Note: Intro -- Contents -- Abstract -- 1 Introduction -- References -- 2 Review: Prediction of Respiratory Motion -- 2.1…Tools for Measuring Target Position During Radiotherapy -- 2.1.1 Radiographs -- 2.1.2 Fiducial Markers -- 2.1.3 Fluoroscopy -- 2.1.4 Computed Tomography -- 2.1.5 Magnetic Resonance Imaging -- 2.1.6 Optical Imaging -- 2.2…Tracking-Based Delivery Systems -- 2.2.1 Linear Accelerator -- 2.2.2 Multileaf Collimator -- 2.2.3 Robotic Couch -- 2.3…Prediction Algorithms for Respiratory Motion -- 2.3.1 Model-Based Prediction Algorithms -- 2.3.1.1 Linear Prediction -- 2.3.1.2 Kalman Filter -- 2.3.1.3 Sinusoidal Model -- 2.3.1.4 Finite State Model -- 2.3.1.5 Autoregressive Moving Average Model -- 2.3.1.6 Support Vector Machine -- 2.3.1.7 Hidden Markov Model -- 2.3.2 Model-Free Prediction Algorithms -- 2.3.2.1 Adaptive Filters -- 2.3.2.2 Artificial Neural Network -- 2.3.3 Hybrid Prediction Algorithms -- 2.3.3.1 Adaptive Neuro-Fuzzy Inference System -- 2.3.3.2 Hybrid Model with Adaptive Filter and Nonlinear Model -- 2.3.3.3 Interacting Multiple Model Filter -- 2.3.3.4 Hybrid Extended Kalman Filter -- 2.4…Open Questions for Prediction of Respiratory Motion -- 2.4.1 Changes of Respiratory Patterns -- 2.4.2 Tumor Deformation and Target Dosimetry -- 2.4.3 Irregular Pattern Detection -- 2.5…Summary -- References -- 3 Phantom: Prediction of Human Motion with Distributed Body Sensors -- 3.1…Introduction -- 3.2…Related Work -- 3.2.1 Kalman Filter -- 3.2.2 Interacting Multiple Model Framework -- 3.2.3 Cluster Number Selection Using Gaussian Mixture Model and Expectation-Maximization Algorithm -- 3.3…Proposed Grouping Criteria with Distributed Sensors -- 3.3.1 Collaborative Grouping with Distributed Body Sensors -- 3.3.2 Estimated Parameters Used for Interacting Multiple Model Estimator -- 3.4…Sensors Multi-Channel IMME: Proposed System Design. , 3.4.1 MC Mixed Initial Condition and the Associated Covariance -- 3.4.2 MC Likelihood Update -- 3.4.3 Switching Probability Update -- 3.4.4 Feedback from Switching Probability Update to Stage 1 for Grouping Criteria with Distributed Sensors -- 3.4.5 Combination of MC Conditioned Estimates and Covariance -- 3.4.6 Computational Time -- 3.5…Experimental Results -- 3.5.1 Motion Data -- 3.5.2 Collaborative Grouping Initialization -- 3.5.3 Comparison of Grouping Methods with Other Techniques -- 3.5.4 Multi-Channel IMME -- 3.5.5 Prediction Overshoot -- 3.5.6 Computational Time -- 3.6…Summary -- References -- 4 Respiratory Motion Estimation with Hybrid Implementation -- 4.1…Introduction -- 4.2…Related Work -- 4.2.1 Recurrent Neural Network -- 4.2.2 Extended Kalman Filter for Recurrent Neural Networks -- 4.3…Multi-Channel Coupled EKF-RNN -- 4.3.1 Decoupled Extended Kalman Filter -- 4.3.2 Hybrid Estimation Based on EKF for Neural Network -- 4.3.3 Optimized Group Number for Recurrent Multilayer Perceptron -- 4.3.4 Prediction Overshoot Analysis -- 4.3.5 Comparisons on Computational Complexity and Storage Requirement -- 4.4…Experimental Results -- 4.4.1 Motion Data Captured -- 4.4.2 Optimized Group Number for RMLP -- 4.4.3 Prediction Overshoot Analysis -- 4.4.4 Comparison on Estimation Performance -- 4.4.5 Error