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• 1. Introduction.- 1.1 Brief Overview of Motion Analysis.- 1.2 Statement of the "Motion from Stereo" Problem.- 1.3 Organization of The Book.-
• 2. Uncertainty Manipulation and Parameter Estimation.- 2.1 Probability Theory and Geometric Probability.- 2.2 Parameter Estimation.- 2.2.1 Standard Kalman filter.- 2.2.2 Extended Kalman filter.- 2.2.3 Discussion.- 2.2.4 Iterated ExtendKalman Filter.- 2.2.5 Robustness and Confidence Procedure.- 2.3 Summary.- 2.4 Appendix: Least-Squares Techniques.-
• 3. Reconstruction of 3D Line Segments.- 3.1 Why 3D Line Segments.- 3.2 Stereo Calibration.- 3.2.1 Camera Calibration.- 3.2.2 Epipolar Constraint.- 3.3 Algorithm of the Trinocular Stereovision.- 3.4 Reconstruction of 3D Segments.- 3.5 Summary.-
• 4. Representations of Geometric Objects.- 4.1 Rigid Motion.- 4.1.1 Definition.- 4.1.2 Representations.- 4.2 3D Line Segments.- 4.2.1 Previous Representations and Deficiencies.- 4.2.2 A New Representation.- 4.3 Summary.- 4.4 Appendix: Visualizing Uncertainty.-
• 5. A Comparative Study of 3D Motion Estimation.- 5.1 Problem Statement.- 5.1.1 Line Segment Representations.- 5.1.2 3D Line Segment Transformation.- 5.2 Extended Kalman Filter Approaches.- 5.2.1 Linearization of the Equations.- 5.2.2 Derivation of Rotation Matrix.- 5.3 Minimization Techniques.- 5.4 Analytical Solution.- 5.4.1 Determining the Rotation.- 5.4.2 Determining the Translation.- 5.5 Kim and Aggarwal's method.- 5.5.1 Determining the Rotation.- 5.5.2 Determining the Translation.- 5.6 Experimental Results.- 5.6.1 Results with Synthetic Data.- 5.6.2 Results with Real Data.- 5.7 Summary.- 5.8 Appendix: Motion putation Using the New Line Segment Representation.-
• 6. Matching and Rigidity Constraints.- 6.1 Matching as a Search.- 6.2 Rigidity Constraint.- 6.3 Completeness of the Rigidity Constraints.- 6.4 Error Measurements inn the Constraints.- 6.4.1 Norm Constraint.- 6.4.2 Dot-Product Constraint.- 6.4.3 Triple-Product Constraint.- 6.5 Other Formalisms Rigidity Constraints.- 6.6 Summary.-
• 7. Hypothesize-and-Verify Method for Two 3D View Motion Analysis.- 7.1 General Presentation.- 7.1.1 Search in the Transformation Space.- 7.1.2 Hypothesize-and-Verify Method.- 7.2 Generating Hypotheses.- 7.2.1 Definition and Primary Algorithm.- 7.2.2 Control Strates in Hypothesis Generation.- 7.2.3 Additional Constraints.- 7.2.4 Algorithm of Hypothesis Generation.- 7.3 Verifying Hypothesis.- 7.3.1 Estimating the Initial Rigid Motion.- 7.3.2 Propagating Hyphoteses.- 7.3.3 Choosing the Best Hypothesis.- 7.3.4 Algorithm of Hypothesis Verification.- 7.4 Matching Noisy Segments.- 7.4.1 Version 1.- 7.4.2 Version 2.- 7.4.3 Version 3.- 7.5 Experimental Results.- 7.5.1 Indoor Scenes with a Large Common Part.- 7.5.2 Indoor Scenes with a Small Common Part.- 7.5.3 Rock Scenes.- 7.6 Summary.- 7.7 Appendix: Transforming a 3D Line Segment.-
• 8. Further Considerations on Reducing Complexity.- 8.1 Sorting Data Features.- 8.2 "Good-Enough" Problem.- 1.3 Organization of The Book.-
• 2. Uncertainty Manipulation and Parameter Estimation.- 2.1 Probability Theory and Geometric Probability.- 2.2 Parameter Estimation.- 2.2.1 Standard Kalman filter.- 2.2.2 Extended Kalman filter.- 2.2.3 Discussion.- 2.2.4 Iterated ExtendKalman Filter.- 2.2.5 Robustness and Confidence Procedure.- 2.3 Summary.- 2.4 Appendix: Least-Squares Techniques.-
• 3. Reconstruction of 3D Line Segments.- 3.1 Why 3D Line Segments.- 3.2 Stereo Calibration.- 3.2.1 Camera Calibration.- 3.2.2 Epipolar Constraint.- 3.3 Algorithm of the Trinocular Stereovision.- 3.4 Reconstruction of 3D Segments.- 3.5 Summary.-
• 4. Representations of Geometric Objects.- 4.1 Rigid Motion.- 4.1.1 Definition.- 4.1.2 Representations.- 4.2 3D Line Segments.- 4.2.1 Previous Representations and Deficiencies.- 4.2.2 A New Representation.- 4.3 Summary.- 4.4 Appendix: Visualizing Uncertainty.-
• 5. A Comparative Study of 3D Motion Estimation.- 5.1 Problem Statement.- 5.1.1 Line Segment Representations.- 5.1.2 3D Line Segment Transformation.- 5.2 Extended Kalman Filter Approaches.- 5.2.1 Linearization of the Equations.- 5.2.2 Derivation of Rotation Matrix.- 5.3 Minimization Techniques.- 5.4 Analytical Solution.- 5.4.1 Determining the Rotation.- 5.4.2 Determining the Translation.- 5.5 Kim and Aggarwal's method.- 5.5.1 Determining the Rotation.- 5.5.2 Determining the Translation.- 5.6 Experimental Results.- 5.6.1 Results with Synthetic Data.- 5.6.2 Results with Real Data.- 5.7 Summary.- 5.8 Appendix: Motion putation Using the New Line Segment Representation.-
