scholarly journals Signal Analysis On Soil Stress From Vibrating Compaction Based On Wavelet Transform

2014 ◽  
Vol 60 (2) ◽  
pp. 257-268
Author(s):  
Zhang Qing-Zhe ◽  
Yan Bing ◽  
Dai Jing-Liang ◽  
Yang Bao-Gui
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Frequency Component ◽  
Start Time ◽  
Transform Method ◽  
Singularity Detection ◽  
Study Results ◽  
Soil Stress ◽  
Stress Signal ◽  
Stress Signals

Abstract The paper presented the wavelet transform method for d e-noising and singularity detection to soil compressive stress signal. The study results show that the reconstruction signals by the wavelet de-noising keeps the low frequency component at [0, 31.25Hz] of the original signal and improves the high frequency property at other frequency bands. The impaction time from the start time to resonance time of the stress signals is varies with the depth of the soil. With the increase of times of compaction, the impaction time of the stress is decreasing in every layer. But the speed of reaching compacted status in each layer is different.

scholarly journals Medical image fusion based on quaternion wavelet transform

2020 ◽  
Vol 14 ◽  
pp. 174830262093129
Author(s):  
Zhang Zhancheng ◽  
Luo Xiaoqing ◽  
Xiong Mengyu ◽  
Wang Zhiwen ◽  
Li Kai
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Medical Image Fusion ◽  
Quaternion Wavelet Transform ◽  
Fused Image

Medical image fusion can combine multi-modal images into an integrated higher-quality image, which can provide more comprehensive and accurate pathological information than individual image does. Traditional transform domain-based image fusion methods usually ignore the dependencies between coefficients and may lead to the inaccurate representation of source image. To improve the quality of fused image, a medical image fusion method based on the dependencies of quaternion wavelet transform coefficients is proposed. First, the source images are decomposed into low-frequency component and high-frequency component by quaternion wavelet transform. Then, a clarity evaluation index based on quaternion wavelet transform amplitude and phase is constructed and a contextual activity measure is designed. These measures are utilized to fuse the high-frequency coefficients and the choose-max fusion rule is applied to the low-frequency components. Finally, the fused image can be obtained by inverse quaternion wavelet transform. The experimental results on some brain multi-modal medical images demonstrate that the proposed method has achieved advanced fusion result.

Audio Watermarking Based on Wavelet Transform

2012 ◽  
Vol 229-231 ◽  
pp. 2784-2788 ◽  
Author(s):  
Mahmoud A. Osman ◽  
Nasser H. Ali
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Audio Signal ◽  
Frequency Component ◽  
Binary Image ◽  
Audio Watermarking ◽  
High Frequency Component ◽  
Discrete Wavelet ◽  
Copyright Information

The process of hiding the information like text, binary image, audio etc. into another signal source like image, audio etc. is called watermarking. The approach involved in watermarking the binary image signal in the wavelet domain of the audio signal was implemented using MATLAB. In this paper, we propose a Discrete Wavelet Transform low frequency to high frequency. Besides, the high frequency spectrum is less sensitive to human ear. That is the reason why the high frequency component is usually discarded in the compression process. Therefore, information to be hidden can be embedded into the low frequency component to against the compression attack. The characteristic of this scheme is that the user can not only use the DAW to embed the text file in to the audio but also binary image. In this paper we embeds copyright information into audio files as a proof of their ownership, we propose an effective, robust, and an inaudible audio watermarking algorithm. The effectiveness of the algorithm has been brought by virtue of applying the discrete wavelets transform (DWT) . Experimental results will be presented in this paper to demonstrate the effectiveness of the proposed algorithm.

Calculation Error's Correction of Three-Component Geomagnetic Field's Marine Survey

2013 ◽  
Vol 756-759 ◽  
pp. 323-326
Author(s):  
Xing Le Zhu ◽  
Chang Han Xiao ◽  
Zhen Ning Yao
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Geomagnetic Field ◽  
Low Frequency ◽  
Frequency Component ◽  
Frequency Noise ◽  
Calculation Error ◽  
High Frequency Noise ◽  
Multi Scale ◽  
Simulation Results

In order to eliminate calculation error, wavelet transform is used to remove noise when navigational data is used to calculate truth-value of three-component geomagnetic field. By introducing Euler attitude rotation matrix, the computing value of geomagnetic vector is decomposed by multi-scale wavelet transform in each frequency. The high-frequency noise is removed and the accurate value of geomagnetic field can be got by rebuilding low-frequency component. Simulation results indicate that the calculated value is identical with setting value and has high precision, which means the method has great applied importance and instructional significance for practical measurement of marine three-component geomagnetic field.

