— The new theory of compressive sensing enables direct analog-to-information conversion of compressible signals at subNyquist acquisition rates. We develop new theory, algorithms...
Jason N. Laska, Sami Kirolos, Marco F. Duarte, Tam...
Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged a...
The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse s...
Laurent Jacques, Jason N. Laska, Petros Boufounos,...
In recent work, we studied the problem of causally reconstructing time sequences of spatially sparse signals, with unknown and slow time-varying sparsity patterns, from a limited ...
—Reliable wireless communications often requires accurate knowledge of the underlying multipath channel. This typically involves probing of the channel with a known training wave...
Waheed Uz Zaman Bajwa, Jarvis Haupt, Gil M. Raz, R...