This book is designed for users who already know R or MATLAB® and now need to learn the other platform. The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

Introduces the basic concepts of FEM in an easy-to-use format so that students and professionals can use the method efficiently and interpret results properly Finite element method (FEM) is a powerful tool for solving engineering problems both in solid structural mechanics and fluid mechanics. This book presents all of the theoretical aspects of FEM that students of engineering will need. It eliminates overlong math equations in favour of basic concepts, and reviews of the mathematics and mechanics of materials in order to illustrate the concepts of FEM. It introduces these concepts by including examples using six different commercial programs online. The all-new, second edition of Introduction to Finite Element Analysis and Design provides many more exercise problems than the first edition. It includes a significant amount of material in modelling issues by using several practical examples from engineering applications. The book features new coverage of buckling of beams and frames and extends heat transfer analyses from 1D (in the previous edition) to 2D. It also covers 3D solid element and its application, as well as 2D. Additionally, readers will find an increase in coverage of finite element analysis of dynamic problems. There is also a companion website with examples that are concurrent with the most recent version of the commercial programs. Offers elaborate explanations of basic finite element procedures Delivers clear explanations of the capabilities and limitations of finite element analysis Includes application examples and tutorials for commercial finite element software, such as MATLAB, ANSYS, ABAQUS and NASTRAN Provides numerous examples and exercise problems Comes with a complete solution manual and results of several engineering design projects Introduction to Finite Element Analysis and Design, 2nd Edition is an excellent text for junior and senior level undergraduate students and beginning graduate students in mechanical, civil, aerospace, biomedical engineering, industrial engineering and engineering mechanics.

Graduate from Excel to MATLAB® to keep up with the evolution of finance data Foundations of Computational Finance with MATLAB® is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today’s analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset. This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB’s capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data sources, communicate graphically, and more. Master MATLAB’s basic operations including matrices, arrays, and flexible data structures Learn how to build your own customized solutions when the built-ins just won’t do Learn how to handle financial data and industry-specific variables including risk and uncertainty Adopt more accurate modeling practices for portfolios, options, time series, and more MATLAB is an integrated development environment that includes everything you need in one well-designed user interface. Available Toolboxes provide tested algorithms that save you hours of code, and the skills you learn using MATLAB make it easier to learn additional languages if you choose to do so. Financial firms are catching up to universities in MATLAB usage, so this is skill set that will follow you throughout your career. When you’re ready to step into the new age of finance, Foundations of Computational Finance with MATLAB provides the expert instruction you need to get started quickly.

The book presents an integrated treatment of continuous simulation with all the background and essential prerequisites in one setting. It features updated chapters and two new sections on Black Swan and the Stochastic Information Packet (SIP) and Stochastic Library Units with Relationships Preserved (SLURP) Standard.

Interval Finite Element Method with MATLAB provides a thorough introduction to an effective way of investigating problems involving uncertainty using computational modeling. The well-known and versatile Finite Element Method (FEM) is combined with the concept of interval uncertainties to develop the Interval Finite Element Method (IFEM). An interval or stochastic environment in parameters and variables is used in place of crisp ones to make the governing equations interval, thereby allowing modeling of the problem. The concept of interval uncertainties is systematically explained. Several examples are explored with IFEM using MATLAB on topics like spring mass, bar, truss and frame. Provides a systematic approach to understanding the interval uncertainties caused by vague or imprecise data Describes the interval finite element method in detail Gives step-by-step instructions for how to use MATLAB code for IFEM Provides a range of examples of IFEM in use, with accompanying MATLAB codes

This book covers the fundamentals of adaptive filtering, with a focus on the least mean square (LMS) adaptive filter. It discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions, while delivering a concise introduction to MATLAB®—complete with problems, computer experiments, and over 110 functions and script files. The text not only addresses the basics of the LMS adaptive filter algorithm but also explores the Wiener filter and its applications, details the steepest descent method, and develops the Newton’s algorithm.

Electronically scanned arrays (ESAs) have become a key technology for sensor electronic systems. MATLAB® provides an excellent framework for ESA design and analysis, and this book is an invaluable resource for those who require simulation analysis tools that provide insight and understanding for ESA design. In addition to covering ESA fundamentals such as pattern synthesis, grating lobes, and instantaneous bandwidth, the text also provides insight into pattern optimization, subarray beamforming, space-based application of ESAs, and ESA reliability modeling. The book provides MATLAB code, giving readers an opportunity to model ESAs and develop an in-depth understanding that other books do not offer.

Familiarize yourself with MATLAB using this concise, practical tutorial that is focused on writing code to learn concepts. Starting from the basics, this book covers array-based computing, plotting and working with files, numerical computation formalism, and the primary concepts of approximations. Introduction to MATLAB is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll Learn Apply sample code to your engineering or science problems Work with MATLAB arrays, functions, and loops Use MATLAB’s plotting functions for data visualization Solve numerical computing and computational engineering problems with a MATLAB case study Who This Book Is For Engineers, scientists, researchers, and students who are new to MATLAB. Some prior programming experience would be helpful but not required.

The book takes an integrative approach to the subject of electromagnetics by supplementing quintessential "old school" information and methods with instruction in the use of new commercial software such as MATLAB.

Provides a one-stop resource for engineers learning biostatistics using MATLAB® and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian paradigms, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLAB® for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and references. Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS also includes: parallel coverage of classical and Bayesian approaches, where appropriate substantial coverage of Bayesian approaches to statistical inference material that has been classroom-tested in an introductory statistics course in bioengineering over several years exercises at the end of each chapter and an accompanying website with full solutions and hints to some exercises, as well as additional materials and examples Engineering Biostatistics: An Introduction using MATLAB® and WinBUGS can serve as a textbook for introductory-to-intermediate applied statistics courses, as well as a useful reference for engineers interested in biostatistical approaches.

Learn the art of creating machine learning models and processing data efficiently with MATLAB. In this book, you will explore various techniques such as regression analysis, clustering, classification, and neural networks, all in the MATLAB environment. Each topic is detailed with practical examples for clear understanding and immediate application. What this Book will help me do Understand the key concepts of machine learning and how they integrate with MATLAB. Learn to preprocess and transform data for effective machine learning workflows. Explore regression methods and apply them to analyze and predict trends in your data. Master classification and clustering techniques for model creation and data categorization. Gain expertise in using MATLAB Neural Network Toolbox for building neural network-based solutions. Author(s) None Kolluru and Giuseppe Ciaburro are seasoned experts in using MATLAB for data analysis and machine learning. With years of experience in research and teaching, they have meticulously curated this book to bridge concepts of theory with real-world applications. Their writing approach is clear, instructional, and focused on equipping learners with practical skills. Who is it for? This book is ideal for data analysts, aspiring data scientists, and students eager to delve into machine learning using MATLAB. Even if you're new to the field, you'll find the instructions gentle yet comprehensive to help you follow along. However, having some background in math and statistics will definitely enhance your learning experience. If you're passionate about data and its insights, this is the guide for you.

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

This book provides a pragmatic, methodical and easy-to-follow presentation of numerical methods and their effective implementation using MATLAB, which is introduced at the outset.

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.

Bridging theory and practice, this book contains over 200 practical exercises and their solutions, to develop the problem-solving abilities of process engineers. The problems were developed by the author during his many years of teaching at university and are kept brief, taken from the fields of instrumentation, modelling, plant control, control strategy design and stability of control. The algorithm flows and codes, which are mostly based on MATLAB®, are given in many cases and allow for easy translation into applications. Since the text is structured according to "Applied Process Control: Essential Methods", all of the necessary background information on the underlying methods can be easily and quickly found in this accompanying book.

