## The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and FinanceThe Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance provides an overview of the theory and practical applications of wavelet transform methods. The author uses several hundred illustrations, some in color, to convey mathematical concepts and the results of applications. The first chapter presents a brief overview of the wavelet transform, including a short history. The remainder of the book is split into two parts: the first part discusses the mathematics of both discrete and continuous wavelet transforms while the second part deals with applications in a variety of subject areas, such as geophysics, medicine, fluid turbulence, engineering testing, speech and sound analysis, image analysis, and data compression. These application chapters make the reader aware of the similarities that exist in the use of wavelet transform analysis across disciplines. A comprehensive list of more than 700 references provides a valuable resource for further study. The book is designed specifically for the applied reader in science, engineering, medicine, finance, or any other of the growing number of application areas. Newcomers to the subject will find an accessible and clear account of the theory of continuous and discrete wavelet transforms, providing a large number of examples of their use across a wide range of disciplines. Readers already acquainted with wavelets can use the book to broaden their perspective. |

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I picked up this book because I want to apply wavelets in my research and have found it to be an excellent introduction to wavelets for someone who comes in with general mathematical knowledge (calculus, etc.) but has never before been exposed to them. It provides just the right amount of mathematical rigor for someone who wants to understand what wavelets are, what they do, how to implement them in practice, while not bogging down the reader in a mire of math equations. The step by step approach and clear explanations make this book an enjoyable, easy read. Highly recommended for getting introduced to wavelets on a practical level.

### Contents

II | 1 |

III | 2 |

IV | 3 |

V | 6 |

VI | 9 |

VIII | 11 |

IX | 14 |

X | 21 |

LIII | 187 |

LIV | 189 |

LV | 195 |

LVII | 199 |

LVIII | 202 |

LIX | 208 |

LX | 211 |

LXI | 221 |

XI | 25 |

XII | 28 |

XIII | 33 |

XIV | 35 |

XV | 45 |

XVI | 51 |

XVII | 55 |

XVIII | 56 |

XIX | 63 |

XX | 65 |

XXI | 67 |

XXII | 69 |

XXIII | 72 |

XXIV | 73 |

XXV | 75 |

XXVI | 77 |

XXVII | 81 |

XXVIII | 83 |

XXIX | 85 |

XXX | 87 |

XXXI | 91 |

XXXII | 96 |

XXXIII | 104 |

XXXIV | 112 |

XXXV | 115 |

XXXVI | 117 |

XXXVII | 119 |

XXXVIII | 121 |

XXXIX | 133 |

XL | 141 |

XLI | 144 |

XLII | 145 |

XLIV | 152 |

XLV | 153 |

XLVI | 159 |

XLVII | 160 |

XLVIII | 171 |

XLIX | 174 |

L | 178 |

LII | 186 |

LXII | 224 |

LXIII | 225 |

LXIV | 228 |

LXV | 229 |

LXVI | 230 |

LXVII | 231 |

LXVIII | 236 |

LXIX | 240 |

LXX | 242 |

LXXI | 253 |

LXXII | 256 |

LXXIII | 259 |

LXXIV | 262 |

LXXV | 263 |

LXXVI | 264 |

LXXVII | 267 |

LXXVIII | 268 |

LXXIX | 271 |

LXXX | 274 |

LXXXI | 277 |

LXXXII | 279 |

LXXXV | 280 |

LXXXVII | 281 |

LXXXVIII | 282 |

XCI | 286 |

XCII | 296 |

XCIII | 298 |

XCIV | 302 |

XCV | 303 |

XCVI | 309 |

XCVII | 311 |

XCVIII | 313 |

C | 314 |

CI | 315 |

CII | 316 |

CIII | 317 |

318 | |

319 | |

321 | |

### Other editions - View all

The Illustrated Wavelet Transform Handbook: Introductory Theory and ... Paul S. Addison Limited preview - 2017 |

The Illustrated Wavelet Transform Handbook: Introductory Theory and ... Paul S. Addison Limited preview - 2017 |

The Illustrated Wavelet Transform Handbook: Introductory Theory and ... Paul S. Addison No preview available - 2016 |

### Common terms and phrases

Addison algorithm amplitude application of wavelet approximation coefficients array chapter components computed contains continuous wavelet transform corresponding D4 wavelet data sets Daubechies wavelets denoising detail coefficients detection dilation discrete signal discrete wavelet transform dyadic grid ECG signal employed energy spectrum Engineering entropy equation example flow Fourier transform fractal Gaussian Haar wavelet hard thresholding hence high frequency Hurst exponent IEEE images iteration kind permission matching pursuit maxima lines Mexican hat wavelet modulus maxima Morlet wavelet Morlet wavelet transform multifractal multiresolution multiresolution analysis neural network noise original signal parameters phase power spectrum Reproduced with kind scale index scaling coefficients scaling function scalogram schematic shown in figure shows sinusoid spectra STFT surface technique time-frequency plane transform vector turbulent two-dimensional values velocity signal vertical wave waveform wavelet analysis wavelet coefficients wavelet decomposition wavelet function wavelet packet wavelet packet decomposition wavelet power wavelet scale wavelet-based method zero

### Popular passages

Page 339 - Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise,

Page 346 - Classification of underwater mammals using feature extraction based on time-frequency analysis and BCM theory,

Page 339 - Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current" , The International Journal of Advanced Manufacturing Technology, 15, pp.

Page 347 - IEEE Trans, on Geoscience and Remote Sensing, vol. 34, May 1996. 16. JA Rodenas and R. Garello, "Wavelet analysis in SAR ocean image profiles for internal wave detection and wavelength estimation", IEEE Trans, on Geoscience and Remote Sensing, vol.

Page 344 - Wavelet Transform Analysis of Transient Wave Propagation in a Dispersive Medium,

Page 332 - Time-Frequency Analysis Using the Matching Pursuit Algorithm Applied to Seizures Originating from the Mesial Temporal Lobe," Electroencephalography and Clinical Neurophysiology, vol.

Page 343 - K. Mori, N. Kasashima, T. Yoshioka, and Y. Ueno, "Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals,