The main objective of this course is to conduct a study on wavelet theory from both theoretical and application perspective (such as signal compression, denoising, communication systems, and recognition) in a coherence manner. The course first builds up background on LTI operators and Fourier analysis (including Shannon’s sampling theory) and its short comings. Windowed Fourier Transform (WFT) ...

In this paper, we present an automated image registration algorithm, which combines the multi-scale wavelet transform (WT) with Scale Invariant Feature Transform (SIFT). The control points are selected using three levels WT. First, the image is decomposed into multi-scale levels using WT, then low frequency (approximation) images are input to SIFT algorithm for automatically obtaining control p...

In mixtures of speech signals the energy content of the components of the mixture is important and determine the structure of the mixture. Energy contents of signals are better shown when time-frequency or time-scale planes are used. In this paper we present a comparison of wavelet transform (WT) and short time Fourier Transform (STFT) in spectral analysis of speech signals. We will show in wav...

-Protective systems require a faithful reproduction of primary current on the Current Transformer (CT) secondary side. Saturation is a common problem in a steel core CT. Saturation problem causes dreadful effects in the protection systems. This paper presents a simple solution towards detection and compensation of current transformer saturation problems. This paper describes a method in which C...

Low power implementable devices like the pacemaker need good sensing circuits to correctly analyze the cardiac signal and take appropriate actions. Existing methods are reaching their limits on sensing abilities. An approximation of the wavelet transform (WT), that can be implemented in an analog dynamic translinear circuit, can be used to further the advances of cardiac signal sensing and lead...

As to the hybrid noise characteristic, a mixed de-noising method is hereby proposed in combination with the median filter and the wavelet transform. Firstly, conduct the median filter to the pending image; then apply an improved threshold function to the wavelet transform (WT) to eliminate noise; finally, adopt Signal Noise Ratio (SNR) to evaluate the image de-noising effect. The experiment res...

Introduction Wavelets and Wavelet transforms have been one of the most important developments in image analysis over the last decade especially for applications such as data com pression segmentation and vision The wavelet transform WT has been described in a num ber of di erent ways by various authors It can be described as a modi cation of the short time Fourier transform the decomposition of...

A new type of analyzing wavelets which optimally match seismic wavelets (SW wavelets) is presented. It is proved that these wavelets are admissible. The advantages of the SW wavelets are shown for the seismic data processing and attributes analysis. By taking one of the SW wavelets as analyzing wavelet, a wavelet transform method (WT method) of calculating the instantaneous spectrum bandwidth (...

Based on pixel level multi resolution analysis and current 3 multiresolution algorithms for Wavelet Transform (WT), Contour-let Transform (CT) and Non-Subsampled Contour-let Transform (NSCT), we present a new hybrid multi resolution algorithm. We use different types of images and extensive simulations combined with subjective and objective evaluation criteria to compare and analyze the pros and...

The main objective of super-resolution (SR) imaging is to reconstruct a high-resolution (HR) image of a scene from one or more low-resolution images of the scene. In resolution enhancement of images, the main loss is on the high frequency components (edges) of the image. This is due to the smoothing caused by interpolation. Hence in order to enhance the quality of the super resolved image, pres...