Assistant Professor Marco Duarte of the Electrical and Computer Engineering Department was the co-winner of the Overview Paper Award, recently given by the Signal Processing Society of the Institute of Electrical and Electronics Engineers (IEEE). Award Recipients
The award was given to Duarte and co-author Yonina C. Eldar for "Structured Compressed Sensing: From Theory to Applications (link is external)," a research article published in IEEE Transactions on Signal Processing, Volume 59, No. 9, September 2011.
As the abstract of the article explained, compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles on CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas.
“This, in turn, necessitates a fresh look on many of the basics of CS,” the two authors said.
Researchers in the CS area have thus deduced that as the specific constraints of real-world applications are considered, the default random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals.
Duarte and Eldar’s article provides a summary of many important developments in this vein and has received more than 500 citations in the last five years, according to Google Scholar.
“In our overview,” wrote Duarte and Eldar, “the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.”
Duarte’s research deals with signal and image processing, compressive sensing, dimensionality reduction, machine learning, computational imaging, distributed sensing, and sensor networks. He earned his B.S. in Computer Engineering and M.S. in Electrical Engineering from the University of Wisconsin - Madison, and he received his Ph.D. in Electrical Engineering from Rice University.
Founded as IEEE’s first society in 1948, the Signal Processing Society is the world’s premier association for signal processing engineers and industry professionals. Engineers around the world look to the society for information on the latest developments in the signal processing field. Its deeply rooted history spans almost 70 years, featuring a membership base of more than 19,000 deeply interested and involved signal processing engineers, academics, industry professionals, and students who are all part of a dynamic global community – spanning 100 countries worldwide. (January 2016)