction Phase -- Analysis -- Experimental Result -- Conclusion -- References -- Nonparametric Marginal Fisher Analysis for Feature Extraction -- Introduction -- Nonparametric Marginal Fisher Analysis (NMFA) -- Classification Criterion -- Two Cases Based on Θ -- The Final Objective Function of NMFA -- Experiments -- Experiment on the ORL Face Database -- Experiment on the AR Face Database -- Observations and Evaluations of the Experimental Results -- Conclusion -- References -- On Designing Task-Oriented Intelligent Interfaces: An E-Mail Based Design Framework -- Introduction -- Problem Overview -- Related Work -- Proposed Dialogue-Oriented Design Framework -- Workflow -- Grammar Induction -- System Architecture -- Communication -- Machine Learning -- Case Study -- Conclusions -- References. , Recognition of Leaf Image Based on Ring Projection Wavelet Fractal Feature.

Note: Intro -- Preface -- Contents -- Overview -- 1 Introduction -- Abstract -- 1.1 Background of Electronic Nose -- 1.2 Development in Application Level -- 1.3 Development in System Level -- 1.4 Related Technologies -- 1.5 Outline of the Book -- References -- 2 E-Nose Algorithms and Challenges -- Abstract -- 2.1 Feature Extraction and De-noising Algorithms -- 2.2 Pattern Recognition Algorithms -- 2.3 Drift Compensation Algorithms -- 2.4 Current E-Nose Challenges -- 2.4.1 Discreteness -- 2.4.2 Drift -- 2.4.3 Disturbance -- 2.5 Summary -- References -- E-Nose Odor Recognition and Prediction: Challenge I -- 3 Heuristic and Bio-inspired Neural Network Model -- Abstract -- 3.1 Introduction -- 3.2 Particle Swarm Optimization Models -- 3.2.1 Standard Particle Swarm Optimization (SPSO) -- 3.2.2 Adaptive Particle Swarm Optimization (APSO) -- 3.2.3 Attractive and Repulsive Particle Swarm Optimization (ARPSO) -- 3.2.4 Diffusion and Repulsion Particle Swarm Optimization (DRPSO) -- 3.2.5 Bacterial Chemotaxis Particle Swarm Optimization (PSOBC) -- 3.3 Hybrid Evolutionary Algorithm -- 3.3.1 PSO with Cosine Mechanism -- 3.3.2 Adaptive Genetic Strategy (AGS) -- 3.4 Concentration Estimation Algorithm -- 3.4.1 Multilayer Perceptron -- 3.4.2 Network Optimization -- 3.5 Experiments -- 3.5.1 Experimental Setup -- 3.5.2 Datasets -- 3.5.3 Concentration Estimation -- 3.6 Results and Discussion -- 3.6.1 Experimental Results -- 3.6.2 Computational Efficiency -- 3.6.3 Discussion -- 3.7 Summary -- References -- 4 Chaos-Based Neural Network Optimization Approach -- Abstract -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Electronic Nose -- 4.2.2 Data Acquisition -- 4.2.3 Back-Propagation Neural Network -- 4.2.4 Mutative Scale Chaotic Sequence Optimization -- 4.2.5 Standard Particle Swarm Optimization (SPSO) -- 4.2.6 Parameter Settings -- 4.2.7 On-Line Usage. , 4.3 Results and Discussion -- 4.4 Summary -- References -- 5 Multilayer Perceptron-Based Concentration Estimation -- Abstract -- 5.1 Introduction -- 5.2 E-Nose Systems and Data Acquisition -- 5.2.1 Low-Cost Electronic Nose System -- 5.2.2 Experimental Setup -- 5.2.3 Description of Dataset -- 5.3 MLP-Based Quantization Models -- 5.3.1 Single Multi-Input Multi-Output (SMIMO) Model -- 5.3.2 Multiple Multi-Input Single-Output (MMISO) Model -- 5.3.3 Model Optimization -- 5.4 Results and Discussion -- 5.5 Summary -- References -- 6 Discriminative Support Vector Machine-Based Odor Classification -- Abstract -- 6.1 Introduction -- 6.2 Classification Methodologies -- 