Название: Image Processing Recipes in MATLAB Автор: Оgе Маrquеs, Gustаvо Веnvеnutti Воrbа Издательство: CRC Press Год: 2024 Страниц: 263 Язык: английский Формат: pdf (true) Размер: 32.5 MB Leveraging the latest developments in MATLAB and its image processing toolbox, this 'cookbook' is a collection of 30 practical recipes for image processing, ranging from foundational techniques to recently published algorithms. Presented in a clear and meaningful sequence, these recipes are prepared with the reader in mind, allowing one to focus on particular topics or read as a whole from cover to cover. This is a cookbook containing 30 recipes that showcase classic and modern image processing techniques using MATLAB. This book aims to provide a concise and easily understandable reference for deploying image processing pipelines quickly and efficiently in MATLAB. The recipes cover the latest developments in MATLAB and relevant toolboxes, including a wide range of image processing methods. These methods range from foundational techniques found in textbooks to popular contemporary algorithms. The book serves as a concise and readable practical reference to deploy image processing pipelines in MATLAB quickly and efficiently. With its accessible and practical approach, the book is a valuable guide for those who navigate this evolving area, including researchers, students, developers, and practitioners in the fields of image processing, computer vision, and image analysis.
Название: Electrical Drive Simulation with MATLAB/Simulink: Selected Technologies Автор: Viktоr Реrеlmutеr Издательство: CRC Press Год: 2024 Страниц: 273 Язык: английский Формат: pdf (true) Размер: 34.3 MB The book discusses the modeling of electric drives, taking into account their relationship with the technological process they serve, which significantly affects the composition, layout and characteristics of the electric drive. There are no published books of this kind, and this book fills a gap in the literature. The book deals with electric drives of rolling mills, paper machines, a number of hoisting and transport devices; these installations are very common and very complex, so that modeling methods in their development and study are mandatory. More than 100 models of the electrical drives that are made with use of the program environment MATLAB/Simulink, are appended to the book. The aims of these models are to aid students studying electrical drives of the various manufacturing machines, to facilitate the understanding of various electrical drive functions; and to create a platform for the development of systems by readers in their fields. Modern electric drives of many production processes and mechanisms are very complex devices, and the availability of modern and effective methods of mathematical modeling of such systems can significantly facilitate and speed up the creation of such electric drives. The graphical programming language MATLAB/Simulink and the set of Electrical/Specialized Power Systems blocks included in it greatly facilitate the creation of models of electrical engineering objects.
Название: MATLAB Machine Learning Recipes: A Problem-Solution Approach, 3rd Edition Автор: Мiсhаеl Раluszеk, Stерhаniе Тhоmаs Издательство: Apress Год: 2024 Страниц: 458 Язык: английский Формат: pdf Размер: 16.8 MB Harness the power of MATLAB to resolve a wide range of Machine Learning challenges. This new and updated third edition provides examples of technologies critical to Machine Learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in Machine Learning. It also has material that will allow those with an interest in other technology areas to see how Machine Learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.
Название: Radar and Ew Modeling in Matlab and Simulink Автор: Саrlоs А. Dаvilа, Glеnn D. Норkins, Grеgоrу А. Shоwmаn Издательство: Artech House Год: 2024 Страниц: 503 Язык: английский Формат: pdf (true) Размер: 31.2 MB This book develops modeling and simulation (M&S) techniques for modern radars and electronic warfare (EW) systems. It covers basic concepts and modeling examples for the three "pillars" of EW: electronic attack (EA) systems, electronic protection (EP) techniques, and electronic support (ES). The book provides a comprehensive overview of the M&S techniques used in these systems, and its many examples and case studies provide a solid foundation for understanding how these techniques can be applied in practice. A valuable resource for engineers, scientists, and managers who are involved in the design, development, or testing of radar and EW systems. It provides a comprehensive overview of the M&S techniques that are used in these systems, and the book's many examples and case studies provide a solid foundation for understanding how these techniques can be applied in practice.
Название: Statistics and Data Analysis for Engineers and Scientists Автор: Таnvir Мustаfу, Мd. Таuhid Ur Rаhmаn Издательство: Springer Год: 2024 Страниц: 190 Язык: английский Формат: pdf Размер: 10.1 MB This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and Machine Learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.
