Seminar on Wavelet Transforms

Practically all signals are non-stationary and are encountered with the problem of providing better time and frequency resolutions due to Heisenberg’s uncertainty principle. This paper focuses on Wavelet Transform techniques, which are only a decade old and are providing better resolutions and time-frequency representation of the signals which the old Fourier Transform and revised Short Term Fourier Transform failed. This paper discusses the drawbacks of FT and STFT and how the Wavelet Transforms has overcome them, along with the vast fields of it’s applications. You can also Subscribe to FINAL YEAR PROJECT'S by Email for more such projects and seminar.


Signals form a crucial part of today’s would as every step that being taken completely depends on producing, receiving, analyzing & retrieving information from them. Practically all this signal that are produced and received have various frequency components in them which calls for complex analysis methods to squeeze out the information contained. For times bygone, there are several methods being used to study the signals. Transforming them from one domain to other has succeeded in providing a lot more information.

Recommended Project: MATLAB project on Speech Recognition Using Wavelet Transform

The research then focused on to the methods of transforming the signals to get the best way of information access. The transformation methods can be listed out as fourier transform, laplace transforms, Wigner distribution, Randon process etc. Among these the fourier transform has been more popular.


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