6.2.1 Euclidean Distance to Centroids (EDC) -- 6.2.2 Simplified Fuzzy ARTMAP Network (SFAM) -- 6.2.3 Multilayer Perceptron Neural Network (MLP) -- 6.2.4 Fisher Linear Discriminant Analysis (FLDA) -- 6.2.5 Support Vector Machine (SVM) -- 6.3 Experiments -- 6.3.1 Experimental Setup -- 6.3.2 Dataset -- 6.3.3 Multi-class Discrimination -- 6.3.4 Data Analysis -- 6.4 Results and Discussion -- 6.4.1 Experimental Results -- 6.4.2 Discussion -- 6.5 Summary -- References -- 7 Local Kernel Discriminant Analysis-Based Odor Recognition -- Abstract -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 PCA -- 7.2.2 KPCA -- 7.2.3 LDA -- 7.3 The Proposed Approach -- 7.3.1 NDA Framework -- 7.3.2 The KPCA Plus NDA Algorithm (KNDA) -- 7.3.3 Multi-class Recognition -- 7.4 Experiments -- 7.4.1 E-Nose System -- 7.4.2 Dataset -- 7.5 Results and Discussion -- 7.5.1 Contribution Rate Analysis -- 7.5.2 Comparisons -- 7.5.3 Computational Efficiency -- 7.6 Summary -- References -- 8 Ensemble of Classifiers for Robust Recognition -- Abstract -- 8.1 Introduction -- 8.2 Data Acquisition -- 8.2.1 Electronic Nose System -- 8.2.2 Experimental Setup -- 8.2.3 E-Nose Data -- 8.3 Methods -- 8.3.1 KPCA-Based Feature Extraction. , 8.3.2 Base Classifiers -- 8.3.3 Multi-class ISVMEN -- 8.4 Results and Discussion -- 8.5 Summary -- References -- E-Nose Drift Compensation: Challenge II -- 9 Chaotic Time Series-Based Sensor Drift Prediction -- Abstract -- 9.1 Introduction -- 9.2 Data -- 9.2.1 Long-Term Sensor Data -- 9.2.2 Discrete Fourier Transform (DFT)-Based Feature Extraction -- 9.3 Chaotic Time Series Prediction -- 9.3.1 Phase Space Reconstruction -- 9.3.2 Prediction Model -- 9.4 Results -- 9.4.1 Chaotic Characteristic Analysis -- 9.4.2 Drift Prediction -- 9.5 Summary -- References -- 10 Domain Adaptation Guided Drift Compensation -- Abstract -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Drift Compensation -- 10.2.2 Extreme Learning Machine -- 10.3 Domain Adaptation Extreme Learning Machine -- 10.3.1 Source Domain Adaptation ELM (DAELM-S) -- 10.3.2 Target Domain Adaptation ELM (DAELM-T) -- 10.4 Experiments -- 10.4.1 Description of Data -- 10.4.2 Experimental Setup -- 10.5 Results and Discussion -- 10.6 Summary -- References -- 11 Domain Regularized Subspace Projection Method -- Abstract -- 11.1 Introduction -- 11.2 Related Work -- 11.2.1 Existing Drift Compensation Approaches -- 11.2.2 Existing Subspace Projection Algorithms -- 11.3 Domain Regularized Component Analysis (DRCA) -- 11.3.1 Mathematical Notations -- 11.3.2 Problem Formulation -- 11.3.3 Model Optimization -- 11.3.4 Remarks on DRCA -- 11.4 Experiments -- 11.4.1 Experiment on Benchmark Sensor Drift Data -- 11.4.2 Experiment on E-Nose Data with Drift and Shift -- 11.4.3 Parameter Sensitivity Analysis -- 11.4.4 Discussion -- 11.5 Summary -- References -- 12 Cross-Domain Subspace Learning Approach -- Abstract -- 12.1 Introduction -- 12.2 Related Work -- 12.2.1 Review of ELM -- 12.2.2 Subspace Learning -- 12.3 The Proposed CdELM Method -- 12.3.1 Notations -- 12.3.2 Model Formulation -- 12.3.3 Model Optimization. , 12.4 Experiments -- 12.4.1 Data Description -- 12.4.2 Experimental Settings -- 12.4.3 Single-Domain Subspace Projection Methods -- 12.4.4 Classification Results -- 12.4.5 Parameter Sensitivity -- 12.5 Summary -- References -- 13 Domain Correction-Based Adaptive Extreme Learning Machine -- Abstract -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Transfer Learning -- 13.2.2 Extreme Learning Machine -- 13.3 The Proposed Method -- 13.3.1 Notations -- 13.3.2 Domain Correction and Adaptive Extreme Learning Machine -- 13.4 Experiments -- 13.4.1 Experiment on Background Interference Data -- 13.4.2 Experiment on Sensor Drift Data -- 13.5 Summary -- References -- 14 Multi-feature Semi-supervised Learning Approach -- Abstract -- 14.1 Introduction -- 14.1.1 Problem Statement -- 14.1.2 Motivation -- 14.2 Related Work -- 14.3 Multi-feature Kernel Semi-supervised Joint Learning Model -- 14.3.1 Notations -- 14.3.2 Model -- 14.3.3 Optimization Algorithm -- 14.3.4 Classification -- 14.3.5 Convergence -- 14.3.6 Computational Complexity -- 14.3.7 Remarks on Optimality Condition -- 14.4 Experiments on Drifted E-Nose Data -- 14.4.1 Description of Data -- 14.4.2 Experimental Setup -- 14.4.3 Parameter Setting -- 14.4.4 Compared Methods -- 14.4.5 Results and Analysis -- 14.5 Experiments on Modulated E-Nose Data -- 14.5.1 Description of Data -- 14.5.2 Experimental Setup -- 14.5.3 Parameter Setting -- 14.5.4 Results and Analysis -- 14.6 Summary -- References -- E-Nose Disturbance Elimination: Challenge III -- 15 Pattern Recognition-Based Interference Reduction -- Abstract -- 15.1 Introduction -- 15.2 Materials and Methods -- 15.2.1 Experimental Platform -- 15.2.2 Sensor Signal Preprocessing -- 15.2.3 E-Nose Data Preparation -- 15.2.4 Feature Selection of Abnormal Odor -- 15.2.5 Genetic Crossover Operator for Solution of Uneven Features. , 15.2.6 Description of Learner-1 Under Multi-class Condition -- 15.2.7 Description of Learner-2 Under Binary Classification Condition -- 15.2.8 Adaptive Counteraction Model -- 15.3 Results and Discussion -- 15.3.1 Recognition Accuracy of Learner-1 and Learner-2 -- 15.3.2 Abnormal Odor Counteraction: Case Study -- 15.3.3 Discussion -- 15.4 Summary -- References -- 16 Pattern Mismatch Guided Interference Elimination -- Abstract -- 16.1 Introduction -- 16.2 Data Acquisition -- 16.3 Proposed PMIE Method -- 16.3.1 Main Idea -- 16.3.2 Pattern Mismatch-Based Interference Elimination (PMIE) -- 16.4 Results and Discussion -- 16.4.1 Data Preprocessing -- 16.4.2 PMIE Training on Dataset 1 -- 16.4.3 Threshold Analysis Based on Dataset 2 -- 16.4.4 Interference Discrimination on Dataset 3 -- 16.4.5 PMIE-Based Interference Elimination Result -- 16.5 Summary -- References -- 17 Self-expression-Based Abnormal Odor Detection -- Abstract -- 17.1 Introduction -- 17.1.1 Background -- 17.1.2 Problem Statement -- 17.1.3 Motivation -- 17.2 Related Work -- 17.3 Self-expression Model (SEM) -- 17.3.1 Model Formulation -- 17.3.2 Algorithm -- 17.4 Extreme Learning Machine-Based Self-expression Model (SE2LM) -- 17.4.1 Model Formulation -- 17.4.2 Algorithm -- 17.5 Experiments -- 17.5.1 Electronic Nose and Data Acquisition -- 17.5.2 Abnormal Odor Detection Based on Dataset 1 -- 17.5.3 Validation of Real-Time Sequence on Dataset 2 -- 17.5.4 Validation of Real-Time Sequence on Dataset 3 -- 17.6 Discussion -- 17.7 Summary -- References -- E-Nose Discreteness Correction: Challenge IV -- 18 Affine Calibration Transfer Model -- Abstract -- 18.1 Introduction -- 18.2 Method -- 18.2.1 Calibration Step -- 18.2.2 Prediction Step -- 18.3 Experiments -- 18.3.1 Electronic Nose Module -- 18.3.2 Gas Datasets -- 18.4 Results and Discussion -- 18.4.1 Sensor Response Calibration. , 18.4.2 Concentration Prediction.

