Ivan M. Selesnick (BS'90, MEE'91, PhD'96) received an NSF CAREER Award for "Systems for Linear and Nonlinear Signal Analysis - Design via Groebner Bases." He is currently an assistant professor at Polytechnic University of New York.

The theory of filtering and transforms is fundamental for the compression, transmission, and analysis of signals from biomedical, geological, or entertainment sources, in audio, image, or video format. Because many natural signals are modeled more accurately by nonlinear models than by linear models, it is especially important to pursue the design, implementation and evaluation of nonlinear transforms. This research advances the state-of-the-art multiresolution analysis tools by bringing powerful mathematical tools from algebraic geometry to bear upon the design of systems for linear and nonlinear signal analysis. The use of Groebner bases fundamentally improves one's ability to construct discrete-time systems, even when the corresponding design equations are nonlinear. This research will lead to a deeper understanding of how to successfully apply Groebner bases in practice, and what their limitations are.

The investigator will also organize an Internet website, `A Signal Processing Exercise and Lab Bank', devoted to signal processing education. The website will be an evolving collection of innovative exercises, projects and demos contributed by educators and researchers, that illustrate both basic theory and its application to real-world signals, and which can be updated to reflect the progress in signal processing techniques. It will facilitate the integration of signal processing research and education, and will likewise help in modernizing SP curriculums, by giving educators access to a collection of introductory exercises illustrating recent and ongoing research topics. By drawing together existing resources, and by providing a point for the dissemination of future innovative exercises and labs, the website will enhance the NSF's existing investment in signal processing education and research.

Narayan Mandayam (MS'91, PhD '94) received an NSF CAREER Award for "Radio Resource Management for Wireless Data Networks." He is currently an assistant professor at Rutgers University.

The CAREER program was established because the NSF recognized the critical role young faculty members play in integrating research and education. The program, according to the NSF, "emphasizes the importance the Foundation places on the early development of academic careers dedicated to stimulating the discovery process in which the excitement of research is enhanced by inspired teaching and enthusiastic learning."

The cellular telephone success story has prompted the wireless communications community to turn its attention to other information services, many of them in the category of "wireless data" communications. One lesson of cellular telephone network operation is that effective radio resource management (power control, channel assignment and handoffs) is essential to promote the quality and efficiency of a system. Radio resource management will be equally, if not more, critical in systems that include high-speed data signals. This research focuses on developing a new framework for radio resource management in wireless data networks. The new approach considered here relies on using microeconomic theories that take into account notions utility (level of satisfaction of a user) and pricing (cost charged to a user) in developing distributed radio resource management algorithms for wireless data services. This research will make fundamental contributions that impact knowledge and technology in the currently evolving third generation of wireless systems.

The cellular telephone success story has prompted the wireless communications community to turn its attention to other information services, many of them in the category of "wireless data" communications. One lesson of cellular telephone network operation is that effective radio resource management (power control, channel assignment and handoffs) is essential to promote the quality and efficiency of a system. Radio resource management will be equally, if not more, critical in systems that include high-speed data signals. This research focuses on developing a new framework for radio resource management in wireless data networks. The new approach considered here relies on using microeconomic theories that take into account notions utility (level of satisfaction of a user) and pricing (cost charged to a user) in developing distributed radio resource management algorithms for wireless data services. This research will make fundamental contributions that impact knowledge and technology in the currently evolving third generation of wireless systems.

Last modified: Thursday, March 11, 1999