SIPI Seminar

"Multiscale Reconstruction of Spatio-Temporally Distributed Phenomena"Rebecca WillettRice University, Electrical & Computer Engineering DepartmentAbstract:
Many critical scientific and engineering applications rely upon the accurate reconstruction of spatio-temporally distributed phenomena from measured data. A number of information processing challenges arise routinely in these problems: indirect sensing modalities, decentralized sensing and processing resources, distorted and noisy data, and complicated signal behavior. Sensing is often indirect in nature, such as tomographic projections in medical imaging, resulting in complicated inverse reconstruction problems. The sensing can also be decentralized, as in wireless sensor networks, leading to complex trade-offs between communications, sensing and processing. Furthermore, in any practical system, the measurements are noisy due to errors in sensing and/or quantization effects. In addition to the complex issues associated with sensing, the behavior of the information-bearing signals of interest may be very rich and complex, and consequently difficult to model a priori. All of these issues combine to make accurate reconstruction a complicated task, involving a myriad of system-level and algorithm trade-offs.In this talk, I will demonstrate that nonparametric multiscale reconstruction methods can overcome all the challenges above and provide a theoretical framework for assessing trade-offs between reconstruction accuracy and system resources. First, the theory supporting these methods facilitates characterization of fundamental performance limits. Examples include lower bounds on the best achievable error performance in medical image reconstruction and upper bounds on the total amount of power that must be consumed to perform a sensor network task. Second, the methods themselves are practical and resource-efficient in a broad range of contexts, including a diverse variety of sensing modalities, noise models, data dimensionalities, and error metrics. Third, existing reconstruction methods can often be enhanced with multiscale techniques, resulting in significant improvements in a number of application domains. Underlying these methods are ideas drawn from the theory of multiscale analysis, statistical learning, nonlinear approximation theory, and iterative reconstruction algorithms. I will demonstrate the effectiveness of the theory and methods in several important applications, including medical image reconstruction and environmental monitoring with wireless sensor networks.Biography:
Rebecca Willett is a graduate student in the Electrical and Computer Engineering Department at Rice University. In addition to studying at Rice, Ms. Willett has worked as a Fellow of the Institute for Pure and Applied Mathematics at UCLA, as a visiting researcher at the University of Wisconsin-Madison and the French National Institute for Research in Computer Science and Control (INRIA), and as a member of the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare). She received the National Science Foundation Graduate Research Fellowship, the Rice University Presidential Scholarship, and the Society of Women Engineers Caterpillar Scholarship. Her research interests include signal processing and communications with applications in medical imaging, astrophysics, and wireless sensor networks. Additional information, including publications and software, are available online at http://www.ece.rice.edu/~willett/.Host: Richard Leahy

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Alma Hernandez