Note: Cover -- Half Title -- Title -- Copyright -- Contents -- Foreword -- Preface -- Authors -- Chapter 1 Introduction -- 1.1 Background and Significance -- 1.1.1 Background of Subpixel Mapping -- 1.1.2 Significance of Subpixel Mapping -- 1.2 Research Status of Subpixel Mapping -- 1.2.1 Initialize-Then-Optimize Subpixel Mapping -- 1.2.2 Soft-Then-Hard Subpixel Mapping -- 1.2.3 Other Types of Subpixel Mapping -- 1.2.4 Research Status of Super-Resolution Technology -- 1.3 Problems in Subpixel Mapping -- 1.4 Main Research Contents and Chapter Arrangement -- References -- Chapter 2 Basic Principles of Subpixel Mapping -- 2.1 Introduction -- 2.2 Spectral Unmixing Method -- 2.2.1 Linear Spectral Unmixing Model -- 2.2.2 Non-linear Spectral Unmixing Model -- 2.3 Theoretical Basis of Spatial Correlation -- 2.4 Processing Flow of Subpixel Mapping -- 2.4.1 Subpixel Sharpening Method -- 2.4.2 Class Allocation Method -- 2.5 Evaluation Method of Subpixel Mapping Accuracy -- 2.6 Summary -- References -- Chapter 3 Subpixel Mapping Based on Single Remote Sensing Image -- 3.1 Introduction -- 3.2 Subpixel Mapping Based on Spatial-Spectral Interpolation -- 3.2.1 Interpolation Problem -- 3.2.2 Existing Subpixel Mapping Based on Interpolation -- 3.2.3 Processing Flow of the Proposed Method -- 3.2.4 Experimental Content and Result Analysis -- 3.3 Subpixel Mapping Based on Hopfield Neural Network With More Supervision Information -- 3.3.1 Traditional Subpixel Mapping Method Based on Hopfield Neural Network -- 3.3.2 Hopfield Neural Network With More Prior Information -- 3.3.3 Experiment Content and Result Analysis -- 3.4 Subpixel Mapping Based on Extended Random Walk -- 3.4.1 Multi-Scale Segmentation Algorithm -- 3.4.2 Extended Random Walk Algorithm -- 3.4.3 Class Allocation Method Based on Object Unit -- 3.4.4 Experimental Content and Result Analysis. , 3.5 Subpixel Mapping Based on Spatial-Spectral Correlation for Spectral Imagery -- 3.5.1 Spatial Correlation -- 3.5.2 Spectral Correlation -- 3.5.3 Spatial-Spectral Correlation Implementation -- 3.5.4 Experimental Content and Result Analysis -- 3.6 Summary -- References -- Chapter 4 Subpixel Mapping Based on Multi-Shift Remote Sensing Images -- 4.1 Introduction -- 4.2 Theoretical Basis -- 4.2.1 Multi-Shift Images Problem -- 4.2.2 Existing Subpixel Mapping Method Based on Multi-Shift Images -- 4.3 Subpixel Mapping Method Based on Multi-Shift With Spatial-Spectral Information -- 4.3.1 Multi-Shift Image With More Spatial-Spectral Information -- 4.3.2 Experiment Content and Result Analysis -- 4.4 Subpixel Mapping Based on the Spatial Attraction Model With Multi-Scale Subpixel Shifted Images -- 4.4.1 Subpixel-Pixel Spatial Attraction Model -- 4.4.2 Subpixel-Subpixel Spatial Attraction Model -- 4.4.3 Spatial Attraction Model With Multi-Scale Subpixel Shifted Image -- 4.4.4 Experiment Content and Result Analysis -- 4.5 Utilizing Parallel Networks to Produce Subpixel Shifted Images With Multi-Scale Spatial-Spectral Information