The increasing availability of computing power, thanks to the advances of Moore's law, has put significant pressure on sensing technology to follow suit. Although sensor hardware cannot always keep pace, recent theoretical developments such as Compressive Sensing and Computational Imaging have demonstrated how smart sensor design can exploit cheap computation to improve sensing and signal acquisition technology. The hallmarks of these theoretical developments are randomization, non-linear reconstruction, and emphasis on signal models.

In this talk we emphasize how a model of the acquisition system can be taken into account in the design of the reconstruction algorithms. Specifically, we examine how the randomization of the measurements interacts with measurement quantization in analog-to-digital conversion. We first consider finite-range quantizers and demonstrate that, counter to common intuition, we can often decrease the error due to quantization by increasing the saturation rate of the quantizer. Then we consider the extreme case of 1-bit quantization and we demonstrate that we can significantly improve performance by explicitly incorporating the appropriate quantization model in the reconstruction. Finally, we consider signals measured though non-linear distortions, and we demonstrate that we can still reconstruct the signal from the measurements, even if the distortion itself is not known.

Petros Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2000, the S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002, and the Sc.D. degree in EECS in 2006. Since January 2009 he has been with Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA.  

Between September 2006 and December 2008, Dr. Boufounos was with the Digital Signal Processing Group at Rice University conducting research in the area of Compressive Sensing. In addition to Compressive Sensing, his immediate research interests include signal processing, data representations, frame theory, and machine learning applied to signal processing. He is also interested in the interaction of compressed sensing with other fields that use sensing extensively, such as robotics and mechatronics. Dr. Boufounos has received the Ernst A. Guillemin Master Thesis Award  for his work on DNA sequencing and the Harold E. Hazen Award for  Teaching Excellence, both from the MIT EECS department. He has also  been an MIT Presidential Fellow. Dr. Boufounos is a member of the  IEEE, Sigma Xi, Eta Kappa Nu, and Phi Beta Kappa.