the wavelet tutorial: part2 dr. charturong tantibundhit
TRANSCRIPT
![Page 1: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/1.jpg)
The Wavelet Tutorial: Part2
Dr. Charturong Tantibundhit
![Page 2: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/2.jpg)
Multiresolution Analysis (MRA)
• MRA, as implied by its name, analyzes the signal at different frequencies with different resolutions. Every spectral component is not resolved equally as was the case in the STFT
• MRA is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies
• This approach makes sense especially when the signal at hand has high frequency components for short durations and low frequency components for long durations
![Page 3: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/3.jpg)
![Page 4: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/4.jpg)
Continuous Wavelet Transform
• Developed as an alternative approach to the short time Fourier transform to overcome the resolution problem
• Two main differences between the STFT and the CWT: – The Fourier transforms of the windowed signals are
not taken, and therefore single peak will be seen corresponding to a sinusoid, i.e., negative frequencies are not computed.
– The width of the window is changed as the transform is computed for every single spectral component, which is probably the most significant characteristic of the wavelet transform
![Page 5: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/5.jpg)
• tau and s , the translation and scale parameters, respectively.
• psi(t) is the transforming function, and it is called the mother wavelet
![Page 6: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/6.jpg)
Scale
• The parameter scale in the wavelet analysis is similar to the scale used in maps. As in the case of maps, high scales correspond to a non-detailed global view (of the signal), and low scales correspond to a detailed view.
• In terms of frequency, low frequencies (high scales) correspond to a global information of a signal (that usually spans the entire signal), whereas high frequencies (low scales) correspond to a detailed information of a hidden pattern in the signal (that usually lasts a relatively short time).
![Page 7: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/7.jpg)
![Page 8: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/8.jpg)
Computation of CWT
![Page 9: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/9.jpg)
![Page 10: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/10.jpg)
![Page 11: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/11.jpg)
• Let x(t) is the signal to be analyzed. The mother wavelet is chosen to serve as a prototype for all windows in the process. All the windows that are used are the dilated (or compressed) and shifted versions of the mother wavelet
• Once the mother wavelet is chosen the computation starts with s=1 and the continuous wavelet transform is computed for all values of s, smaller and larger than “1”
![Page 12: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/12.jpg)
![Page 13: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/13.jpg)
![Page 14: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/14.jpg)
![Page 15: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/15.jpg)
Example
• These signals are drawn from a database signals that includes event related potentials of normal people, and patients with Alzheimer's disease
A normal person
![Page 16: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/16.jpg)
![Page 17: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/17.jpg)
![Page 18: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/18.jpg)
An event related potential of a patient diagnosed with Alzheimer's disease
![Page 19: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/19.jpg)
![Page 20: The Wavelet Tutorial: Part2 Dr. Charturong Tantibundhit](https://reader036.vdocument.in/reader036/viewer/2022082418/5697c01f1a28abf838cd18ec/html5/thumbnails/20.jpg)