Richard Baraniuk (Department of Electrical and Computer Engineering, Rice University)

Sensors, signal processing hardware, and algorithms are under increasing pressure to accommodate ever larger and higher-dimensional data sets; ever faster capture, sampling, and processing rates; ever lower power consumption; communication over ever more difficult channels; and radically new sensing modalities. Fortunately, over the past few decades, there has been an enormous increase in computational power and data storage capacity, which provide a new angle to tackle these challenges. In this talk, I will overview some of the recent progress and open problems in "Compressive Sensing", an emerging field based on the revelation that a small collection of nonadaptive (even random) linear projections of a compressible signal or image contain enough information for signal reconstruction and processing. The implications of CS are promising for many applications and enable the design of new kinds of analog-to-digital converters, imaging systems and cameras, and distributed source coding algorithms for sensor networks.