This book offers a fresh perspective on mathematical analysis, integrating concepts from wavelets, fractals, and signals. It emphasizes intuitive and geometric ideas, making advanced topics accessible without requiring extensive prerequisites. The blend of diverse themes enriches understanding and reflects modern trends in applied mathematics.
This course in analysis offers a unique approach by connecting traditional mathematical concepts to modern themes like wavelets, fractals, and probabilistic elements, which are often overlooked in typical graduate studies. The book presents a cohesive blend of diverse topics, illustrating how applied trends are reshaping the boundaries of mathematics. Importantly, it maintains an intuitive and visual style, requiring only a minimal background, making it accessible to students without additional prerequisites.
Focusing on innovative frame theory and its application in data science, this book delves into spatial-scale feature extraction, network dynamics, and data-driven environmental predictions. It highlights the importance of these techniques in advancing multi-channel data mining systems, crucial for achieving the United Nations' Sustainable Development Goals. Drawing from two decades of research, it offers advanced methodologies beneficial for scientists, professionals, and graduate students in data science, applied mathematics, environmental science, and geoscience.