Recently MIT announced the latest research in compressed sensing that may soon be saving us money for a wide range of imaging and signal-processing applications. Compressed sensing is a new technique in signal processing and imaging that takes a weighted linear combination of samples. Using simple linear equations, these few samples can represent the complete signal. These advances may reshape the way we work with large data sets.

For applications where the initial signal is sparse such as imaging for MRI and X-ray CT, it can reduce bandwidth, saving money. Imagine MRI machines that take seconds to produce images that used to take up to an hour. Suddenly, data becomes easier to gather, manipulate, and interpret. Depending on the application, hardware may require re-engineering to perform this compressed sampling.

Additional information:

• https://news.mit.edu/2013/toward-practical-compressed-sensing-0201
• https://www.wired.com/2010/02/ff_algorithm/
• https://news.mit.edu/2012/faster-fourier-transforms-0118
• https://arxiv.org/abs/1201.2501v1
• https://en.wikipedia.org/wiki/Compressed_sensing
• https://nuit-blanche.blogspot.com/2013/02/negative-reviews-and-weak-minded.html