Spectral Analysis for Physical Applications
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This book is an up-to-date introduction to univariate spectral analysis at the graduate level, which reflects a new scientific awareness of spectral complexity, as well as the widespread use of spectral analysis on digital computers with considerable computational power. The text provides theoretical and computational guidance on the available techniques, emphasizing those that work in practice. Spectral analysis finds extensive application in the analysis of data arising in many of the physical sciences, ranging from electrical engineering and physics to geophysics and oceanography. A valuabl...
This book is an up-to-date introduction to univariate spectral analysis at the graduate level, which reflects a new scientific awareness of spectral complexity, as well as the widespread use of spectral analysis on digital computers with considerable computational power. The text provides theoretical and computational guidance on the available techniques, emphasizing those that work in practice. Spectral analysis finds extensive application in the analysis of data arising in many of the physical sciences, ranging from electrical engineering and physics to geophysics and oceanography. A valuable feature of the text is that many examples are given showing the application of spectral analysis to real data sets. Special emphasis is placed on the multitaper technique, because of its practical success in handling spectra with intricate structure, and its power to handle data with or without spectral lines. The text contains a large number of exercises, together with an extensive bibliography.
Table of contents:
Glossary of symbols; 1. Introduction to spectral analysis; 2. Stationary stochastic processes; 3. Deterministic spectral analysis; 4. Foundations for stochastic spectral theory; 5. Linear time-invariant filters; 6. Non-parametric spectral estimation; 7. Multiple taper spectral estimation; 8. Calculation of discrete prolate spheroidal sequences; 9. Parametric spectral estimation; 10. Harmonic analysis; References; Appendix: data and code via e-mail; Index.
An up-to-date introduction to univariate spectral analysis at the graduate level.
An up-to-date introduction to univariate spectral analysis at the graduate level.
Table of contents:
Glossary of symbols; 1. Introduction to spectral analysis; 2. Stationary stochastic processes; 3. Deterministic spectral analysis; 4. Foundations for stochastic spectral theory; 5. Linear time-invariant filters; 6. Non-parametric spectral estimation; 7. Multiple taper spectral estimation; 8. Calculation of discrete prolate spheroidal sequences; 9. Parametric spectral estimation; 10. Harmonic analysis; References; Appendix: data and code via e-mail; Index.
An up-to-date introduction to univariate spectral analysis at the graduate level.
An up-to-date introduction to univariate spectral analysis at the graduate level.
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