Performance Over Prediction Time Horizon -- 4.4.6 Comparisons on Computational Complexity -- 4.5…Summary -- References -- 5 Customized Prediction of Respiratory Motion -- 5.1…Introduction -- 5.2…Prediction Process for Each Patient -- 5.3…Proposed Filter Design for Multiple Patients -- 5.3.1 Grouping Breathing Pattern for Prediction Process -- 5.3.2 Neuron Number Selection -- 5.4…Experimental Results -- 5.4.1 Breathing Motion Data -- 5.4.2 Feature Selection Metrics -- 5.4.3 Comparison on Estimation Performance. , 5.4.4 Prediction Accuracy with Time Horizontal Window -- 5.4.5 Prediction Overshoot Analysis -- 5.4.6 Comparisons on Computational Complexity -- 5.5…Summary -- References -- 6 Irregular Breathing Classification from Multiple Patient Datasets -- 6.1…Introduction -- 6.2…Related Work -- 6.2.1 Expectation--Maximization Based on Gaussian Mixture Model -- 6.2.2 Neural Network -- 6.3…Proposed Algorithms on Irregular Breathing Classifier -- 6.3.1 Feature Extraction from Breathing Analysis -- 6.3.2 Clustering of Respiratory Patterns Based on EM -- 6.3.3 Reconstruction Error for Each Cluster Using NN -- 6.3.4 Detection of Irregularity Based on Reconstruction Error -- 6.4…Evaluation Criteria for Irregular Breathing Classifier -- 6.4.1 Sensitivity and Specificity -- 6.4.2 Receiver Operating Characteristics -- 6.5…Experimental Results -- 6.5.1 Breathing Motion Data -- 6.5.2 Selection of the Estimated Feature Metrics ( \hat{x} ) -- 6.5.3 Clustering of Respiratory Patterns Based on EM -- 6.5.4 Breathing Pattern Analysis to Detect Irregular Pattern -- 6.5.5 Classifier Performance -- 6.6…Summary -- References -- 7 Conclusions and Contributions -- 7.1…Conclusions -- 7.1.1 Hybrid Implementation of Extended Kalman Filter -- 7.1.2 Customized Prediction of Respiratory Motion with Clustering -- 7.1.3 Irregular Breathing Classification from Multiple Patient Datasets -- 7.2…Contributions -- Appendix A -- A.1…Acronyms Definitions -- A.2…Symbol Definitions -- A.2…Symbol Definitions -- Appendix B -- B.1…Matlab Codes for Neural Network -- B.2…Matlab Codes for Adaptive Neural Network -- B.3…Matlab Codes for Kalman Filter -- B.4…Matlab Codes for Decoupled Extended Kalman Filter -- B.5…Matlab Codes for Feature Extraction -- B.6…Matlab Codes for Reconstruction Error -- B.7…Matlab Codes for Irregular Detection -- B.8…Matlab Codes for Detection of True Positive and True Negative. , B.9…Matlab Codes for ROC Curves.

Note: Intro -- Preface to the Series -- Foreword -- Preface -- Contents -- Abbreviations -- 1 Global Status and Economic Importance of Mungbean -- Abstract -- 1.1 Introduction -- 1.2 Mungbean Market Segments and Varietal Requirements -- 1.2.1 Use of Dry Grains -- 1.2.2 Bean Sprouts -- 1.2.3 Transparent Noodles and Starch Extraction -- 1.2.4 Bean Paste and Sweets -- 1.2.5 Other Uses -- 1.2.6 Animal Feed -- 1.3 Mungbean Production Per Country -- 1.3.1 India -- 1.3.2 Myanmar -- 1.3.3 China -- 1.3.4 South Asia -- 1.3.5 Southeast Asia -- 1.3.6 Australia -- 1.3.7 East Asia -- 1.3.8 Africa -- 1.3.9 South America -- 1.3.10 Central Asia -- 1.4 Mungbean Improvement Research -- 1.5 Conclusion -- References -- 2 Mungbean Genetic Resources and Utilization -- Abstract -- 2.1 Introduction -- 2.1.1 Taxonomic Classification -- 2.1.2 Mungbean Gene Pool -- 2.2 Mungbean Germplasm Collections and Introductions -- 2.2.1 Collections -- 2.2.2 Introductions -- 2.3 Process to Access Genetic Resources -- 2.4 Mungbean Germplasm Characterization and Evaluation -- 2.5 Development of Mungbean Core and Mini-Core -- 2.6 Mungbean Varietal Development Programs -- 2.7 Mungbean Crop Wild Relatives (CWRs) and Their Utilization -- 2.8 Mungbean Genomic Resources and Their Importance -- 2.9 Future Prospects and Challenges -- References -- 3 Taxonomy of Mungbean and Its Relatives -- Abstract -- 3.1 Introduction -- 3.2 Genus Vigna Savi -- 3.3 Subgenus Ceratotropis (Piper) Verdcourt -- 3.3.1 Section Ceratotropis N. Tomooka et Maxted -- 3.3.1.1 Vigna radiata (L.) Wilczek -- 3.3.2 New Species of the Subgenus Ceratotropis -- 3.3.2.1 Vigna sathishiana A. P. Balan et S. V. Predeep -- 3.3.2.2 Vigna konkanensis Latha, K. V. Bhat, I. S. Bisht, Scariah, Joseph John et Krishnaraj -- 3.3.2.3 Vigna pandeyana R. D. Gore, S. P. Gaikwad et S. D. Randive. , 3.3.2.4 Vigna yadavii S. P. Gaikwad, R. D. Gore, S. D. Randive et K. U. Garad -- 3.4 Gene Pool Concept for Mungbean -- 3.5 Stress Tolerance of Mungbean and Its Relatives -- 3.6 Future Perspectives -- References -- 4 Classical Genetics and Traditional Breeding in Mungbean -- Abstract -- 4.1 Introduction -- 4.2 Cytogenetics -- 4.3 Nuclear DNA Content -- 4.4 Floral Biology -- 4.5 Genetics of Traits -- 4.5.1 Plant Type and Growth Habit -- 4.5.2 Pigmentation -- 4.5.3 Stem Traits -- 4.5.4 Leaf Traits -- 4.5.5 Inflorescence/Flower Traits -- 4.5.6 Pod Traits -- 4.5.7 Seed Traits -- 4.5.8 Photoperiod Response -- 4.5.9 Yield Traits -- 4.5.9.1 Biotic and Abiotic Stress Resistance -- 4.6 Breeding Methods -- 4.6.1 Introduction -- 4.6.2 Pure Line Breeding -- 4.6.3 Recombination Breeding -- 4.6.4 Mutagenesis -- 4.7 Conclusion -- References -- 5 Breeding Progress and Future Challenges: Biotic Stresses -- Abstract -- 5.1 Introduction -- 5.2 Major Biotic Stresses -- 5.3 Plant Genetic Resources -- 5.4 Sources of Resistance to Diseases -- 5.5 Sources of Resistance to Insect Pests -- 5.6 Genetic Basis of Resistance -- 5.7 Breeding Methods and Strategies -- 5.7.1 Selection -- 5.7.2 Hybridization -- 5.7.3 Intraspecific Hybridization -- 5.7.4 Interspecific Hybridization -- 5.7.5 Mutation Breeding -- 5.8 Impact of Resistance Breeding -- 5.9 Future Outlook -- References -- 6 Breeding Progress and Future Challenges: Abiotic Stresses -- Abstract -- 6.1 Introduction -- 6.2 Major Abiotic Stresses -- 6.2.1 Temperature Stress (High and Low) -- 6.2.2 Water Stress (Deficit and Excess) -- 6.2.3 Salinity -- 6.2.4 Other Abiotic Stresses -- 6.3 Sources of Resistance -- 6.4 Genetic Studies -- 6.5 Breeding -- 6.6 Future Prospects -- References -- 7 Breeding Progress and Future Challenges-Nutritional Quality -- Abstract -- 7.1 Introduction -- 7.2 Protein -- 7.3 Carbohydrates -- 7.4 Lipids. , 7.5 Vitamins -- 7.6 Iron -- 7.7 Zinc -- 7.8 Other Minerals -- 7.9 Phytic Acid -- 7.10 Other Compounds -- 7.11 Scope for Genetic Improvement in Nutritional Quality: Opportunities and Challenges -- References -- 8 Molecular Marker Resources