• 6. Matching and Rigidity Constraints.- 6.1 Matching as a Search.- 6.2 Rigidity Constraint.- 6.3 Completeness of the Rigidity Constraints.- 6.4 Error Measurements inn the Constraints.- 6.4.1 Norm Constraint.- 6.4.2 Dot-Product Constraint.- 6.4.3 Triple-Product Constraint.- 6.5 Other Formalisms Rigidity Constraints.- 6.6 Summary.-
• 7. Hypothesize-and-Verify Method for Two 3D View Motion Analysis.- 7.1 General Presentation.- 7.1.1 Search in the Transformation Space.- 7.1.2 Hypothesize-and-Verify Method.- 7.2 Generating Hypotheses.- 7.2.1 Definition and Primary Algorithm.- 7.2.2 Control Strates in Hypothesis Generation.- 7.2.3 Additional Constraints.- 7.2.4 Algorithm of Hypothesis Generation.- 7.3 Verifying Hypothesis.- 7.3.1 Estimating the Initial Rigid Motion.- 7.3.2 Propagating Hyphoteses.- 7.3.3 Choosing the Best Hypothesis.- 7.3.4 Algorithm of Hypothesis Verification.- 7.4 Matching Noisy Segments.- 7.4.1 Version 1.- 7.4.2 Version 2.- 7.4.3 Version 3.- 7.5 Experimental Results.- 7.5.1 Indoor Scenes with a Large Common Part.- 7.5.2 Indoor Scenes with a Small Common Part.- 7.5.3 Rock Scenes.- 7.6 Summary.- 7.7 Appendix: Transforming a 3D Line Segment.-
• 8. Further Considerations on Reducing Complexity.- 8.1 Sorting Data Features.- 8.2 "Good-Enough" Method.- 8.3 Speeding Up the Hypothesis Generation Process Through Grouping.- 8.4 Finding Clusters Based on Proximity.- 8.5 Finding Planes.- 8.6 Experimental Results.- 8.6.1 Grouping Results.- 8.6.2 Motion Results.- 8.7 Conclusion.-
• 9. Multiple Object Motions.- 9.1 Multiple Object Motions.- 9.2 Influence of Egomotion on Observed Object Motion.- 9.3 Experimental Results.- 9.3.1 Real Scene with Synthetic Moving Objects.- 9.3.2 Real Scene with a Real Moving Object.- 9.4 Summary.-
• 10. Object Recognition and Localization.- 10.1 Model-Based Object Recognition.- 10.2 Adapting the Motion-Determination Algorithm.- 10.3 Experimental Result.- 10.4 Summary.-
• 11. Calibrating a Mobile Robot and Visual Navigation.- 11.1 The INRIA Mobile Robot.- 11.2 Calibration Problem.- 11.3 Navigation Problem.- 11.4 Experimental Results.- 11.5 Integrating Motion Information from Odometry.- 11.6 Summary.-
• 12. Fusing Multiple 3D Frames.- 12.1 System Description.- 12.2 Fusing Segments from Multiple Views.- 12.2.1 Fusing General Primitives.- 12.2.2 Fusing Line Segments.- 12.2.3 Example.- 12.2.4 Summary of the Fusion Algorithm.- 12.3 Experimental Results.- 12.3.1 Example
• 1: Integration of Two Views.- 12.3.2 Example
• 2: Integration of a Long Sequence.- 12.4 Summary.-
• 13. Solving the Motion Tracking Problem: A Framework.- 13.1 Previous Work.- 13.2 Position of the Problem and Primary Ideas.- 13.3 Solving the Motion Tracking Problem: A Framework.- 13.3.1 Outline of the Framework.- 13.3.2 A Pedagogical Example.- 13.4 Splitting or Merging.- 13.5 Handling Abrupt Changes of Motion.- 13.6 Discussion.- 13.7 Summary.-
• 14. Modeling and Estimating Motion Kinematics.- 14.1 The Classical Kinematic Model.- 14.2 Closed-Form Solutions for Some Special Motions.- 14.2.1 Motion with Constant Angular and Translational Velocities.- 14.2.2 Motion with Constant Angular Velocity and Constant Translational Acceleration.- 14.2.3 Motion with Constant Angular Velocity and General Translational Velocity.- 14.2.4 Discussions.- 14.3 Relation with Two-View Motion Analysis.- 14.4 Formulation for the EKF Approach.- 14.4.1 State Transition Equation.- 14.4.2 Measurement Equations.- 14.5 Linearized Kinematic Model.- 14.5.1 Linear Approximation.- 14.5.2 State Transition Equation.- 14.5.3 Measurement Equations.- 14.5.4 Discussions.- 14.6 Summary.-
• 15. Implementation Details and Experimental Results.- 15.1 Matching Segments.- 15.1.1 Prediction of a Token.- 15.1.2 Matching Criterion.- 15.1.3 Reducing the Complexity by Bucketing Techniques.- 15.2 Support of Existence.- 15.3 Algorithm of the Token Tracking Process.- 15.4 Grouping Tokens into Objects.- 15.5 Experimental Results.- 15.5.1 Synthetic Data.- 15.5.2 Real Data with Controlled Motion.- 15.5.3 Real Data with Uncontrolled Motion.- 15.6 Summary.-
• 16. Conclusions and Perspectives.- 16.1 Summary.- 16.2 Perspectives.- Appendix: Vector Manipulation and Differentiation.- A.1 Manipulation of Vectors.- A.2 Differentiation of Vectors.- References.
• (source: Nielsen Book Data)

