Petros T. Boufounos, Rice University, USA, Justin K. Romberg, Georgia Institute of Technology, USA and Richard G. Baraniuk, Rice University, USA
Recent signal processing trends have moved the analog to digital conversion closer to the sensor in order to exploit the flexibility of digital systems. Meanwhile, sensor and analog to digital conversion technology have not kept up with the ever increasing demands from the data processing side of modern signal processing systems; the data acquisition side is rapidly becoming the bottleneck.
Compressive sensing is a new technology that promises to ameliorate or remove this. Compressive Sensing reduces the data acquisition burden on the sensor at the small expense of increased computation in the signal reconstruction process. Recent results demonstrate that it is possible to subsample a signal at a rate much lower than the Nyquist rate without any loss of information as long as the signal is sparse in some basis. The signal can then be recovered using a simple convex optimization algorithm. The theory also provides robustness guarantees if noise is present in the acquisition or if the signal deviates from the sparse model but can be well approximated by it. The theory has led to exciting data acquisition systems, such as the compressive sensing single pixel camera, the random demodulation sampler, and the compressive sensing RADAR. The field is rapidly growing; the Compressive Sensing repository at Rice University (www.dsp.ece.rice.edu/cs) recently exceeded 160 related papers.
The goal of this tutorial is to expose the compressive sensing theory to a wide audience of signal processing engineers in academia and industry. The tutorial will present the fundamentals of Compressive sensing in an approachable manner, aiming to entice engineers in industry and academia to exploit the new theory in their applications and their research. Although several theoretical results will be presented, the emphasis is on the intuition and the understanding of the theory. The target audience level is the same as the target audience of the IEEE Signal Processing Magazine. The remainder of this proposal provides a brief overview of the fundamental compressive sensing results and applications that we intend to present at the tutorial, describes our pedagogical approach and outlines the tutorial topics.
The tutorial will consist of three lectures of one hour each with interspersed question and answer periods. The presentation will be as active and participatory as possible to encourage better learning outcomes. The target audience level is the same as the target audience of the IEEE Signal Processing Magazine.
Although several theoretical results will be presented, the emphasis is on the intuition and the understanding of the fundamental concepts. Applications will also be presented to motivate the theory and demonstrate the key benefits and the limitations of compressive sensing.
Together with the lecture slides we intend to distribute the following overview of compressive sensing: Richard Baraniuk, "A Lecture on Compressive Sensing." IEEE Signal Processing Magazine, v. 24, iss. 4, pp. 118-121, July 2007.
All the lecture materials will be free and open both for the tutorial participants and for anyone worldwide after the conference. All materials will be housed in the Connexions system at cnx.rice.edu, or possibly at the new IEEE-Signal Processing Society Connexions project at ieeecnx.org, together with other material on Compressive Sensing already on Connexions.
Connexions, based at Rice University and directed by R. Baraniuk, provides an ideal platform to illustrate the many interconnections of compressive sensing, to disseminate the material free-of-charge to anyone in the world, and to foster the growth of vibrant communities around the subject. It has been recently embraced by the IEEE Signal Processing Society on a major initiative to develop signal processing educational material that "will be available for free access by anyone, anywhere, at any time." The open-access nature of Connexions will enable our materials to reach a large audience of a size not possible with a standard monograph or textbook. The Connexions server currently handles 600.000 unique visitors per moth from 200 countries. Tutorial participants will be invited to use, reuse, and customize our materials in their own courses for free.
Petros Boufounos completed his undergraduate and graduate studies in the Massachusetts Institute of Technology (MIT) and 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 September 2007, Dr. Boufounos has been with the Rice University Digital Signal Processing Group doing research in the area of compressive sensing. In addition to compressive sensing, his research interests include signal processing, data representations, frame theory, and machine learning applied to signal processing.
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.
Justin Romberg attended Rice University, where he received the BS (1997), MS (1999), and PhD (2003) degrees in electrical engineering. His graduate work centered on multiscale geometrical models for image processing.
In fall of 2003, he joined the Applied and Computational Mathematics Department at Caltech, where he worked on the theoretical foundations of compressive sampling. He spent the fall of 2004 as a Fellow at UCLA’s Institute for Pure and Applied Mathematics.
In the fall of 2006, he joined the faculty at Georgia Tech. Justin Romberg is an Assistant Professor in the School of Electrical and Computer Engineering at Georgia Tech.
Richard Baraniuk, Victor E. Cameron Professor in the DSP Group (dsp.rice.edu) of the Electrical and Computer Engineering Department at Rice University. His research interests lie in new theory and algorithms for DSP, including multiscale analysis and wavelets, digital imaging and image processing, inverse problems, and networking and communications. He was elected a Fellow of the IEEE in 2001 and has received national young investigator awards from the National Science Foundation and the Office of Naval Research, the Rosenbaum Fellowship from the Isaac Newton Institute of Cambridge University, and the ECE Young Alumni Achievement Award from the University of Illinois. He has received the George R. Brown Award for Superior Teaching at Rice twice and the C. Holmes MacDonald National Outstanding Teaching Award from Eta Kappa Nu. In 1999 he founded the Connexions Project (cnx.rice.edu), a rapidly growing collection of free, open-access educational materials and an open-source software toolkit to help authors publish and collaborate, instructors rapidly build and share custom courses, and students explore the links among concepts, courses, and disciplines.