Hao Tang – författare

The 100th anniversary of the first publication of Wittgenstein’s Tractatus is celebrated by a collection of original papers by well-known experts on various aspects of one of the greatest works of philosophy in the twentieth century.

Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring.This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.Introduces deep learning for classification, covering recent improvements in image segmentation and encoding priors, anomaly detection and target recognition, and domain adaptabilityIncludes both learning representations and unsupervised deep learning, covering deep learning picture fusion, regression, parameter retrieval, forecasting, and interpolation from a practical standpointProvides a number of physics-aware deep learning models, including the code and the parameterization of models on a companion website, as well as links to relevant data repositories, allowing readers to test techniques themselves

Deep Learning for Earth Observation and Climate Monitoring bridges the gap between deep learning and the Earth sciences, offering cutting-edge techniques and applications that are transforming our understanding of the environment. With a focus on practical scenarios, this book introduces readers to the fundamental concepts of deep learning, from classification and image segmentation to anomaly detection and domain adaptability. The book includes practical discussion on regression, parameter retrieval, forecasting, and interpolation, among other topics. With a solid foundational theory, real-world examples, and example codes, it provides a full understanding of how intelligent systems can be applied to enhance Earth observation and especially climate monitoring.This book allows readers to apply learning representations, unsupervised deep learning, and physics-aware models to Earth observation data, enabling them to leverage the power of deep learning to fully utilize the wealth of environmental data from satellite technologies.- Introduces deep learning for classification, covering recent improvements in image segmentation and encoding priors, anomaly detection and target recognition, and domain adaptability- Includes both learning representations and unsupervised deep learning, covering deep learning picture fusion, regression, parameter retrieval, forecasting, and interpolation from a practical standpoint- Provides a number of physics-aware deep learning models, including the code and the parameterization of models on a companion website, as well as links to relevant data repositories, allowing readers to test techniques themselves

The 100th anniversary of the first publication of Wittgenstein’s Tractatus is celebrated by a collection of original papers by well-known experts on various aspects of one of the greatest works of philosophy in the twentieth century.

This special compendium provides a concise and unified vision of facial image processing. It addresses a collection of state-of-the-art techniques, covering the most important areas for facial biometrics and behavior analysis. These techniques also converge to serve an emerging practical application of interactive distance learning.Readers will get a broad picture of the fundamental science of the field and technical details that make the research interesting. Moreover, the intellectual investigation motivated by the demand of real-life application will make this volume an inspiring read for current and prospective researchers and engineers in the fields of computer vision, machine learning and image processing.

How secure are intelligent computing networks (ICNs) when data, algorithms, and computing power intertwine? As ICNs become the backbone of modern digital ecosystems, their growing complexity introduces unprecedented risks in security and safety. Integrated Security and Safety of Intelligent Computing Networks offers a groundbreaking, coupling-aware perspective that redefines how we understand and fortify ICN infrastructures.This book goes beyond conventional security frameworks by examining the intricate, dynamic relationships among the three pillars of ICNs—data, computing power, and algorithms. It provides a systematic exploration of how their deep coupling can both empower and endanger these systems. Through a well-structured progression from foundational theories to real-world case studies, it uncovers the limitations of current defenses and introduces innovative, scenario-driven methodologies that address distributed attacks, data poisoning, and algorithmic vulnerabilities. From edge computing threats to federated learning backdoors, the book bridges theoretical modeling with practical solutions across system, data, and algorithm planes.Designed for researchers, engineers, and graduate students specializing in cybersecurity, intelligent systems, and next-generation computing, this monograph delivers essential insights and tools to build trustworthy ICNs. With a strong focus on coupling-aware design and integrated defense strategies, it provides not only technical depth but also strategic foresight—empowering readers to navigate and shape the future of secure intelligent infrastructures.