This new edition provides an updated approach for students, engineers, and researchers to apply numerical methods for solving problems using MATLAB® This accessible book makes use of MATLAB® software to teach the fundamental concepts for applying numerical methods to solve practical engineering and/or science problems. It presents programs in a complete form so that readers can run them instantly with no programming skill, allowing them to focus on understanding the mathematical manipulation process and making interpretations of the results. Applied Numerical Methods Using MATLAB®, Second Edition begins with an introduction to MATLAB usage and computational errors, covering everything from input/output of data, to various kinds of computing errors, and on to parameter sharing and passing, and more. The system of linear equations is covered next, followed by a chapter on the interpolation by Lagrange polynomial. The next sections look at interpolation and curve fitting, nonlinear equations, numerical differentiation/integration, ordinary differential equations, and optimization. Numerous methods such as the Simpson, Euler, Heun, Runge-kutta, Golden Search, Nelder-Mead, and more are all covered in those chapters. The eighth chapter provides readers with matrices and Eigenvalues and Eigenvectors. The book finishes with a complete overview of differential equations. Provides examples and problems of solving electronic circuits and neural networks Includes new sections on adaptive filters, recursive least-squares estimation, Bairstow's method for a polynomial equation, and more Explains Mixed Integer Linear Programing (MILP) and DOA (Direction of Arrival) estimation with eigenvectors Aimed at students who do not like and/or do not have time to derive and prove mathematical results Applied Numerical Methods Using MATLAB®, Second Edition is an excellent text for students who wish to develop their problem-solving capability without being involved in details about the MATLAB codes. It will also be useful to those who want to delve deeper into understanding underlying algorithms and equations.
Provides practical examples of circuit design and analysis using PSpice, MATLAB, and the Smith Chart This book presents the three technologies used to deal with electronic circuits: MATLAB, PSpice, and Smith chart. It gives students, researchers, and practicing engineers the necessary design and modelling tools for validating electronic design concepts involving bipolar junction transistors (BJTs), field-effect transistors (FET), OP Amp circuits, and analog filters. Electronic Circuits with MATLAB®, PSpice®, and Smith Chart presents analytical solutions with the results of MATLAB analysis and PSpice simulation. This gives the reader information about the state of the art and confidence in the legitimacy of the solution, as long as the solutions obtained by using the two software tools agree with each other. For representative examples of impedance matching and filter design, the solution using MATLAB and Smith chart (Smith V4.1) are presented for comparison and crosscheck. This approach is expected to give the reader confidence in, and a deeper understanding of, the solution. In addition, this text: Increases the reader's understanding of the underlying processes and related equations for the design and analysis of circuits Provides a stepping stone to RF (radio frequency) circuit design by demonstrating how MATLAB can be used for the design and implementation of microstrip filters Features two chapters dedicated to the application of Smith charts and two-port network theory Electronic Circuits with MATLAB®, PSpice®, and Smith Chart will be of great benefit to practicing engineers and graduate students interested in circuit theory and RF circuits.
This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
The uncertainties in the aerosol effects on radiative forcing limit our knowledge of climate change, presenting us with an important research challenge. Aerosols in Atmospheric Chemistry introduces basic concepts about the characterization, formation, and impacts of ambient aerosol particles as an introduction to graduate students new to the field. Each chapter also provides an up-to-date synopsis of the latest knowledge of aerosol particles in atmospheric chemistry.
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