A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be ...

the goal of this research is to predict total stock market index of tehran stock exchange, using the compound method of arima and neural network in order for the active participations of finance market as well as macro decision makers to be able to predict trend of the market. first, the series of price index was decomposed by wavelet transform, then the smooth's series  predicted by using...

Wavelet denoising techniques are used to remove additive Gaussian noise by thresholding the wavelet coe cients. Like other transform based lters, wavelet shrinkage methods provide blur or visual artifacts that are exhibited in the neighborhood of image edges. The motive for implementing the Hybrid Wavelet Transform Filter (HWTF) is to provide discriminate smoothing operator for noise removal. T...

iii ACKNOWLEDGEMENTS I would like to thank to my thesis supervisor Assoc. Prof. Levent M. Arslan for his guidance and support. It was a great pleasure to work with him during this thesis and in all the projects we have been involved in the last three years. I would like to thank Prof. reading my thesis and participating in my thesis committee. I would like to thank to my family and to Aylin for...

Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coefficients mold, the principles of wavelet transform in image denoising are analyzed, the disadvantage...

We have built several networks of inferior olive (IO) model neurons to study the emerging spatio-temporal patterns of neuronal activity. The degree and extent of the electrical coupling, and the presence of stimuli were the main factors considered in the IO networks. The network activity was analyzed using a discrete wavelet transform which provides a quantitative characterization of the spatio...

Fabric defect detection is one of the indispensible units in the manufacturing industry to maintain the quality of the end product. Wavelet transform is well suited for quality inspection application due to its multi-resolution representation and to extract fabric features. This paper presents the comparison of three wavelet based models. These models include Tree structured wavelet transform, ...

A few different approaches exist for computing undecimated wavelet transform. In this work we construct three undecimated schemes and evaluate their performance for image noise reduction. We use standard wavelet based de-noising techniques and compare the performance of our algorithms with the original undecimated wavelet transform, as well as with the decimated wavelet transform. The experimen...

Fourier transform is applied to detect the direction of stripe noise before de-noising, which is advantageous for selecting the corresponding detail coefficients for threshold quantization after stationary wavelet transform. Depending on the direction of stripe noise, the corresponding detail coefficients contain stripe noise need to be removed, while retaining the approximate coefficients and ...

Wavelet transforms have become one of the most important and powerful tool of signal representation. Nowadays, it has been used in image processing, data compression, and signal processing. This paper will introduce the basic concept for Wavelet Transforms, the fast algorithm of Wavelet Transform, and some applications of Wavelet Transform. The difference between conventional Fourier Transform ...