Toward practical compressed sensing
By By Larry Hardesty, MIT News Office | 02 Feb 2013
The last 10 years have seen a flurry of research on an emerging technology called compressed sensing. Compressed sensing does something that seems miraculous - It extracts more information from a signal than the signal would appear to contain. One of the most celebrated demonstrations of the technology came in 2006, when Rice University researchers produced images with a resolution of tens of thousands of pixels using a camera whose sensor had only one pixel.
Compressed sensing promises dramatic reductions in the cost and power consumption of a wide range of imaging and signal-processing applications. But it's been slow to catch on commercially, in part because of a general scepticism that sophisticated math ever works as well in practice as it does in theory.
Researchers at MIT's Research Laboratory of Electronics (RLE) hope to change that, with a new mathematical framework for evaluating compressed-sensing schemes that factors in the real-world performance of hardware components.
''The people who are working on the theory side make some assumptions that circuits are ideal, when in reality, they are not,'' says Omid Abari, a doctoral student in the Department of Electrical Engineering and Computer Science (EECS) who led the new work. ''On the other hand, it's very costly to build a circuit, in terms of time and also money. So this work is a bridge between these two worlds. Theory people could improve algorithms by considering circuit non-idealities, and the people who are building a chip could use this framework and methodology to evaluate the performance of those algorithms or systems. And if they see their potential, they can build a circuit.''
Mixed reviews
In a series of recent papers, four members of associate professor Vladimir Stojanovic's Integrated Systems Group at RLE - Abari, Stojanovic, post-doc Fabian Lim and recent graduate Fred Chen - applied their methodology to two applications where compressed sensing appeared to promise significant power savings.
The first was spectrum sensing, in which wireless devices would scan the airwaves to detect unused frequencies that they could use to increase their data rates. The second was the transmission of data from wireless sensors - such as electrocardiogram (EKG) leads - to wired base stations.
.webp)
