Examples of using Wavelets in English and their translations into Bulgarian
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See a list of some Continuous wavelets.
Meyer wavelets can be defined by scaling functions.
See a list of some Continuous wavelets.
Wavelets are defined by the wavelet function ψ(t) i.e.
Ask me about computational simulation,parallel computing, wavelets.
Her deep andbeautiful analysis of wavelets and their applications.
Meyer wavelets can be defined by scaling functions Wavelet functionEdit.
While at Rutgers,she published Ten lectures on wavelets in 1992.
Daubechies and Symlet wavelets can be defined by the scaling filter.
A wavelet transform is the representation of a function by wavelets.
The use of wavelets as an analytical tool is like Fourier analysis- simple and yet very powerful.
In this video of Ingrid Daubechies' lecture on wavelets, the reverse is true.
In fact, wavelets are an extension of Fourier analysis to the case of localization in both frequency and space.
Pass through the slots behaves as a source of secondary wavelets according to Huygen's principle.
In mathematics, a continuous wavelet transform(CWT)is used to divide a continuous-time function into wavelets.
The numerical implementation of the micro-local point of view is by wavelets and similar approaches, which are very powerful numerically.
The main difference is that wavelets are well localized in both time and frequency domain whereas the Fourier transform is only localized in frequency domain.
It was a breakthrough by Daubechies in 1987,when she constructed compactly supported continuous wavelets, which led to many important applications.
In January 1992 Daubechies gave a lecture Wavelets making waves in mathematics and engineering to a joint AMS-MAA meeting in Baltimore, Maryland.
Wavelets are defined by the wavelet function ψ(t)(i.e. the mother wavelet) and scaling function φ(t)(also called father wavelet) in the time domain.
Every point on the wavefront acts as the source of secondary wavelets that spread out in the light cone with the same speed as the wave.
The concept of wavelets has its origins in many fields, and part of the accomplishment of Daubechies is finding those places where the concept arose and showing how all the approaches relate to one another.
The principal difference between the Fourier Transform and the wavelet is that the wavelets are localized both in time and frequency whereas the standard Fourier transform is localized only in frequency.
For fundamental discoveries on wavelets and wavelet expansions and for her role in making wavelets methods a practical basic tool of applied mathematics.
Rather than using Fourier transform methods to analyse signals he had the intuitive idea of using wavelets and later, in collaboration with Alex Grossmann, he put his intuition on a firm mathematical basis by introducing the continuous wavelet transform.
For analysis with orthogonal wavelets the high pass filter is calculated as the quadrature mirror filter of the low pass, and reconstruction filters are the time reverse of the decomposition filters.
The main difference in general is that wavelets are localized in both time and frequency whereas the standard Fourier transform is only localized in frequency.
There are a number of ways of defining a wavelet(or a wavelet family).
Definition of a wavelet==There are a number of ways of defining a wavelet(or a wavelet family).
His innovative work on wavelet theory has led to the development of image processing and filtering methods used in technologies ranging from medical imaging to wireless communication.