Compressed Sensing

The Nyquist-Shannon sampling theorem states that a signal can be reconstructed perfectly if the highest frequency is less than half the sampling rate. In 2004, researchers found that with knowledge about a signal’s sparsity, a signal can be reconstructed with even fewer samples, a process called compressed sensing. The lower sampling rate makes storing and processing this data much more efficient.

Some of the applications of this insight include mobile phone cameras, holography, facial recognition, medical imaging, network tomography and radio astronomy.