## Wavelets and Signal Processing: An Application-Based IntroductionThe wavelets transform is a mathematical technique in the field of image compression and digital signal analysis. The author aims at providing the reader with a working understanding of wavelets. In numerous examples, he discusses the potentials and limits of the tool in industrial applications. The book is completed by the author`s own Matlab codes. It is very well suited for electrical engineering students and engineers in industry. |

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### Contents

Contents | 1 |

Continuous Analysis | 13 |

The Discrete Wavelet Transform | 43 |

More Applications | 95 |

Appendix | 125 |

147 | |

### Other editions - View all

Wavelets and Signal Processing: An Application-Based Introduction Hans-Georg Stark Limited preview - 2005 |

Wavelets and Signal Processing: An Application-Based Introduction Hans-Georg Stark No preview available - 2009 |

Wavelets and Signal Processing: An Application-Based Introduction Hans-Georg Stark No preview available - 2010 |

### Common terms and phrases

analogously analysis function applying approximation signal arithmetic coding average codeword length basis sequences basis system circular frequency columns compression rate computed continuous-time function continuous-time signal f(t correspondence pair data compression decomposition denoised denote described detail signals diagram discrete Fourier Transform discrete signal discrete wavelet transform distortion dual filters entropy example fdec filter coefficients follows formula Fourier basis Fourier transform fpwavelet given Haar-wavelet histogram Huffman code implemented interval iterations J-step-DWT MATLAB Wavelet Toolbox matrix Moreover nonzero Note obtain original signal parameters phase plane phase response pixel plotted procedure PSNR reader reads reconstruction respectively sampled signal sampling distance scale factors scaling function sect sequence elements sequence f shows signal analysis signal changes signal compression signal f step STFT stored transform values transformed signal vanishing moments visualized wavelet basis wavelet transform window function