This book is designed for undergraduate and graduate students taking a first course in Dynamics of Structures, Structural Dynamics or Earthquake Engineering. It includes several topics on the theory of structural dynamics and the applications of this theory to the analysis of buildings, bridges, towers and other structures subjected to dynamic and earthquake forces. This comprehensive text demonstrates the applications of numerical solution techniques to a large variety of practical, real-world problems under dynamic loads.

About The Authors –

Dr Ashok K. Jain is Professor of Civil Engineering at the Indian Institute of Technology Roorkee (formerly University of Roorkee), obtained his B.E. and M.E. degrees with honours from the University of Roorkee in 1972 and 1974, and a doctorate degree from the University of Michigan, Ann Arbor, in 1978. His main areas of interest include multistoreyed buildings, concrete and steel bridges, and nonlinear seismic response of structures. Besides teaching and research, he has been a structural consultant to various state and central government agencies as well as many private companies. A recipient of several awards, he has been a research fellow at the University of Michigan; a visiting Professor at the McGill University, Montreal; Director, Malaviya National Institute of Technology, Jaipur; and Head of Civil Engineering Department, I.I.T. Roorkee.

Book Contents –

Part 1 Single degree of Freedom Systems Part 2 Multi-degree of Freedom Systems Part 3 Application to Earthquake Engineering Part 4 Wind Load Appendix 1 Measuring Earthquakes: Magnitude and Intensity Appendix 2 MATLAB Basics Answers to Selected Problems Index

Essential MATLAB for Engineers and Scientists, Sixth Edition, provides a concise, balanced overview of MATLAB's functionality that facilitates independent learning, with coverage of both the fundamentals and applications. The essentials of MATLAB are illustrated throughout, featuring complete coverage of the software's windows and menus. Program design and algorithm development are presented clearly and intuitively, along with many examples from a wide range of familiar scientific and engineering areas. This updated edition includes the latest MATLAB versions through 2016a, and is an ideal book for a first course on MATLAB, or for an engineering problem-solving course using MATLAB, as well as a self-learning tutorial for professionals and students expected to learn and apply MATLAB. Updated to include all the newer features through MATLAB R2016a Includes new chapter on complex variables analysis Presents a comparison of execution time between compiled and un-compiled code that includes examples Describes the new H2 graphics features

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language Presents case studies illustrating key concepts across multiple fields Includes source code, sample datasets, and lecture slides

In Engineering theory and applications, we think and operate in terms of logics and models with some acceptable and reasonable assumptions. The present text is aimed at providing modelling and analysis techniques for the evaluation of reliability measures (2-terminal, all-terminal, k-terminal reliability) for systems whose structure can be described in the form of a probabilistic graph. Among the several approaches of network reliability evaluation, the multiple-variable-inversion sum-of-disjoint product approach finds a well-deserved niche as it provides the reliability or unreliability expression in a most efficient and compact manner. However, it does require an efficiently enumerated minimal inputs (minimal path, spanning tree, minimal k-trees, minimal cut, minimal global-cut, minimal k-cut) depending on the desired reliability. The present book covers these two aspects in detail through the descriptions of several algorithms devised by the ‘reliability fraternity’ and explained through solved examples to obtain and evaluate 2-terminal, k-terminal and all-terminal network reliability/unreliability measures and could be its USP. The accompanying web-based supplementary information containing modifiable Matlab® source code for the algorithms is another feature of this book. A very concerted effort has been made to keep the book ideally suitable for first course or even for a novice stepping into the area of network reliability. The mathematical treatment is kept as minimal as possible with an assumption on the readers’ side that they have basic knowledge in graph theory, probabilities laws, Boolean laws and set theory.

This book shows how constrained principal component analysis (CPCA) offers a unified framework for regression techniques and PCA. Keeping the use of complicated iterative methods to a minimum, the book includes implementation details and many real application examples. It also offers material for methodologically oriented readers interested in developing statistical techniques of their own. MATLAB programs as well as data to create the book's examples are available on the author's website.