he problem of analyzing sequences of images to extract three-dimensional T motion and structure has been at the heart of the research in computer vi- sion for many years. It is very important since its success or failure will determine whether or not vision can be used as a sensory process in reactive systems. The considerable research interest in this field has been motivated at least by the following two points: 1. The redundancy of information contained in time-varying images can over- come several difficulties encountered in interpreting a single image. 2. There are a lot of important applications including automatic vehicle driv- ing, traffic control, aerial surveillance, medical inspection and global model construction. However, there are many new problems which should be solved: how to effi- ciently process the abundant information contained in time-varying images, how to model the change between images, how to model the uncertainty inherently associated with the imaging system and how to solve inverse problems which are generally ill-posed. There are of course many possibilities for attacking these problems and many more remain to be explored. We discuss a few of them in this book based on work carried out during the last five years in the Computer Vision and Robotics Group at INRIA (Institut National de Recherche en Informatique et en Automatique).
(source: Nielsen Book Data)

• A high performance matrix multiplication algorithm for MPPs.- Iterative moment method for electromagnetic transients in grounding systems on CRAY T3D.- Analysis of crystalline solids by means of a parallel FEM method.- Parallelization strategies for Tree N-body codes.- Numerical solution of stochastic differential equations on transputer network.- Development of a stencil compiler for one-dimensional convolution operators on the CM-5.- Automatic parallelization of the AVL FIRE benchmark for a distributed-memory system.- 2-D cellular automata and short range molecular dynamics programs for simulations on networked workstations and parallel computers.- Pablo-based performance monitoring tool for PVM applications.- Linear algebra computation on parallel machines.- A neural classifier for radar images.- ScaLAPACK: A portable linear algebra library for distributed memory computers - Design issues and performance.- A proposal for a set of parallel basic linear algebra subprograms.- Parallel implementation of a Lagrangian stochastic particle model of turbulent dispersion in fluids.- Reduction of a regular matrix pair (A, B) to block Hessenberg-triangular form.- Parallelization of algorithms for neural networks.- Paradigms for the parallelization of Branch&Bound algorithms.- Three-dimensional version of the Danish Eulerian Model.- A proposal for a Fortran 90 interface for LAPACK.- ScaLAPACK tutorial.- Highly parallel concentrated heterogeneous computing.- Adaptive polynomial preconditioning for the conjugate gradient algorithm.- The IBM parallel engineering and scientific subroutine library.- Some preliminary experiences with sparse BLAS in parallel iterative solvers.- Load balancing in a Network Flow Optimization code.- User-level VSM optimization and its application.- Benchmarking the cache memory effect.- Efficient Jacobi algorithms on multicomputers.- Front tracking: A parallelized approach for internal boundaries and interfaces.- Program generation techniques for the development and maintenance of numerical weather forecast Grid models.- High performance computational chemistry: NWChem and fully distributed parallel applications.- Parallel ab-initio molecular dynamics.- Dynamic domain decomposition and load balancing for parallel simulations of long-chained molecules.- Concurrency in feature analysis.- A parallel iterative solver for almost block-diagonal linear systems.- Distributed general matrix multiply and add for a 2D mesh processor network.- Distributed and parallel computing of short-range molecular dynamics.- Lattice field theory in a parallel environment.- Parallel time independent quantum calculations of atom diatom reactivity.- Parallel oil reservoir simulation.- Formal specification of multicomputers.- Multi-million particle molecular dynamics on MPPs.- Wave propagation in urban microcells: a massively parallel approach using the TLM method.- The NAG Numerical PVM Library.- Cellular automata modeling of snow transport by wind.- Parallel algorithm for mapping of parallel programs into pyramidal multiprocessor.- Data-parallel molecular dynamics with neighbor-lists.- Visualizing astrophysical 3D MHD turbulence.- A parallel sparse QR-factorization algorithm.- Decomposing linear programs for parallel solution.- A parallel computation of the Navier-Stokes equation for the simulation of free surface flows with the volume of fluid method.- Improving the performance of parallel triangularization of a sparse matrix using a reconfigurable multicomputer.- Comparison of two image-space subdivision algorithms for Direct Volume Rendering on distributed-memory multicomputers.- Communication harnesses for transputer systems with tree structure and cube structure.- A thorough investigation of the projector quantum Monte Carlo method using MPP technologies.- Distributed simulation of a set of elastic macro objects.- Parallelization of ab initio molecular dynamics method.- Parallel computations with large atmospheric models.
• (source: Nielsen Book Data)

This book presents the refereed proceedings of the Second International Workshop on Applied Parallel Computing in Physics, Chemistry and Engineering Science, PARA'95, held in Lyngby, Denmark, in August 1995. The 60 revised full papers included have been contributed by physicists, chemists, and engineers, as well as by computer scientists and mathematicians, and document the successful cooperation of different scientific communities in the booming area of computational science and high performance computing. Many widely-used numerical algorithms and their applications on parallel computers are treated in detail.
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• 1. Triangular Operations and Negations (Allegro).- 1
• .1. Triangular Norms and Conorms.- 1
• .2. Negations.- 1
• .3. Associated Triangular Operations.- 1
• .4. Archimedean Triangular Operations.- 1
• .5. Induced Negations and Complementary Triangular Operations.- 1
• .6. Implications Induced by Triangular Norms.-
• 2. Fuzzy Sets (Andante spianato).- 2
• .1. The Concept of a Fuzzy Set.- 2
• .2. Operations on Fuzzy Sets.- 2
• .3. Generalized Operations.- 2
• .4. Other Elements of the Language of Fuzzy Sets.- 2
• .5. Towards Cardinalities of Fuzzy Sets.-
• 3. Scalar Cardinalities of Fuzzy Sets (Scherzo).- 3
• .1. An Axiomatic Viewpoint.- 3
• .2. Cardinality Patterns.- 3
• .3. Valuation Property and Subadditivity.- 3
• .4. Cartesian Product Rule and Complementarity.- 3
• .5. On the Fulfilment of a Group of the Properties.- 3.5
• .1. VAL and CART.- 3.5
• .2. CART and COMP.- 3.5
• .3. VAL and COMP.- 3.5
• .4. VAL, CART and COMP.-
• 4. Generalized Cardinals with Triangular Norms (Rondeau a la polonaise).- 4
• .1. Generalized FGCounts.- 4.1
• .1. The Corresponding Equipotency Relation.- 4.1
• .2. Inequalities.- 4.1
• .3. Arithmetical Operations.- 4.1.3
• .1. Addition.- 4.1.3
• .2. Subtraction.- 4.1.3
• .3. Multiplication.- 4.1.3
• .4. Division.- 4.1.3
• .5. Exponentiation.- 4.1
• .4. Some Derivative Concepts of Cardinality.- 4
• .2. Generalized FLCounts.- 4.2
• .1. Equipotencies and Inequalities.- 4.2
• .2. Addition and Other Arithmetical Operations.- 4
• .3. Generalized FECounts.- 4.3
• .1. The Height of a Generalized FECount.- 4.3
• .2. Singular Fuzzy Sets.- 4.3
• .3. Equipotencies, Inequalities and Arithmetical Questions.- List of Symbols.
• (source: Nielsen Book Data)