for Subpixel Mapping -- 4.5.1 Multi-Scale Network and Spatial-Spectral Network -- 4.5.2 Multi-Scale Spatial-Spectral Information -- 4.5.3 Experimental Content and Result Analysis -- 4.6 Spatiotemporal Subpixel Mapping by Considering the Point Spread Function Effect -- 4.6.1 Spatial Dependence -- 4.6.2 Temporal Dependence -- 4.6.3 Spatiotemporal Dependence -- 4.6.4 Experimental Content and Result Analysis -- 4.7 Summary -- References -- Chapter 5 Subpixel Mapping of Remote Sensing Image Based on Fusion Technology -- 5.1 Introduction -- 5.2 Soft-Then-Hard Subpixel Mapping Based on Pansharpening Technology -- 5.2.1 Pansharpening Technology -- 5.2.2 STHSRM-PAN -- 5.2.3 Experimental Content and Result Analysis. , 5.3 Subpixel Land Cover Mapping Based on Parallel Processing Path for Hyperspectral Image -- 5.3.1 Fusion Path -- 5.3.2 Deep Learning Path -- 5.3.3 Dual Processing Path -- 5.3.4 Experimental Content and Result Analysis -- 5.4 Subpixel Mapping Based on Multi-Source Remote Sensing Fusion Data for Land Cover Classes -- 5.4.1 Data-Level Fusion -- 5.4.2 Feature Fusion -- 5.4.3 Obtaining Mapping Result -- 5.4.4 Experimental Content and Result Analysis -- 5.5 Summary -- References -- Chapter 6 Remote Sensing Image Subpixel Mapping Based on Classification Then Reconstruction -- 6.1 Introduction -- 6.2 Theoretical Basis -- 6.2.1 Super-Resolution Algorithm -- 6.2.2 Fully Supervised Information Classification Algorithm -- 6.3 Subpixel Mapping Based on MAP Super-Resolution Reconstruction Then Classification -- 6.3.1 Transformed MAP-Based Super-Resolution Reconstruction -- 6.3.2 LSSVM Classification Algorithm -- 6.3.3 Experiment Content and Result Analysis -- 6.4 Subpixel Mapping Based on Pansharpening Then Classification -- 6.4.1 Implementation Steps -- 6.4.2 Experiment Content and Result Analysis -- 6.5 Summary -- References -- Chapter 7 Application of Subpixel Mapping Technology in Remote Sensing Imaging -- 7.1 Introduction -- 7.2 Improving Flood Subpixel Mapping for Multispectral Image by Supplying More Spectral Information -- 7.2.1 Existing SRFIM -- 7.2.2 SRFIM-MSI -- 7.2.3 Experiment Content and Result Analysis -- 7.3 Subpixel Mapping of Urban Buildings Based in Multispectral Image With Spatial-Spectral Information -- 7.3.1 Spaceborne Multispectral Remote Sensing Image -- 7.3.2 Experiment Content and Result Analysis -- 7.4 Multispectral Subpixel Burned-Area Mapping Based on Space-Temperature Information -- 7.4.1 Space Part -- 7.4.2 Temperature Part -- 7.4.3 Implementation of STI -- 7.4.4 Experiment Content and Result Analysis -- 7.5 Summary -- References. , Appendix: Abbreviations -- Content Validity -- Index.

Note: Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Förderkennzeichen BMBF 16ESE0407 , Verbundnummer 01187722

Note: Förderkennzeichen BMBF 02WCL1335B. - Verbund-Nummer 01156782 , Paralleltitel dem englischen Berichtsblatt entnommen , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden , Sprache der Zusammenfassung: Deutsch, Englisch