SIGNAL DENOISING USING WAVELETS
Swathi Nibhanupudi · OhioLink ETD Center (Ohio Library and Information Network) · 2003
In any type of signal processing, it has been demonstrated that it is important to remove noise from the signal before recognizing or classifying the patterns.Otherwise, the whole process may give wrong results.In this work the choice of denoising mechanisms for various types of input data and Gaussian noise is explored, to increase the signal strength.In this thesis, denoising the input signals using a wavelet transform is discussed.It is shown that the performance of a signal classifier improves when these denoising techniques are introduced before actually applying the classifier.For our experiments, the classifier applied is a hybrid intelligent system that employs three important techniques of artificial intelligence, namely genetic algorithms, neural networks and fuzzy logic.Along with explaining the denoising algorithm clearly, this work shows the importance of selection of a suitable wavelet for the given input data and thus shows that the efficiency of a signal denoiser depends on three factors: the thresholding techniques, the kind of wavelet used in denoising, and the synchronization between the wavelet selected and the input data.This statement is justified with results from experiments on ECG data which employ different kinds of wavelets such as Haar, Daubechies, Symlet and Coiflet.The improvements in denoising after using vector quantization of wavelet coefficients before thresholding are also discussed.