Counting is one of the basic elementary mathematical activities. It comes with two complementary aspects: to determine the number of elements of a set - and to create an ordering between the objects of counting just by counting them over. For finite sets of objects these two aspects are realized by the same type of num bers: the natural numbers. That these complementary aspects of the counting pro cess may need different kinds of numbers becomes apparent if one extends the process of counting to infinite sets. As general tools to determine numbers of elements the cardinals have been created in set theory, and set theorists have in parallel created the ordinals to count over any set of objects. For both types of numbers it is not only counting they are used for, it is also the strongly related process of calculation - especially addition and, derived from it, multiplication and even exponentiation - which is based upon these numbers. For fuzzy sets the idea of counting, in both aspects, looses its naive foundation: because it is to a large extent founded upon of the idea that there is a clear distinc tion between those objects which have to be counted - and those ones which have to be neglected for the particular counting process.
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• Preface. Acknowledgments. 1 Radionuclides and their Radiometric Measurement. 1.1 Radionuclides. 1.2 Modes of Radioactive Decay. 1.3 Detection and Measurement of Radiation. 2 Special Features of the Chemistry of Radionuclides and their Separation. 2.1 Small Quantities. 2.2 Adsorption. 2.3 Use of Carriers. 2.4 Utilization of Radiation in the Determination of Radionuclides. 2.5 Consideration of Elapsed Time. 2.6 Changes in the System Caused by Radiation and Decay. 2.7 The Need for Radiochemical Separations. 3 Factors Affecting Chemical Forms of Radionuclides in Aqueous Solutions. 3.1 Solution pH. 3.2 Redox Potential. 3.3 Dissolved Gases. 3.4 Ligands Forming Complexes with Metals. 3.5 Humic Substances. 3.6 Colloidal Particles. 3.7 Source and Generation of Radionuclides. 3.8 Appendix: Reagents Used to Adjust Oxidation States of Radionuclides. 4 Separation Methods. 4.1 Precipitation. 4.2 Solubility Product. 4.3 Ion Exchange. 4.4 Solvent Extraction. 4.5 Extraction Chromatography. 5 Yield Determinations and Counting Source Preparation. 5.1 The Determination of Chemical Yield in Radiochemical Analyses. 5.2 Preparation of Sources for Activity Counting. 5.3 Essentials in Chemical Yield Determination and in Counting Source Preparation. 6 Radiochemistry of the Alkali Metals. 6.1 Most Important Radionuclides of the Alkali Metals. 6.2 Chemical Properties of the Alkali Metals. 6.3 Separation Needs of Alkali Metal Radionuclides. 6.4 Potassium 40K. 6.5 Cesium 134Cs, 135Cs, and 137Cs. 6.6 Essentials in the Radiochemistry of the Alkali Metals. 7 Radiochemistry of the Alkaline Earth Metals. 7.1 Most Important Radionuclides of the Alkaline Earth Metals. 7.2 Chemical Properties of the Alkaline Earth Metals. 7.3 Beryllium 7Be and 10Be. 7.4 Calcium 41Ca and 45Ca. 7.5 Strontium 89Sr and 90Sr. 7.6 Radium 226Ra and 228Ra. 7.7 Essentials in the Radiochemistry of the Alkaline Earth Metals. 8 Radiochemistry of the 3d-Transition Metals. 8.1 The Most Important Radionuclides of the 3d-Transition Metals. 8.2 Chemical Properties of the 3d-Transition Metals. 8.3 Iron 55Fe. 8.4 Nickel 59Ni and 63Ni. 8.5 Essentials in 3-d Transition Metals Radiochemistry. 9 Radiochemistry of the 4d-Transition Metals. 9.1 Important Radionuclides of the 4d-Transition Metals. 9.2 Chemistry of the 4d-Transition Metals. 9.3 Technetium 99Tc. 9.4 Zirconium 93Zr. 9.5 Molybdenum 93Mo. 9.6 Niobium 94Nb. 9.7 Essentials in the Radiochemistry of 4-d Transition Metals. 10 Radiochemistry of the Lanthanides. 10.1 Important Lanthanide Radionuclides. 10.2 Chemical Properties of the Lanthanides. 10.3 Separation of Lanthanides from Actinides. 10.4 Lanthanides as Actinide Analogs. 10.5 147Pm and 151Sm. 10.6 Essentials of Lanthanide Radiochemistry. 11 Radiochemistry of the Halogens. 11.1 Important Halogen Radionuclides. 11.2 Physical and Chemical Properties of the Halogens. 11.3 Chlorine 36Cl. 11.4 Iodine 129I. 11.5 Essentials of Halogen Radiochemistry. 12 Radiochemistry of the Noble Gases. 12.1 Important Radionuclides of the Noble Gases. 12.2 Physical and Chemical Characteristics of the Noble Gases. 12.3 Measurement of Xe Isotopes in Air. 12.4 Determination of 85Kr in Air. 12.5 Radon and its Determination. 12.6 Essentials of Noble Gas Radiochemistry. 13 Radiochemistry of Tritium and Radiocarbon. 13.1 Tritium 3H. 13.2 Radiocarbon 14C. 13.3 Essentials of Tritium and Radiocarbon Radiochemistry. 14 Radiochemistry of Lead, Polonium, Tin, and Selenium. 14.1 Polonium 210Po. 14.2 Lead 210Pb. 14.3 Tin 126Sn. 14.4 Selenium 79Se. 14.5 Essentials of Polonium, Lead, Tin, and Selenium Radiochemistry. 15 Radiochemistry of the Actinides. 15.1 Important Actinide Isotopes. 15.2 Generation and Origin of the Actinides. 15.3 Electronic Structures of the Actinides. 15.4 Oxidation States of the Actinides. 15.5 Ionic Radii of the Actinides. 15.6 Major Chemical Forms of the Actinides. 15.7 Disproportionation. 15.8 Hydrolysis and Polymerization of the Actinides. 15.9 Complex Formation of the Actinides. 15.10 Oxides of the Actinides. 15.11 Actinium. 15.12 Thorium. 15.13 Protactinium. 15.14 Uranium. 15.15 Neptunium. 15.16 Plutonium. 15.17 Americium and Curium. 16 Speciation Analysis. 16.1 Considerations Relevant to Speciation. 16.2 Significance of Speciation. 16.3 Categorization of Speciation Analyzes. 16.4 Fractionation Techniques for Environmental Samples. 16.5 Analysis of Radionuclide and Isotope Compositions. 16.6 Spectroscopic Speciation Methods. 16.7 Wet Chemical Methods. 16.8 Sequential Extractions. 16.9 Computational Speciation Methods. 16.10 Characterization of Radioactive Particles. 17 Measurement of Radionuclides by Mass Spectrometry. 17.1 Introduction. 17.2 Inductively Coupled Plasma Mass Spectrometry (ICP-MS). 17.3 Accelerator Mass Spectrometry (AMS). 17.4 Thermal Ionization Mass Spectrometry (TIMS). 17.5 Resonance Ionization Mass Spectrometry (RIMS). 17.6 Essentials of the Measurement of Radionuclides by Mass Spectrometry. 18 Sampling and Sample Pretreatment for the Determination of Radionuclides. 18.1 Introduction. 18.2 Air Sampling and Pretreatment. 18.3 Sampling Gaseous Components. 18.4 Atmospheric Deposition Sampling. 18.5 Water Sampling. 18.6 Sediment Sampling and Pretreatment. 18.7 Soil Sampling and Pretreatment. 18.8 Essentials in Sampling and Sample Pretreatment for Radionuclides. 19 Chemical Changes Induced by Radioactive Decay. 19.1 Autoradiolysis. 19.2 Transmutation and Subsequent Chemical Changes. 19.3 Recoil Hot Atom Chemistry. Index.
• (source: Nielsen Book Data)

