Matlab

A Practical Introduction to Programming and Problem Solving

Matlab

MATLAB: A Practical Introduction to Programming and Problem Solving, Fourth Edition, winner of a 2017 Textbook Excellence Award (Texty), has been updated to reflect the functionality of the current version of MATLAB, including the new H2 Graphics system. It features new and revised end-of-chapter exercises, more engineering applications to help the reader learn this software tool in context, and a new section on object-oriented programming in MATLAB. MATLAB has become the standard software tool for solving scientific and engineering problems due to its powerful built-in functions and its ability to program. Assuming no knowledge of programming, this book guides the reader through both programming and built-in functions to easily exploit MATLAB's extensive capabilities for tackling engineering problems. The book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. In-depth coverage is given to input/output, a topic fundamental to many engineering applications. Winner of a 2017 Textbook Excellence Award (Texty) from the Textbook and Academic Authors Association Presents programming concepts and MATLAB built-in functions side-by-side Offers a systematic, step-by-step approach, building on concepts throughout the book and facilitating easier learning Includes sections on common pitfalls and programming guidelines to direct students toward best practices Combines basic programming concepts, built-in functions, and advanced topics for problem solving with MATLAB to make this book uniquely suitable for a wide range of courses teaching or using MATLAB across the curriculum

Numerical Methods

Using MATLAB

Numerical Methods

The fourth edition of Numerical Methods Using MATLAB® provides a clear and rigorous introduction to a wide range of numerical methods that have practical applications. The authors’ approach is to integrate MATLAB® with numerical analysis in a way which adds clarity to the numerical analysis and develops familiarity with MATLAB®. MATLAB® graphics and numerical output are used extensively to clarify complex problems and give a deeper understanding of their nature. The text provides an extensive reference providing numerous useful and important numerical algorithms that are implemented in MATLAB® to help researchers analyze a particular outcome. By using MATLAB® it is possible for the readers to tackle some large and difficult problems and deepen and consolidate their understanding of problem solving using numerical methods. Many worked examples are given together with exercises and solutions to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization and many other fields. The text will be a valuable aid to people working in a wide range of fields, such as engineering, science and economics. Features many numerical algorithms, their fundamental principles, and applications Includes new sections introducing Simulink, Kalman Filter, Discrete Transforms and Wavelet Analysis Contains some new problems and examples Is user-friendly and is written in a conversational and approachable style Contains over 60 algorithms implemented as MATLAB® functions, and over 100 MATLAB® scripts applying numerical algorithms to specific examples

Object Detection and Recognition in Digital Images

Theory and Practice

Object Detection and Recognition in Digital Images

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

MATLAB® Recipes for Earth Sciences

MATLAB® Recipes for Earth Sciences

MATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data and image analysis. The revised and updated Fourth Edition includes sixteen new sections and most chapters have greatly been expanded so that they now include a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Array Manipulation; Control Flow; Creating Graphical User Interfaces; Hypothesis Testing; Kolmogorov-Smirnov Test; Mann-Whitney Test; Ansari-Bradley Test; Detecting Abrupt Transitions in Time Series; Exporting 3D Graphics to Create Interactive Documents; Importing, Processing and Exporting LANDSAT Images; Importing and Georeferencing TERRA ASTER Images; Processing and Exporting EO-1 Hyperion Images; Image Enhancement; Correction and Rectification; Shape-Based Object Detection in Images; Discriminant Analysis; and Multiple Linear Regression. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. The book’s supplementary electronic material (available online through Springer Link) includes recipes that include all the MATLAB commands featured in the book and the example data.

Introduction to Mathematical Modeling and Computer Simulations

Introduction to Mathematical Modeling and Computer Simulations

Introduction to Mathematical Modeling and Computer Simulations is written as a textbook for readers who want to understand the main principles of Modeling and Simulations in settings that are important for the applications, without using the profound mathematical tools required by most advanced texts. It can be particularly useful for applied mathematicians and engineers who are just beginning their careers. The goal of this book is to outline Mathematical Modeling using simple mathematical descriptions, making it accessible for first- and second-year students.

Introduction to Food Engineering

Introduction to Food Engineering

This fourth edition of this successful textbook succinctly presents the engineering concepts and unit operations used in food processing, in a unique blend of principles with applications. Depth of coverage is very high. The authors use their many years of teaching to present food engineering concepts in a logical progression that covers the standard course curriculum. Both are specialists in engineering and world-renowned. Chapters describe the application of a particular principle followed by the quantitative relationships that define the related processes, solved examples and problems to test understanding. Supplemental processes including filtration, sedimentation, centrifugation, and mixing Extrusion processes for foods Packaging concepts and shelf life of foods Expanded information on Emerging technologies, such as high pressure and pulsed electric field; Transport of granular foods and powders; Process controls and measurements; Design of plate heat exchangers; Impact of fouling in heat transfer processes; Use of dimensional analysis in understanding physical phenomena

Engineering Optimization

Theory and Practice

Engineering Optimization

Technology/Engineering/Mechanical Helps you move from theory to optimizing engineering systems in almost any industry Now in its Fourth Edition, Professor Singiresu Rao's acclaimed text Engineering Optimization enables readers to quickly master and apply all the important optimization methods in use today across a broad range of industries. Covering both the latest and classical optimization methods, the text starts off with the basics and then progressively builds to advanced principles and applications. This comprehensive text covers nonlinear, linear, geometric, dynamic, and stochastic programming techniques as well as more specialized methods such as multiobjective, genetic algorithms, simulated annealing, neural networks, particle swarm optimization, ant colony optimization, and fuzzy optimization. Each method is presented in clear, straightforward language, making even the more sophisticated techniques easy to grasp. Moreover, the author provides: Case examples that show how each method is applied to solve real-world problems across a variety of industries Review questions and problems at the end of each chapter to engage readers in applying their newfound skills and knowledge Examples that demonstrate the use of MATLAB® for the solution of different types of practical optimization problems References and bibliography at the end of each chapter for exploring topics in greater depth Answers to Review Questions available on the author's Web site to help readers to test their understanding of the basic concepts With its emphasis on problem-solving and applications, Engineering Optimization is ideal for upper-level undergraduates and graduate students in mechanical, civil, electrical, chemical, and aerospace engineering. In addition, the text helps practicing engineers in almost any industry design improved, more efficient systems at less cost.

Adaptive Filtering

Algorithms and Practical Implementation

Adaptive Filtering

In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.

Guide to Scientific Computing

Guide to Scientific Computing

Guide to Scientific Computing provides an introduction to the many problems of scientific computing, as well as the wide variety of methods used for their solution. It is ideal for anyone who needs an understanding of numerical mathematics or scientific computing - whether in mathematics, the sciences, engineering, or economics. This book provides an appreciation of the need for numerical methods for solving different types of problems, and discusses basic approaches. For each of the problems mathematical justification and examples provide both practical evidence and motivations for the reader to follow. Practical justification of the methods is presented through computer examples and exercises. The major effort of programming is removed from the reader, as are the harder parts of analysis, so that the focus is clearly on the basics. Since some algebraic manipulation is unavoidable, it is carefully explained when necessary, especially in the early stages. Guide to Scientific Computing includes an introduction to MATLAB, but the code used is not intended to exemplify sophisticated or robust pieces of software; it is purely illustrative of the methods under discussion. The book has an appendix devoted to the basics of the MATLAB package, its language and programming. The book provides an introduction to this subject which is not, in its combined demands of computing, motivation, manipulation, and analysis, paced such that only the most able can understand.