and Their Application -- Abstract -- 8.1 Introduction-The Most Important Molecular Marker Types for Plant Science -- 8.2 Molecular Markers in Plant Breeding -- 8.3 Molecular Markers of Mungbean-A Brief History -- 8.3.1 The First Molecular Markers for Mungbean Breeding: Markers Associated with Bruchid Resistance -- 8.3.2 Markers for Diversity Analysis in Mungbean -- 8.3.3 Molecular Marker for Cultivar Identification and Hybridity Tests -- 8.3.4 Developing Markers Linked to Traits of Interest -- 8.3.5 Marker-Assisted Selection -- 8.3.6 Pyramiding Multiple Traits in Breeding Lines -- 8.3.7 Genomic Selection -- 8.3.8 Constraints to Successful Marker-Assisted Selection -- References -- 9 Mungbean Genome and Synteny with Other Genomes -- Abstract -- 9.1 Introduction -- 9.2 Status of Mungbean Genome Assembly -- 9.3 Improvement of the Mungbean Gene Model -- 9.4 DNA Methylation Profile of the Mungbean Genome -- 9.5 Genomics of Mungbean Germplasm Collection -- 9.6 Listing Functional Loci in the Mungbean Genome by Translational Genomics -- 9.7 Perspectives -- References -- 10 Genomic Approaches to Biotic Stresses -- Abstract -- 10.1 Introduction -- 10.2 Powdery Mildew Disease -- 10.2.1 Sources of the Resistance to Powdery Mildew -- 10.2.2 Genome Analysis of Powdery Mildew Resistance -- 10.3 Cercospora Leaf Spot Disease -- 10.3.1 Sources and Genetics of the Resistance to Cercospora Leaf Spot -- 10.3.2 Genome Analysis of Cercospora Leaf Spot Resistance -- 10.4 Yellow Mosaic Disease -- 10.4.1 Sources of Resistance to Yellow Mosaic Disease -- 10.4.2 Genetics of the Resistance to Yellow Mosaic Disease. , 10.4.3 Genome Analysis of the Yellow Mosaic Disease -- 10.5 Bruchids -- 10.5.1 Sources of Resistance to Bruchids -- 10.5.2 Biochemical Basis of the Bruchid Resistance -- 10.5.3 Genetics of the Resistance to Bruchids -- 10.5.4 Genome Analysis of the Bruchid Resistance -- 10.6 Bean Bug -- 10.7 New Genomics Approaches for Resistance to Disease and Insect Resistance in Mungbean -- 10.8 Conclusion -- References -- 11 Genomic Approaches to Abiotic Stresses in Mungbean -- Abstract -- 11.1 Introduction -- 11.2 Abiotic Stress in Mungbean -- 11.3 Current Approaches for Enhancing Abiotic Stress Tolerance in Mungbean -- 11.3.1 Conventional Breeding -- 11.3.2 Marker-Assisted Selection -- 11.3.3 Mungbean Genome Sequencing and Molecular Markers -- 11.3.4 QTL Mapping and Genome-Wide Association Studies (GWAS) of Abiotic Stress Tolerance in Mungbean -- 11.3.5 Mungbean Transformation -- 11.3.6 Genome Editing and Other Molecular Technologies to Bring Mungbean Breeding in the Twenty-First Century -- References -- 12 Future Prospects and Challenges -- Abstract -- 12.1 Introduction -- 12.2 Access to Genetic Diversity for Breeding -- 12.3 Stabilize Yields and Adapt Mungbean to More Stressful Environments -- 12.4 Exploit Variation in Phenology to Improve Yields of Short-Duration Mungbean -- 12.5 Improve Yield Potential of Mungbean -- 12.6 Biofortification and Consumption -- 12.7 New Traits for Mungbean Breeding -- 12.8 Genomics for Mungbean Crop Improvement -- 12.9 Improving Trait Mapping -- 12.10 Genomic Prediction and Genomic Selection -- 12.11 Conclusion -- References.