Mehul S. Raval – författare

Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data.Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB® programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications.In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data.Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB® programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications.In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS).

This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS).

Dieses Buch präsentiert die neuesten Erkenntnisse auf dem Gebiet des digitalen Ökosystems für Innovationen in der Landwirtschaft. Das Buch ist in zwei Abschnitte mit dreizehn Kapiteln unterteilt, die sich mit spezialisierten Bereichen befassen. Es gibt dem Leser einen Überblick über die Rahmenbedingungen und Technologien, die an der Digitalisierung der Landwirtschaft beteiligt sind, sowie über die Methoden zur Datenverarbeitung, Entscheidungsfindung und innovativen Dienste/Anwendungen zur Förderung digitaler Transformationen in der Landwirtschaft. Die Kapitel wurden von Experten verfasst, die ihre Erfahrungen in verständlicher Sprache durch Fallstudien, geeignete Illustrationen und Tabellen teilen. Der Inhalt wurde entwickelt, um die Bedürfnisse der Geoinformatik, Datenwissenschaften, Landwirtschafts- und Umweltwissenschaften von Universitäten, landwirtschaftlichen Universitäten, technologischen Universitäten, Forschungsinstituten und akademischen Hochschulen weltweit zu erfüllen.Es unterstützt Planer, politische Entscheidungsträger und Erweiterungswissenschaftler bei der Planung und nachhaltigen Bewirtschaftung von Landwirtschaft und natürlichen Ressourcen.

The book is organized into five sections, Understanding the Problem, Data Collection and Cleaning, Exploratory Data Analysis and Visualization, Model Building, and Model Deployment.

It provides the reader with an overview of the frameworks and technologies involved in the digitalization of agriculture, as well as the data processing methods, decision-making processes, and innovative services/applications for enabling digital transformations in agriculture.