Cross-Scale Coupling and Energy Transfer in the Magnetosphere-Ionosphere-Thermosphere System provides a systematic understanding of Magnetosphere-Ionosphere-Thermosphere dynamics. Cross-scale coupling has become increasingly important in the Space Physics community. Although large-scale processes can specify the averaged state of the system reasonably well, they cannot accurately describe localized and rapidly varying structures in space in actual events. Such localized and variable structures can be as intense as the large-scale features. This book covers observations on quantifying coupling and energetics and simulation on evaluating impacts of cross-scale processes. It includes an in-depth review and summary of the current status of multi-scale coupling processes, fundamental physics, and concise illustrations and plots that are usable in tutorial presentations and classrooms. Organized by physical quantities in the system, Cross-Scale Coupling and Energy Transfer in the Magnetosphere-Ionosphere-Thermosphere System reviews recent advances in cross-scale coupling and energy transfer processes, making it an important resource for space physicists and researchers working on the magnetosphere, ionosphere, and thermosphere.

This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.

This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.