Robin Crockett is Reader in Data Analysis in the Faculty of Arts, Science and Technology at the University of Northampton. He is a member of the Institute of Mathematics and its Applications (IMA) and the Institute of Physics (IOP) and holds Chartered Scientist Status. He specialises in investigating periodic, recurrent and anomalous features in data, and has led a highly successful short course on Fourier analysis at the European Geosciences Union General Assembly for many years.
Inhaltsangabe
Preface Acknowledgments 1. What is Fourier analysis 2. Covariance-based approaches 3. Fourier series 4. Fourier transforms 5. Using the FFT to identify periodic features in time-series 6. constraints on the FFT 7. Stationarity and spectrograms 8. Noise in time-series 9. Periodograms and significance Appendix A. DFT matrices and symmetries Appendix B. Simple spectrogram code Further reading and online resources References Index.
Preface Acknowledgments 1. What is Fourier analysis 2. Covariance-based approaches 3. Fourier series 4. Fourier transforms 5. Using the FFT to identify periodic features in time-series 6. constraints on the FFT 7. Stationarity and spectrograms 8. Noise in time-series 9. Periodograms and significance Appendix A. DFT matrices and symmetries Appendix B. Simple spectrogram code Further reading and online resources References Index.
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