Written by chemists for chemists, this is a comprehensive guide to the important radionuclides as well as techniques for their separation and analysis. It introduces readers to the important laboratory techniques and methodologies in the field, providing practical instructions on how to handle nuclear waste and radioactivity in the environment.
(source: Nielsen Book Data)

• Design and Modeling of Anti Wind Up PID Controllers.- A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm.- Towards Robust Performance Guarantees for Models Learned from High-Dimensional Data.- Expert-Based Method of Integrated Waste Management Systems for Developing Fuzzy Cognitive Map.- Leukocyte Detection through an Evolutionary Method.- PWARX model identification based on clustering approach.- Supplier quality evaluation using a fuzzy multi criteria decision making approach.- Concept Trees: Building Dynamic Concepts from Semi-Structured Data using Nature-Inspired Methods.- Swarm Intelligence Techniques and Their Adaptive Nature with Applications.- Signal Based Fault Detection and Diagnosis for rotating electrical machines: Issues and Solutions.- Modelling Of Intrusion Detection System Using Artificial Intelligence -Evaluation Of Performance Measures.- Enhanced Power System Security Assessment through Intelligent Decision Trees.- Classification of Normal and Epileptic Seizure EEG Signals based on Empirical Mode Decomposition.- A Rough Set Based Total Quality Management Approach in Higher Education.- Iterative Dual Rational Krylov and Iterative SVD-Dual Rational Krylov Model Reduction for Switched Linear Systems.- Household Electrical Consumption Modeling through Fuzzy Logic Approach.- Modeling, Identification and Control of irrigation station with sprinkling: Takagi- Sugeno approach.- Review and Improvement of Several Optimal Intelligent Pitch Controllers and Estimator of WECS via Artificial Intelligent Approaches.- Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach.- Approximation of Optimized Fuzzy Logic Controller for Shunt Active Power Filter.-Soft Computing Techniques For Optimal Capacitor Placement.- Advanced Metaheuristics-based Approach for Fuzzy Control Systems Tuning.- Robust Estimation Design for Unknown Inputs Fuzzy Bilinear Models: Application to Faults Diagnosis.- Unit Commitment Optimization Using Gradient-Genetic algorithm and Fuzzy logic approaches.- Impact of Hardware/Software Partitioning and MicroBlaze FPGA Configurations on the Embedded Systems Performances.- A Neural Approach to Cursive Handwritten Character Recognition using Features Extracted from Binarization Technique.- System Identification Technique and Neural Networks for Material Lifetime Assessment Application.- Measuring Software Reliability: A Trend using Machine Learning Techniques.- Hybrid Metaheuristic Approach for Scheduling of Aperiodic OS Tasks.
• (source: Nielsen Book Data)

The book offers a snapshot of the theories and applications of soft computing in the area of complex systems modeling and control. It presents the most important findings discussed during the 5th International Conference on Modelling, Identification and Control, held in Cairo, from August 31-September 2, 2013. The book consists of twenty-nine selected contributions, which have been thoroughly reviewed and extended before their inclusion in the volume. The different chapters, written by active researchers in the field, report on both current theories and important applications of soft-computing. Besides providing the readers with soft-computing fundamentals, and soft-computing based inductive methodologies/algorithms, the book also discusses key industrial soft-computing applications, as well as multidisciplinary solutions developed for a variety of purposes, like windup control, waste management, security issues, biomedical applications and many others. It is a perfect reference guide for graduate students, researchers and practitioners in the area of soft computing, systems modeling and control.
(source: Nielsen Book Data)

Computer algebra systems allow students to work on mathematical models more efficiently than in the case of pencil and paper. The use of such systems also leads to fewer errors and enables students to work on complex and computationally intensive models. Aimed at undergraduates in their second or third year, this book is filled with examples from a wide variety of disciplines, including biology, economics, medicine, engineering, game theory, physics, and chemistry. The text includes a large number of Maple(R) recipes.
(source: Nielsen Book Data)

• Preface
• 1. Introduction to Computer Simulation 1.1 Historical Background 1.2 Theory, Modeling and Simulation in Physics 1.3 Reductionism in Physics 1.4 Basics of Ordinary and Partial Differential Equations in Physics 1.5 Numerical Solution of Differential Equations: Mesh-Based vs. Particle Methods 1.6 The Role of Algorithms in Scientific Computing 1.7 Remarks on Software Design 1.8 Summary
• 2. Fundamentals of Statistical Physics 2.1 Introduction 2.2 Elementary Statistics 2.3 Introduction to Classical Statistical Mechanics 2.4 Introduction to Thermodynamics 2.5 Summary
• 3. Inter- and Intramolecular Short-Range Potentials 3.1 Introduction 3.2 Quantum Mechanical Basis of Intermolecular Interactions 3.2.1 Perturbation Theory 3.3 Classical Theories of Intermolecular Interactions 3.4 Potential Functions 3.5 Molecular Systems 3.6 Summary
• 4. Molecular Dynamics Simulation 4.1 Introduction 4.2 Basic Ideas of MD 4.3 Algorithms for Calculating Trajectories 4.4 Link between MD and Quantum Mechanics 4.5 Basic MD Algorithm: Implementation Details 4.6 Boundary Conditions 4.7 The Cutoff Radius for Short-Range Potentials 4.8 Neighbor Lists: The Linked-Cell Algorithm 4.9 The Method of Ghost Particles 4.10 Implementation Details of the Ghost Particle Method 4.11 Making Measurements 4.12 Ensembles and Thermostats 4.13 Case Study: Impact of Two Different Bodies 4.14 Case Study: Rayleigh-Taylor Instability 4.15 Case Study: Liquid-Solid Phase Transition of Argon
• 5. Advanced MD Simulation 5.1 Introduction 5.2 Parallelization 5.3 More Complex Potentials and Molecules 5.4 Many Body Potentials 5.5 Coarse Grained MD for Mesoscopic Systems
• 6. Outlook on Monte Carlo Simulations 6.1 Introduction 6.2 The Metropolis Monte-Carlo Method 6.2.1 Calculation of Volumina and Surfaces 6.2.2 Percolation Theory 6.3 Basic MC Algorithm: Implementation Details 6.3.1 Case Study: The 2D Ising Magnet 6.3.2 Trial Moves and Pivot Moves 6.3.3 Case Study: Combined MD and MC for Equilibrating a Gaussian Chain 6.3.4 Case Study: MC of Hard Disks 6.3.5 Case Study: MC of Hard Disk Dumbbells in 2D 6.3.6 Case Study: Equation of State for the Lennard-Jones Fluid 6.4 Rosenbluth and Rosenbluth Method 6.5 Bond Fluctuation Model 6.6 Monte Carlo Simulations in Different Ensembles 6.7 Random Numbers Are Hard to Find
• 7. Applications from Soft Matter and Shock Wave Physics 7.1 Biomembranes 7.2 Scaling Properties of Polymers 7.3 Polymer Melts 7.4 Polymer Networks as a Model for the Cytoskeleton of Cells 7.5 Shock Wave Impact in Brittle Solids
• 8. Concluding Remarks A Appendix A.1 Quantum Statistics of Ideal Gases A.2 Maxwell-Boltzmann, Bose-Einstein- and Fermi-Dirac Statistics A.3 Stirling's Formula A.4 Useful Integrals in Statistical Physics A.3 Useful Conventions for Implementing Simulation Programs A.4 Quicksort and Heapsort Algorithms A.4 Selected Solutions to Exercises Abbreviations Bibliography Index.
• (source: Nielsen Book Data)

