Image Fusion using Mallat Algorithm of Wavelet Transform

In image processing, image fusion is separating the data from multi-source pictures and has a major scope in processing an image. A completed fused image is acquired by averaging the two images. Wavelet transform based algorithm is proposed to enhance the image, wherein two images to be prepared are right off the bat deteriorated into sub-images with various recurrence, and after that the information is fused using these images specific paradigm, lastly these sub-images are reconstructed into the outcome image with copious information. Correlation and SNR parameters are utilized to analyze the fused images.

A Phase Difference Measurement Method Based on the Extended Kalman Filter for Coriolis Mass Flowmeters

According to the characteristics of stable single-phase flow, a phase difference measurement method based on the extended Kalman filter is proposed in this paper for use with Coriolis mass flowmeters. Firstly, the Mallat algorithm is applied to filter out interference signals. Then, the frequency and phase difference of the two reconstructed signals are detected through the extended Kalman filter. Compared with the sliding Goertzel algorithm or discrete time Fourier transform, the proposed method does not need to predict the signal frequency and avoids quadratic error. Simulations and experiments show that the proposed method has stronger anti-interference, higher measurement accuracy and lower relative error than the existing method based on the Hilbert transformation.

Rail Damage Detection Based on AE Technology and Wavelet Data Processing

In order to improve the accuracy and reliability of the defect recognition in rails, the new principle and methods of NDT should be explored, and the safety of railway operation and guiding the repair can be guaranteed. The study is aiming at comparing and analyzing the characteristics of destructive data signals with non-destructive ones. The damage and defect can be judged by the differences of the processed data in the frequency domain and energy spectrum. The location of the defect and damage can be obtained by the singularities of destructive signals using wavelet data processing method: continuous wavelet transform, Mallat algorithm and à Trous algorithm. Based on the above consequence, à Trous algorithm is found that its result is more similar to the real damage location, which proves that the method can be used in the real damage detection and provide us more precise defect location information for early warning.

Image Compression Technology Reseach Based on Lifting Wavelet Transform

Because the operational speed of the traditional image compression based on Mallat algorithm is slow, and auxiliary Storage space is occupied, image compressed method based on lifting wavelet transform is proposed, in order to realize its versatility, flexibility and high efficient. Experiments show that the lifting wavelet transform, is easy to realize integer wavelet transform can be used for lossless compression, and has higher compression ratio.

Application of Wavelet Algorithm in Spectral Analysis

The purpose of this paper is combined with the advantages of wavelet transform and FFT, using wavelet transform to change the signal decomposition, and the use of FFT and filter for the reconstructed signal to decompose the signal and extract the different frequency components. In processing, we find that there is frequency alias in sub-band reconstruction which is caused by Mallat algorithm. In this article, we offer a solution of this problem.

A Mallat Based Wavelet ECG De-Noising Algorithm

A Mallat based wavelet de-noising algorithm in ECG analysis is studied. We use bior3.7 wavelet based on Mallat algorithm for ECG decomposition. Then we choose composite threshold and wavelet reconfiguration algorithm for signal de-noising to achieve an effective result. Data get from MIT/BIH is examined using the method. The result shows that it can not only remove the power frequency disturbance, EMG interference and baseline drift emerging in ECG, but also preserve the ECG characteristics.

The Runoff Forecasting Model Based on Wavelet Adaptive Neural Fuzzy Inference System

Considering various the wavelet decomposition reconstruction technology and training cycle of adaptive neural fuzzy inference system, this article propose four runoff forecast model of wavelet analysis and adaptive neural fuzzy inference system integration, such as the long cycle based on Mallat algorithm in runoff prediction, the long cycle based on wavelet packet algorithm in runoff prediction, the short cycle based on Mallat algorithm in runoff prediction, the short cycle based on wavelet packet algorithm in runoff prediction, and illuminate the model of the principles, structures and procedures. This model is used in Tangnaihe station monthly runoff forecast which lies in the Huanghe source area. Simulation results are evaluated by the cycle decomposition coefficients and Nash-Sutcliffe coefficient; it shows that the long cycle based on Mallat algorithm is best, the short cycle based on wavelet packet algorithm is worst. The author analyzes the reason and makes some proposal.