Автор: Гольдштейн А.Л. Название: Оптимизация в среде MATLAB: учебное пособие Издательство: Пермь: Изд-во Пермского национального исследовательского политехнического университета Год: 2015 ISBN: 978-5-398-01361-0 Язык: Русский Формат: djvu Размер: 22,5 Мб Качество: хорошее, текстовый слой, оглавление. Кол-во страниц: 192 Описание: Система компьютерной математики Matlab имеет развитый высокоуровневый язык программирования и прекрасную двумерную и трехмерную графику, позволяющую наглядно представлять результаты расчетов, экспериментов и процессы моделирования.
Название: Fractal Patterns with MATLAB Автор: Sаntо Ваnеrjее, А. Gоwrisаnkаr, Коmаndlа Маhiраl Rеddу Издательство: Springer Год: 2024 Язык: английский Формат: pdf (true), epub Размер: 26.3 MB This book presents the iterative beauty of fractals and fractal functions graphically with the aid of MATLAB programming. The fractal images generated using the MATLAB codes provide visual delight and highly encourage the fractal lovers for creative thinking. The book compiles five cutting-edge research chapters, each with state-of-the art fractal illustrations. It starts with the fundamental theory for the construction of fractal sets via the deterministic iteration algorithm. Incorporating the theoretical base, fractal illustrations of elementary fractal sets are provided with the explicit MATLAB code. The book gives examples of MATLAB codes to present the fractal surfaces. This book is contributed to all the research beginners as well as the professionals on the field of fractal analysis. As it covers basic fractals like Sierpinski triangle to advanced fractal functions with explicit MATLAB code, the presented fractal illustrations hopefully benefit even the non-field readers. The book is a useful course to all the research beginners on the fractal and fractal-related fields.
Название: Image Processing and Machine Learning, Volume 1: Foundations of Image Processing Автор: Еrik Сuеvаs, Аlmа Nауеli Rоdríguеz Издательство: CRC Press Год: 2024 Страниц: 225 Язык: английский Формат: pdf (true) Размер: 40.9 MB Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. Our primary objective was to create a comprehensive textbook that serves as an invaluable resource for an image processing class. With this goal in mind, we carefully crafted a book that encompasses both the theoretical foundations and practical applications of the most prevalent image processing methods. From pixel operations to geometric transformations, spatial filtering to image segmentation, and edge detection to color image processing, we have meticulously covered a wide range of topics essential to understanding and working with images. Moreover, recognizing the increasing relevance of ML in image processing, we have incorporated fundamental ML concepts and their applications in this field. By introducing readers to these concepts, we aim to equip them with the necessary knowledge to leverage ML techniques for various image processing tasks. Volume 1 is organized in a way that allows readers to easily understand the goal of each chapter and reinforce their understanding through practical exercises using MATLAB programs.
Название: Image Processing and Machine Learning, Volume 2: Advanced Topics in Image Analysis and Machine Learning Автор: Еrik Сuеvаs, Аlmа Nауеli Rоdríguеz Издательство: CRC Press Год: 2024 Страниц: 239 Язык: английский Формат: pdf (true) Размер: 31.6 MB Image processing and Machine Learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, Machine Learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important Machine Learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1. Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn from data and make informed predictions or decisions without the need for explicit programming. ML finds extensive applications in various domains. For instance, in automation, ML algorithms can automate tasks that would otherwise rely on human intervention, thereby reducing errors and enhancing overall efficiency. Predictive analytics is another area where ML plays a crucial role. By analyzing vast datasets, ML models can detect patterns and make predictions, facilitating applications such as stock market analysis, fraud detection, and customer behavior analysis. We have observed that students grasp the material more effectively when they have access to code that they can manipulate and experiment with. In line with this, our book utilizes MATLAB as the programming language for implementing the systems.
Название: MATLAB Parallel Computing Toolbox User’s Guide (R2023b) Автор: MathWorks Издательство: The MathWorks, Inc. Год: September 2023 Страниц: 1216 Язык: английский Формат: pdf (true) Размер: 10.1 MB Perform parallel computations on multicore computers, GPUs, and computer clusters. Parallel Computing Toolbox lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to parallelize MATLAB applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes. The toolbox lets you use the full processing power of multicore desktops by executing applications on workers (MATLAB computational engines) that run locally. Without changing the code, you can run the same applications on clusters or clouds (using MATLAB Parallel Server). You can also use the toolbox with MATLAB Parallel Server to execute matrix calculations that are too large to fit into the memory of a single machine. Parallel Computing Toolbox provides you with tools for a local cluster of workers on your client machine. MATLAB Parallel Server software allows you to run as many MATLAB workers on a remote cluster of computers as your licensing allows.
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