This work is a needed reference for widely used techniques and methods of computer simulation in physics and other disciplines, such as materials science. Molecular dynamics computes a molecule's reactions and dynamics based on physical models; Monte Carlo uses random numbers to image a system's behaviour when there are different possible outcomes with related probabilities. The work conveys both the theoretical foundations as well as applications and "tricks of the trade", that often are scattered across various papers. Thus it will meet a need and fill a gap for every scientist who needs computer simulations for his/her task at hand. In addition to being a reference, case studies and exercises for use as course reading are included.
(source: Nielsen Book Data)

• Preface
• 1. Introduction to Computer Simulation 1.1 Historical Background 1.2 Theory, Modeling and Simulation in Physics 1.3 Reductionism in Physics 1.4 Basics of Ordinary and Partial Differential Equations in Physics 1.5 Numerical Solution of Differential Equations: Mesh-Based vs. Particle Methods 1.6 The Role of Algorithms in Scientific Computing 1.7 Remarks on Software Design 1.8 Summary
• 2. Fundamentals of Statistical Physics 2.1 Introduction 2.2 Elementary Statistics 2.3 Introduction to Classical Statistical Mechanics 2.4 Introduction to Thermodynamics 2.5 Summary
• 3. Inter- and Intramolecular Short-Range Potentials 3.1 Introduction 3.2 Quantum Mechanical Basis of Intermolecular Interactions 3.2.1 Perturbation Theory 3.3 Classical Theories of Intermolecular Interactions 3.4 Potential Functions 3.5 Molecular Systems 3.6 Summary
• 4. Molecular Dynamics Simulation 4.1 Introduction 4.2 Basic Ideas of MD 4.3 Algorithms for Calculating Trajectories 4.4 Link between MD and Quantum Mechanics 4.5 Basic MD Algorithm: Implementation Details 4.6 Boundary Conditions 4.7 The Cutoff Radius for Short-Range Potentials 4.8 Neighbor Lists: The Linked-Cell Algorithm 4.9 The Method of Ghost Particles 4.10 Implementation Details of the Ghost Particle Method 4.11 Making Measurements 4.12 Ensembles and Thermostats 4.13 Case Study: Impact of Two Different Bodies 4.14 Case Study: Rayleigh-Taylor Instability 4.15 Case Study: Liquid-Solid Phase Transition of Argon
• 5. Advanced MD Simulation 5.1 Introduction 5.2 Parallelization 5.3 More Complex Potentials and Molecules 5.4 Many Body Potentials 5.5 Coarse Grained MD for Mesoscopic Systems
• 6. Outlook on Monte Carlo Simulations 6.1 Introduction 6.2 The Metropolis Monte-Carlo Method 6.2.1 Calculation of Volumina and Surfaces 6.2.2 Percolation Theory 6.3 Basic MC Algorithm: Implementation Details 6.3.1 Case Study: The 2D Ising Magnet 6.3.2 Trial Moves and Pivot Moves 6.3.3 Case Study: Combined MD and MC for Equilibrating a Gaussian Chain 6.3.4 Case Study: MC of Hard Disks 6.3.5 Case Study: MC of Hard Disk Dumbbells in 2D 6.3.6 Case Study: Equation of State for the Lennard-Jones Fluid 6.4 Rosenbluth and Rosenbluth Method 6.5 Bond Fluctuation Model 6.6 Monte Carlo Simulations in Different Ensembles 6.7 Random Numbers Are Hard to Find
• 7. Applications from Soft Matter and Shock Wave Physics 7.1 Biomembranes 7.2 Scaling Properties of Polymers 7.3 Polymer Melts 7.4 Polymer Networks as a Model for the Cytoskeleton of Cells 7.5 Shock Wave Impact in Brittle Solids
• 8. Concluding Remarks A Appendix A.1 Quantum Statistics of Ideal Gases A.2 Maxwell-Boltzmann, Bose-Einstein- and Fermi-Dirac Statistics A.3 Stirling's Formula A.4 Useful Integrals in Statistical Physics A.3 Useful Conventions for Implementing Simulation Programs A.4 Quicksort and Heapsort Algorithms A.4 Selected Solutions to Exercises Abbreviations Bibliography Index.
• (source: Nielsen Book Data)

This work is a needed reference for widely used techniques and methods of computer simulation in physics and other disciplines, such as materials science. Molecular dynamics computes a molecule's reactions and dynamics based on physical models; Monte Carlo uses random numbers to image a system's behaviour when there are different possible outcomes with related probabilities. The work conveys both the theoretical foundations as well as applications and "tricks of the trade", that often are scattered across various papers. Thus it will meet a need and fill a gap for every scientist who needs computer simulations for his/her task at hand. In addition to being a reference, case studies and exercises for use as course reading are included.
(source: Nielsen Book Data)

