Sparsity and Compressed Sensing

Research and implementation of compressed sensing algorithms and sparse signal recovery techniques. This project explores the mathematical foundations of sparse representations and their applications in signal processing, including the development of efficient algorithms for signal reconstruction from incomplete measurements. The work demonstrates how sparsity can be leveraged to dramatically reduce sampling requirements while maintaining signal fidelity.