Signal processing : a mathematical approach /
Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate- or graduate-level understanding of mathematics-including...
| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Boca Raton :
CRC Press, Taylor & Francis Group,
[2015]
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| Edition: | 2nd ed |
| Series: | Monographs and research notes in mathematics
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| Subjects: |
Table of Contents:
- 1. Introduction
- 2. Fourier series and Fourier transforms
- 3. Remote sensing
- 4. Finite-parameter models
- 5. Transmission and remote sensing
- 6. The Fourier transform and convolution filtering
- 7. Infinite sequences and discrete filters
- 8. Convolution and the vector DFT
- 9. Plane-wave propagation
- 10. The phase problem
- 11. Transmission tomography
- 12. Random sequences
- 13. Nonlinear methods
- 14. Discrete entropy maximization
- 15. Analysis and synthesis
- 16. Wavelets
- 17. The BLUE and the Kalman filter
- 18. Signal detection and estimation
- 19. Inner products
- 20. Wiener filtering
- 21. Matrix theory
- 22. Compressed sensing
- 23. Probability
- 24. Using the wave equation
- 25. Reconstruction in Hilbert space
- 26. Some theory of Fourier analysis
- 27. Reverberation and echo cancellation
- Front Cover; Signal Processing: A Mathematical Approach, Second Edition; MONOGRAPHS AND RESEARCH NOTES IN MATHEMATICS; Dedication; Contents; Preface; Chapter 1 Introduction; Chapter 2 Fourier Series and Fourier Transforms; Chapter 3 Remote Sensing; Chapter 4 Finite-Parameter Models; Chapter 5 Transmission and Remote Sensing; Chapter 6 The Fourier Transform and Convolution Filtering; Chapter 7 Infinite Sequences and Discrete Filters; Chapter 8 Convolution and the Vector DFT; Chapter 9 Plane-Wave Propagation; Chapter 10 The Phase Problem; Chapter 11 Transmission Tomography
- Chapter 12 Random SequencesChapter 13 Nonlinear Methods; Chapter 14 Discrete Entropy Maximization; Chapter 15 Analysis and Synthesis; Chapter 16 Wavelets; Chapter 17 The BLUE and the Kalman Filter; Chapter 18 Signal Detection and Estimation; Chapter 19 Inner Products; Chapter 20 Wiener Filtering; Chapter 21 Matrix Theory; Chapter 22 Compressed Sensing; Chapter 23 Probability; Chapter 24 Using the Wave Equation; Chapter 25 Reconstruction in Hilbert Space; Chapter 26 Some Theory of Fourier Analysis; Chapter 27 Reverberation and Echo Cancellation; Bibliography; Back Cover