• Preface
• 1. Introduction to Computer Simulation 1.1 Historical Background 1.2 Theory, Modeling and Simulation in Physics 1.3 Reductionism in Physics 1.4 Basics of Ordinary and Partial Differential Equations in Physics 1.5 Numerical Solution of Differential Equations: Mesh-Based vs. Particle Methods 1.6 The Role of Algorithms in Scientific Computing 1.7 Remarks on Software Design 1.8 Summary
• 2. Fundamentals of Statistical Physics 2.1 Introduction 2.2 Elementary Statistics 2.3 Introduction to Classical Statistical Mechanics 2.4 Introduction to Thermodynamics 2.5 Summary
• 3. Inter- and Intramolecular Short-Range Potentials 3.1 Introduction 3.2 Quantum Mechanical Basis of Intermolecular Interactions 3.2.1 Perturbation Theory 3.3 Classical Theories of Intermolecular Interactions 3.4 Potential Functions 3.5 Molecular Systems 3.6 Summary
• 4. Molecular Dynamics Simulation 4.1 Introduction 4.2 Basic Ideas of MD 4.3 Algorithms for Calculating Trajectories 4.4 Link between MD and Quantum Mechanics 4.5 Basic MD Algorithm: Implementation Details 4.6 Boundary Conditions 4.7 The Cutoff Radius for Short-Range Potentials 4.8 Neighbor Lists: The Linked-Cell Algorithm 4.9 The Method of Ghost Particles 4.10 Implementation Details of the Ghost Particle Method 4.11 Making Measurements 4.12 Ensembles and Thermostats 4.13 Case Study: Impact of Two Different Bodies 4.14 Case Study: Rayleigh-Taylor Instability 4.15 Case Study: Liquid-Solid Phase Transition of Argon
• 5. Advanced MD Simulation 5.1 Introduction 5.2 Parallelization 5.3 More Complex Potentials and Molecules 5.4 Many Body Potentials 5.5 Coarse Grained MD for Mesoscopic Systems
• 6. Outlook on Monte Carlo Simulations 6.1 Introduction 6.2 The Metropolis Monte-Carlo Method 6.2.1 Calculation of Volumina and Surfaces 6.2.2 Percolation Theory 6.3 Basic MC Algorithm: Implementation Details 6.3.1 Case Study: The 2D Ising Magnet 6.3.2 Trial Moves and Pivot Moves 6.3.3 Case Study: Combined MD and MC for Equilibrating a Gaussian Chain 6.3.4 Case Study: MC of Hard Disks 6.3.5 Case Study: MC of Hard Disk Dumbbells in 2D 6.3.6 Case Study: Equation of State for the Lennard-Jones Fluid 6.4 Rosenbluth and Rosenbluth Method 6.5 Bond Fluctuation Model 6.6 Monte Carlo Simulations in Different Ensembles 6.7 Random Numbers Are Hard to Find
• 7. Applications from Soft Matter and Shock Wave Physics 7.1 Biomembranes 7.2 Scaling Properties of Polymers 7.3 Polymer Melts 7.4 Polymer Networks as a Model for the Cytoskeleton of Cells 7.5 Shock Wave Impact in Brittle Solids
• 8. Concluding Remarks A Appendix A.1 Quantum Statistics of Ideal Gases A.2 Maxwell-Boltzmann, Bose-Einstein- and Fermi-Dirac Statistics A.3 Stirling's Formula A.4 Useful Integrals in Statistical Physics A.3 Useful Conventions for Implementing Simulation Programs A.4 Quicksort and Heapsort Algorithms A.4 Selected Solutions to Exercises Abbreviations Bibliography Index.
• (source: Nielsen Book Data)

This work is a needed reference for widely used techniques and methods of computer simulation in physics and other disciplines, such as materials science. Molecular dynamics computes a molecule's reactions and dynamics based on physical models; Monte Carlo uses random numbers to image a system's behaviour when there are different possible outcomes with related probabilities. The work conveys both the theoretical foundations as well as applications and "tricks of the trade", that often are scattered across various papers. Thus it will meet a need and fill a gap for every scientist who needs computer simulations for his/her task at hand. In addition to being a reference, case studies and exercises for use as course reading are included.
(source: Nielsen Book Data)

• Cover page; Half title; LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY; Title Page; Copyright; Dedication; CONTENTS; Preface;
• Chapter 1: Introduction;
• Chapter 2: Finite Element Method-A Summary; Overview; FEM Formulation; Matrix Approach; Example 2.1: Analysis of a 2D Truss; General Procedure for Global Matrix Assembly; Example 2.2: Global Matrix for Triangular Elements; Weighted Residual Approach; Galerkin Method; Shape Functions; Convergence and Stability; Example2.3: Heat Transfer in a Slender Steel Bar; Exercise Problems; References;
• Chapter 3: COMSOL-A Modeling Tool for Engineers.

• OverviewCOMSOL Interface; COMSOL Modules; COMSOL Model Library and Tutorials; General Guidelines for Building a Model;
• Chapter 4: COMSOL Models for Physical Systems; Overview; Section 4.1: Static and Dynamic Analysis of Structures; Example 4.1: Stress Analysis for a Thin Plate Under Stationary Loads; Example 4.2: Dynamic Analysis for a Thin Plate: Eigenvalues and Modal Shapes; Example 4.3: Parametric Study for a Bracket Assembly: 3D Stress Analysis; Example 4.4: Buckling of a Column with Triangular Cross-section: Linearized Buckling Analysis.

• Example 4
• .5: Static and Dynamic Analysis for a 2D Bridge-support TrussExample 4
• .6: Static and Dynamic Analysis for a 3D Truss Tower; Section 4
• .2: Dynamic Analysis and Models of Internal Flows: Laminar and Turbulent; Example 4
• .7: Axisymmetric Flow in a Nozzle: Simplified Water-jet; Example 4
• .8: Swirl Flow Around a Rotating Disk: Laminar Flow; Example 4
• .9: Swirl Flow Around a Rotating Disk: Turbulent Flow; Example 4
• .10: Flow in a U-shape Pipe with Square Cross-sectional Area: Laminar Flow; Example 4
• .11: Double-driven Cavity Flow: Moving Boundary Conditions.

