Modern Signal Processing
Modern Signal Processing
Daniel N. Rockmore, Dartmouth College, New Hampshire
Dennis M. Healy, Jr, University of Maryland, College Park
Mathematical Sciences Research Institute
Dennis M. Healy, Jr, University of Maryland, College Park
Mathematical Sciences Research Institute
November 2010
Paperback
9780521158213
$51.99
USDPaperback
USD
Hardback
The mathematical basis of signal processing and its many areas of application is the subject of this book. Based on a series of graduate-level lectures held at the Mathematical Sciences Research Institute, the volume emphasizes current challenges, new techniques adapted to new technologies, and certain recent advances in algorithms and theory.
- Fascinating techniques in signal processing
- Aspects of group theory in signal processing
- Broad range of applications, from military through medical
Product details
November 2010Paperback
9780521158213
354 pages
234 × 156 × 19 mm
0.5kg
Available
Table of Contents
- 1. Introduction D. Rockmore and D. Healy
- 2. Hyperbolic geometry, Nehari's theorem, electric circuits, and analog signal processing J. Allen and D. Healy
- 3. Engineering applications of the motion-group Fourier transform G. Chirikjian and Y. Wang
- 4. Fast x-ray and beamlet transforms for three-dimensional data D. Donoho and O. Levi
- 5. Fourier analysis and phylogenetic trees S. Evans
- 6. Diffuse tomography as a source of challenging nonlinear inverse problems for a general class of networks A. Grunbaum
- 7. An invitation to matrix-valued spherical functions A. Grunbaum, I. Pacharoni and J. Tirao
- 8. Image registration for MRI P. Kostelec and S. Periaswamy
- 9. The mathematics of JPEG 2000 Jin Li
- 10. Integrated sensing and processing for statistical pattern recognition C. Priebe, D. Marchette and D. Healy
- 11. Sampling of functions and sections for compact groups D. Maslen
- 12. The Cooley-Tukey FFT and group theory D. Maslen and D. Rockmore
- 13. Mathematical challenges for optical communications U. Osterberg
- 14. The generalized spike process, sparsity and statistical independence N. Saito.
