This volume consists of contributions spanning a wide spectrum of harmonic analysis and its applications written by speakers at the February Fourier Talks from 2002 2013. Containing cutting-edge results by an impressive array of mathematicians, engineers and scientists in academia, industry and government, it will be an excellent reference for graduate students, researchers and professionals in pure and applied mathematics, physics and engineering. Topics covered include:

Special Topics in Harmonic Analysis

Applications and Algorithms in the Physical Sciences

Gabor Theory

RADAR and Communications: Design, Theory, and Applications

The February Fourier Talks are held annually at the Norbert Wiener Center for Harmonic Analysis and Applications. Located at the University of Maryland, College Park, the Norbert Wiener Center provides a state-of- the-art research venue for the broad emerging area of mathematical engineering.

Presents state-of-the-art results in gabor theory, applications and algorithms in the physical sciences, signal processing and sampling, and theoretical harmonic analysis Written and reviewed by the leading experts in the field Selected from over ten years of annual talks at the Norbert Wiener Center

Inhalt

Wiener Randomization on Unbounded Domains and an Application to Almost Sure Well-Posedness of NLS.- Bridging Erasures and the Infrastructure of Frames.- Choosing Function Spaces in Harmonic Analysis.- Existence of Frames with Prescribed Norms and Frame Operator.- Phase Transitions in Phase Retrieval.- Sparsity-Assisted Signal Smoothing.- A Message-Passing Approach to Phase Retrieval of Sparse Signals.- Importance Sampling in Signal Processing Applications.- Finite Dimensional Dynamical Sampling: An Overview.- Signal Processing on Weighted Line Graphs.- Adaptive Signal Processing.- Spectral Correlation Hub Screening of Multivariate Time Series.- A Spectral Analysis Approach for Experimental Designs.- The Synchrosqueezing Transform for Instantaneous Spectral Analysis.- Supervised Non-Negative Matrix Factorization for Audio Source Separation.