Explore MATLAB source code curated for "数学" with clean implementations, documentation, and examples.
Application of the Analytic Hierarchy Process (AHP) to resolve challenges in mathematics and economics, including AHP program implementation and detailed explanations of AHP modeling techniques.
About Compressed Sensing Reconstruction Algorithms - Compressed Sensing (CS), also known as Compressive Sampling, is an emerging interdisciplinary field between mathematics and information science that has gained popularity in recent years. Proposed by researchers including Candès and Terence Tao, CS challenges conventional sampling and encoding techniques based on the Nyquist-Shannon sampling theorem. The core implementation involves sparse signal reconstruction through optimization algorithms like L1-minimization, with key functions including measurement matrix design and reconstruction solvers.
Application Background: Image encryption represents a critical research domain where conventional data encryption methods face challenges due to image-specific characteristics like pixel correlation and massive data volumes. While limited algorithms exist, some demonstrate strong performance under specific parameters. Multiple chaos mapping implementation algorithms will be explored through practical MATLAB coding examples. Key Technologies: Encryption, image encryption, data encryption, matrix operations, mathematical transformations, and chaos theory implementations.