Introduction to Computational Engineering with MATLAB®

Tim Bower is an Associate Professor of Robotics and Automation Engineering Technology and Computer Systems Technology at Kansas State University Salina. He received the B.S. Electrical Engineering degree from Kansas State University (K-State) in 1987 and the M.S. Electrical Engineering degree from the University of Kansas in 1990. He was a Senior Member of the Technical Staff at Sprint's Local Telephone Division from 1989 to 1998. From 1998 to 2003, he was a systems administration manager and instructor at Kansas State University in Manhattan Kansas while taking graduate course work in Computer Science. He joined the faculty of K-State's campus in Salina Kansas in 2004. He teaches undergraduate courses related to programming in C, Python, and MATLAB, robotics programming, machine vision, numerical computation, operating systems, data structures and algorithms, and systems administration. Away from teaching, he enjoys spending time with his wife, three grown children, and five grandchildren.

1. MATLAB Programming. 1.1. The MATLAB Development Environment. 1.2. Variables and Values. 1.3. MATLAB Scripts. 1.4. Input and Output. 1.5. For Loops. 1.6. Control Constructs. 1.7. Vectors and Matrices in MATLAB. 1.8. MATLAB Functions. 1.9. Functions Operating on Vectors. 1.10. Importing Data Into MATLAB. 1.11. Text Strings in MATLAB. 1.12. Exercises. 2. Graphical Data Analysis. 2.1. Using the Plot Tool. 2.2. Basic Line Plots. 2.3. 3-D Plots. 2.4. Exercises. 3. Statistical Data Analysis. 3.1. Introduction to Statistics. 3.2. Common Statistical Functions. 3.3. Moving Window Statistics. 3.4. Probability Distributions. 3.5. Generating Random Numbers. 3.6. Statistics on Matrices. 3.7. Plots of Statistical Data. 3.8. Central Limit Theorem. 3.9. Sampling and Confidence Intervals. 3.10. Statistical Significance. 3.11. Exercises. 4. Using the Symbolic Math Toolbox. 4.1. Throwing a Ball Up. 4.2. Symbolic Algebra. 4.3. Symbolic Calculus. 4.4. Symbolic Differential Equations. 4.5. Exercises. 5. Introduction to Linear Algebra. 5.1. Working with Vectors. 5.2. Working with Matrices. 5.3. Geometric Transforms. 5.4. Systems of Linear Equations. 5.5. Elimination. 5.6. LU Decomposition. 5.7. Linear System Applications. 5.8. Under-determined Systems. 5.9. Over-determined Systems and Vector Projections. 5.10. Least Squares Regression. 5.11. Left-Divide Operator. 5.12. Exercises. 6. Application of Eigenvalues and Eigenvectors. 6.1. Introduction to Eigenvalues and Eigenvectors. 6.2. Eigenvector Animation. 6.3. Finding Eigenvalues and Eigenvectors. 6.4. Properties of Eigenvalues and Eigenvectors. 6.5. Diagonalization and Powers of A. 6.6. Change of Basis and Difference Equations. 6.7. Systems of Linear ODEs. 6.8. Singular Value Decomposition (SVD). 6.9. Principal Component Analysis (PCA). 6.10. Eigenvector Animation Code. 6.11. Exercises. 7. Computational Numerical Methods. 7.1. Optimization. 7.2. Data Interpolation. 7.3. Numerical Differentiation. 7.4. Numerical Integration. 7.5. Numerical Differential Equations. 7.6. Exercises. A. Linear Algebra Appendix. B. The Number e. Bibliography. Index.