A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4Key FeaturesGain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learnGet up to speed with Intel OpenVINO and its integration with OpenCV 4Implement high-performance machine learning models with helpful tips and best practicesBook DescriptionOpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.What you will learnUnderstand the core machine learning concepts for image processingExplore the theory behind machine learning and deep learning algorithm designDiscover effective techniques to train your deep learning modelsEvaluate machine learning models to improve the performance of your modelsIntegrate algorithms such as support vector machines and Bayes classifier in your computer vision applicationsUse OpenVINO with OpenCV 4 to speed up model inferenceWho this book is forThis book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.

Fundamental and advanced RF sputtering techniques for thin-film applications Controlling film thickness, composition, and uniformity remains a persistent challenge in thin-film fabrication. Radio Frequency Sputtering: Thin-Film Deposition and Device Applications covers both fundamental principles and advanced techniques of RF sputtering. Written by researchers from academic and industry laboratories, this reference provides practical guidance for optimizing deposition processes across electronic components, solar cells, and functional coatings. The book provides step-by-step instructions for RF sputtering processes alongside clear definitions and illustrative examples. Dedicated sections on troubleshooting and process optimization offer targeted advice for overcoming common deposition challenges. Coverage extends to sustainability practices in sputtering operations and examines both established and emerging device applications, connecting process parameters to measurable outcomes in device performance. Readers will also find: Detailed treatment of RF magnetron sputtering configurations and their influence on film quality across different substrate materialsGuidance on controlling deposition parameters to achieve target film thickness, composition, and structural uniformity in production environmentsCoverage of thin-film applications in semiconductor devices, energy harvesting systems, and advanced functional coatings for diverse industriesDiscussion of emerging applications including spintronic devices, memory technologies, and materials designed for high-radiation environmentsPractical frameworks for integrating sustainability considerations into RF sputtering workflows and laboratory or manufacturing operationsRadio Frequency Sputtering serves materials scientists, semiconductor physicists, and engineers working in thin-film fabrication across academic and industrial settings. By connecting deposition fundamentals with device-level outcomes, this reference supports practitioners seeking to optimize RF sputtering processes for both research and production applications.

A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4Key FeaturesGain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learnGet up to speed with Intel OpenVINO and its integration with OpenCV 4Implement high-performance machine learning models with helpful tips and best practicesBook DescriptionOpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.What you will learnUnderstand the core machine learning concepts for image processingExplore the theory behind machine learning and deep learning algorithm designDiscover effective techniques to train your deep learning modelsEvaluate machine learning models to improve the performance of your modelsIntegrate algorithms such as support vector machines and Bayes classifier in your computer vision applicationsUse OpenVINO with OpenCV 4 to speed up model inferenceWho this book is forThis book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.

Fundamental and advanced RF sputtering techniques for thin-film applications Controlling film thickness, composition, and uniformity remains a persistent challenge in thin-film fabrication. Radio Frequency Sputtering: Thin-Film Deposition and Device Applications covers both fundamental principles and advanced techniques of RF sputtering. Written by researchers from academic and industry laboratories, this reference provides practical guidance for optimizing deposition processes across electronic components, solar cells, and functional coatings. The book provides step-by-step instructions for RF sputtering processes alongside clear definitions and illustrative examples. Dedicated sections on troubleshooting and process optimization offer targeted advice for overcoming common deposition challenges. Coverage extends to sustainability practices in sputtering operations and examines both established and emerging device applications, connecting process parameters to measurable outcomes in device performance. Readers will also find: Detailed treatment of RF magnetron sputtering configurations and their influence on film quality across different substrate materialsGuidance on controlling deposition parameters to achieve target film thickness, composition, and structural uniformity in production environmentsCoverage of thin-film applications in semiconductor devices, energy harvesting systems, and advanced functional coatings for diverse industriesDiscussion of emerging applications including spintronic devices, memory technologies, and materials designed for high-radiation environmentsPractical frameworks for integrating sustainability considerations into RF sputtering workflows and laboratory or manufacturing operationsRadio Frequency Sputtering serves materials scientists, semiconductor physicists, and engineers working in thin-film fabrication across academic and industrial settings. By connecting deposition fundamentals with device-level outcomes, this reference supports practitioners seeking to optimize RF sputtering processes for both research and production applications.