• Example 4
• .12: Water Hammer Model: Transient Flow AnalysisExample 4
• .13: Static Fluid Mixer Model; Section 4
• .3: Modeling of Steady and Unsteady Heat Transfer in Media; Example 4
• .14: Heat Transfer in a Multilayer Sphere; Example 4
• .15: Heat Transfer in a Hexagonal Fin; Example 4
• .16: Transient Heat Transfer Through a Nonprismatic Fin with Convective Cooling; Example 4
• .17: Heat Conduction Through a Multilayer Wall with Contact Resistance; Section 4
• .4: Modeling of Electrical Circuits; Example 4
• .18: Modeling an RC Electrical Circuit; Example 4
• .19: Modeling an RLC Electrical Circuit.

• Section 4
• .5: Modeling Complex and Multiphysics ProblemsExample 4
• .20: Stress Analysis for an Orthotropic Thin Plate; Example 4
• .21: Thermal Stress Analysis and Transient Response of a Bracket; Example 4
• .22: Static Fluid Mixer with Flexible Baffles; Example 4
• .23: Double Pendulum: Multibody Dynamics; Example 4
• .24: Multiphysics Model for Thermoelectric Modules; Example 4
• .25: Acoustic Pressure Wave Propagation in an Automotive Muffler; Exercise Problems; References; Suggested Further Readings; Trademark References; Index.

• 1: Introduction.
• 2: Finite Element Method (FEM)-A Summary.
• 3: COMSOL - A Modeling Tool For Engineers.
• 4: Modeling Single-Physics Problems.
• 5: Modeling Multi-Physics Problems.
• 6: Modeling Energy Systems With COMSOL.
• 7: Advanced Features Of COMSOL.
• Appendices.
• Index.
• (source: Nielsen Book Data)

COMSOL Multiphysics software is one of the most valuable software modeling tools for engineers and scientists. This book is designed for engineers from the fields of mechanical, electrical, and civil disciplines, and introduces multiphysics modeling techniques and examples accompanied by practical applications using COMSOL 4.x. The book includes a companion CD-ROM with files of over 25 models, images, and code. The main objective is to introduce readers to use COMSOL as an engineering tool for modeling by solving examples that could become a guide for modeling similar or more complicated problems. The objective is to provide a collection of examples and modeling guidelines through which readers can build their own models. Readers are assumed to be familiar with the principles of numerical modeling and the finite element method (FEM). The book takes a flexible-level approach for presenting the materials along with using practical examples. The mathematical fundamentals, engineering principles, and design criteria are presented as integral parts of examples. At the end of each chapter are references that contain more in-depth physics, technical information, and data; these are referred to throughout the book and used in the examples. "COMSOL for Engineers" could be used to complement another text that provides background training in engineering computations and methods. Examples provided in this book should be considered as "lessons" for which background physics could be explained in more detail. FEATURES Includes a companion CD-ROM with files of over 25 models, images, code Uses progressive approach in terms of examples and models.
(source: Nielsen Book Data)

• 1: Introduction
• 2: Finite Element Method (FEM)-A Summary
• 3: COMSOL - A Modeling Tool For Engineers
• 4: Modeling Single-Physics Problems
• 5: Modeling Multi-Physics Problems
• 6: Modeling Energy Systems With COMSOL
• 7: Advanced Features of COMSOL
• Appendices
• Index.
• (source: Nielsen Book Data)

COMSOL5 Multiphysics(R) is one of the most valuable software modeling tools for engineers and scientists. This book, an updated edition of the previously published, COMSOL for Engineers, covers COMSOL5 which now includes a revolutionary tool, the Application Builder. This component enables users to build apps based on COMSOL models that can be run on almost any operating system (Windows, MAC, mobile/iOS, etc.). Designed for engineers from various disciplines, the book introduces multiphysics modeling techniques and examples accompanied by practical applications using COMSOL5.x. The main objective is to introduce readers to use COMSOL as an engineering tool for modeling, by solving examples that could become a guide for modeling similar or more complicated problems. The book provides a collection of examples and modeling guidelines through which readers can build their own models. The mathematical fundamentals, engineering principles, and design criteria are presented as integral parts of the examples. At the end of chapters are references that contain more in-depth physics, technical information, and data; these are referred to throughout the book and used in the examples. COMSOL5 for Engineers could be used to complement another text that provides background training in engineering computations and methods. Exercises are provided at the end of the text for use in adoption situations. Features: -Expands the Finite Element Method (FEM) theory and adds more examples from the original edition -Outlines the new features in COMSOL5, the graphical user interface (GUI), and how to build a COMSOL app for models -Includes apps for selected model examples-with parameterization of these models -Features new and modified, solved model examples, in addition to the models provided in the original edition -Companion disc with executable copies of each model and their related animations eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com.
(source: Nielsen Book Data)

• 1: Introduction
• 2: Finite Element Method (FEM)-A Summary
• 3: COMSOL - A Modeling Tool For Engineers
• 4: Modeling Single-Physics Problems
• 5: Modeling Multi-Physics Problems
• 6: Modeling Energy Systems With COMSOL
• 7: Advanced Features of COMSOL
• Appendices
• Index.
• (source: Nielsen Book Data)

COMSOL5 Multiphysics(R) is one of the most valuable software modeling tools for engineers and scientists. This book, an updated edition of the previously published, COMSOL for Engineers, covers COMSOL5 which now includes a revolutionary tool, the Application Builder. This component enables users to build apps based on COMSOL models that can be run on almost any operating system (Windows, MAC, mobile/iOS, etc.). Designed for engineers from various disciplines, the book introduces multiphysics modeling techniques and examples accompanied by practical applications using COMSOL5.x. The main objective is to introduce readers to use COMSOL as an engineering tool for modeling, by solving examples that could become a guide for modeling similar or more complicated problems. The book provides a collection of examples and modeling guidelines through which readers can build their own models. The mathematical fundamentals, engineering principles, and design criteria are presented as integral parts of the examples. At the end of chapters are references that contain more in-depth physics, technical information, and data; these are referred to throughout the book and used in the examples. COMSOL5 for Engineers could be used to complement another text that provides background training in engineering computations and methods. Exercises are provided at the end of the text for use in adoption situations. Features: -Expands the Finite Element Method (FEM) theory and adds more examples from the original edition -Outlines the new features in COMSOL5, the graphical user interface (GUI), and how to build a COMSOL app for models -Includes apps for selected model examples-with parameterization of these models -Features new and modified, solved model examples, in addition to the models provided in the original edition -Companion disc with executable copies of each model and their related animations eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com.
(source: Nielsen Book Data)