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.

Название: MATLAB Image Acquisition Toolbox User’s Guide (R2023b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 666
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Acquire images and video from industry-standard hardware. Image Acquisition Toolbox provides functions and blocks for connecting cameras to MATLAB and Simulink. It includes a MATLAB app that lets you interactively detect and configure hardware properties. You can then generate equivalent MATLAB code to automate your acquisition in future sessions. The toolbox enables acquisition modes such as processing in-the-loop, hardware triggering, background acquisition, and synchronizing acquisition across multiple devices. Image Acquisition Toolbox supports all major standards and hardware vendors, including USB3 Vision, GigE Vision, and GenICam GenTL. You can connect to machine vision cameras and frame grabbers, as well as high-end scientific and industrial devices.

Название: MATLAB Statistics and Machine Learning Toolbox User’s Guide (R2023b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 1176
Язык: английский
Формат: pdf (true)
Размер: 53.4 MB

Analyze and model data using statistics and Machine Learning. Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML. For multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power. The toolbox provides supervised, semi-supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, k-means, and other clustering methods. You can apply interpretability techniques such as partial dependence plots and LIME, and automatically generate C/C++ code for embedded deployment. Many toolbox algorithms can be used on data sets that are too big to be stored in memory. MATLAB Coder generates readable and portable C and C++ code from Statistics and Machine Learning Toolbox functions that support code generation. You can integrate the generated code into your projects as source code, static libraries, or dynamic libraries.

Название: MATLAB Control System Toolbox User's Guide (R2023b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 1906
Язык: английский
Формат: pdf (true)
Размер: 26.4 MB

In Control System Toolbox, System Identification ToolboxTM, and Robust Control ToolboxTM software, you represent linear systems as model objects. In System Identification Toolbox, you also represent nonlinear models as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems.

Название: MATLAB Medical Imaging Toolbox User's Guide (R2023b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 282
Язык: английский
Формат: pdf (true)
Размер: 12.3 MB

Visualize, register, segment, and label 2D and 3D medical images. Medical Imaging Toolbox provides apps, functions, and workflows for designing and testing diagnostic imaging applications. You can perform 3D rendering and visualization, multimodal registration, and segmentation and labeling of radiology images. The toolbox also lets you train predefined Deep Learning networks (with Deep Learning Toolbox). You can import, preprocess, and analyze radiology images from various imaging modalities, including projected X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine (PET, SPECT). The Medical Image Labeler app lets you semi-automate 2D and 3D labeling for use in AI workflows. You can perform multimodal registration of medical images, including 2D images, 3D surfaces, and 3D volumes. The toolbox provides an integrated environment for end-to-end computer-aided diagnosis and medical image analysis.

Название: Signals and Systems with MATLAB and Simulink
Автор: Fаrzin Аsаdi
Издательство: Springer
Год: 2024
Страниц: 479
Язык: английский
Формат: pdf (true)
Размер: 27.6 MB

This textbook provides a compact but comprehensive treatment that guides students to solve Signals and Systems problems using MATLAB/Simulink. Ideal as a hands-on source for courses in Signals and Systems or Control Systems, this text focuses on solving problems using market-standard software, corresponding to all key concepts covered in the classroom. The author uses his extensive classroom experience to guide students toward deeper understanding of key concepts, while they gain facility with software they will need to master for later studies and practical use in their engineering careers. A signal is the variation of a physical, or non-physical, quantity with respect to one or more independent variable(s). Signals typically carry information that is somehow relevant for some purpose. System is defined as any process in which input signals are transformed to output signals. The study of signals and systems is, by necessity, highly mathematical since its focus is to allow one to predict, with some certainty, the behavior of systems when they are subjected to different input signals. Signals and systems course is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, and consumer electronics. In this course, you will examine signal and systems analysis in continuous and discrete time.

Название: Теория информации. Лабораторный практикум в MATLAB
Автор: A.И. Пpиxoдькo
Издательство: Инфра-Инженерия
Год: 2022
Страниц: 108
Язык: русский
Формат: pdf
Размер: 20.7 MB

Приводятся основные положения теории информации. Содержатся теоретические сведения, расчетные задания с примерами m-файлов, а также методические указания и контрольные вопросы. Основные разделы теории информации — кодирование источника (сжимающее кодирование) и канальное (помехоустойчивое) кодирование. Теория информации тесно связана с информационной энтропией, коммуникационными системами, криптографией и другими смежными дисциплинами. Для студентов, обучающихся по направлениям подготовки «Инфокоммуникационные технологии и системы связи» и «Радиотехника». Может быть полезно преподавателям.

Название: An Introduction to Scientific Computing: Fifteen Computational Projects Solved with MATLAB, 2nd Edition
Автор: Iоnut Dаnаilа, Раsсаl Jоlу, Sidi Маhmоud Каbеr
Издательство: Springer
Год: 2023
Страниц: 379
Язык: английский
Формат: pdf (true), epub
Размер: 60.1 MB

This book demonstrates scientific computing by presenting twelve computational projects spanning a broad spectrum of disciplines including Fluid Mechanics, Chemistry, Elasticity, Thermal Science, Computer Aided Design, Signal and Image Processing and more. Each project guides the reader through typical steps of scientific computing, from physical and mathematical description of the problem, to numerical formulation and programming and finally to critical discussion of results. The text teaches practical computational methods which are not usually available in basic textbooks: numerical checking of accuracy or stability, choice of boundary conditions, effective solving of linear systems, comparison to exact solutions and more. Programming techniques such as vectorial programming and memory storage optimization are addressed, and chapter-ending references form a guide for further reading. The final section of each project contains the solutions to proposed exercises and guides the reader in using the MATLAB scripts available online. We consider that an interpreted language (such as MATLAB, SCILAB, and OCTAVE) is the ideal framework to start a scientific programming activity.

Название: An Introduction to Optimization with Applications in Machine Learning and Data Analytics
Автор: Jеffrеу Раul Whееlеr
Издательство: CRC Press
Год: 2024
Страниц: 475
Язык: английский
Формат: pdf (true)
Размер: 22.4 MB

The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, Computer Science, economics, statistics, and business. Optimization reaches into many different fields. Many of the problems rely on using a computer. Microsoft’s Excel is most often used, as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB or Mathematica, and the computer science students to write their own programs in Java or Python.