Application of Wavelet Transform in the De-Noising of Angular Displacement Signals of Crank Rocker Mechanism

2014 ◽  
Vol 687-691 ◽  
pp. 1062-1065
Author(s):  
Xiao Cun Jiang ◽  
Xiao Liu ◽  
Kui Xia Han ◽  
Ji Fang Liu ◽  
Xiao Cui
Keyword(s):  
Wavelet Transform ◽  
Frequency Band ◽  
High Frequency ◽  
Angular Displacement ◽  
Low Frequency ◽  
Wavelet Domain ◽  
Test Results ◽  
High Frequency Band ◽  
Transform Method

In order to get rid of noise from the angular displacement of the crank rocker mechanism, the wavelet transform method is introduced. After the noise corresponds to the high frequency band of wavelet domain of the signal and the signal corresponds to the low frequency band of wavelet domain of the signal, the signal is decomposed into four layers, and the high frequency brand is set zero. The test results show that this method was most ideal for the de-noising effect on displacement signals, which is able to not only retain valid signals but to effectively remove the noise.

Steady-state detection method of burning flame temperature based on wavelet transform and least squares fitting

2018 ◽  
Vol 41 (5) ◽  
pp. 1458-1467 ◽  
Author(s):  
Shizeng Lu ◽  
Hongliang Yu ◽  
Xiaohong Wang
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Least Squares ◽  
High Frequency ◽  
Detection Method ◽  
Flame Temperature ◽  
Low Frequency ◽  
Frequency Component ◽  
Least Squares Fitting ◽  
State Detection

The steady-state detection of burning flame temperature plays an important role in the modelling, state identification and optimization control of the cement clinker burning process. In this paper, the steady-state detection method of burning flame temperature based on wavelet transform and least squares method is studied. First, the burning flame temperature data were detected accurately using a video detection device. Then, the temperature signal was decomposed into the high-frequency and low-frequency components based on the wavelet transform method, and the wavelet basis function and the decomposition layer were determined by least squares fitting error. Thus, the signal trend item can be obtained by removing the high-frequency component that represents the signal noise, and reconstructing the low-frequency component that reflects the basic trend of the signal. On this basis, the first derivative of the trend was further obtained, and the steady-state detection threshold was set to achieve steady state-detection of the burning flame temperature. The results showed that the method proposed in this paper can accurately extract the burning flame temperature trend and realize steady-state detection. This paper provides a feasible method for the steady-state detection of burning temperature.

Analysis and Simulation of Meteorological Wind Fields Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1228-1236 ◽  
Author(s):  
Fo Rong Jin ◽  
Wei Rong Wang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
Inertial Subrange ◽  
Target Velocity ◽  
Wind Fields ◽  
Transform Method ◽  
Time Frequency ◽  
Velocity Spectra ◽  
Energy Relations

In this work, we examined the non Gauss distribution characteristic and evolution law of the wavelet coefficient of a gust using wavelet transform; according to the time-frequency characteristic, the wavelet transform coefficients and the energy relations of the target velocity spectra are derived; the wavelet coefficient is generated using the cascade model reflecting the turbulent intermittent; the unsteady gust artificial generation method is established based on inverse wavelet transform; and the arbitrary unsteady fluctuation law can be generated by regulating the coefficient of low frequency. The results show that: the natural gust is in good agreement with Karman wind speed spectrum, meets the turbulence-5 / 3 law in the inertial subrange, and exhibits the nature of intermittence and local self-similarity; the artificial wind sequence based on the inverse wavelet transform method shows similar turbulence statistics with natural gust, with which, the effectiveness of the method is confirmed.

A NOVEL IMAGE FUSION APPROACH BASED ON WAVELET TRANSFORM AND FUZZY LOGIC

2006 ◽  
Vol 04 (04) ◽  
pp. 617-626 ◽  
Author(s):  
TAO LI ◽  
JIAN LIU ◽  
ZHICHENG WANG ◽  
YAN TIAN
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Spatial Information ◽  
Low Frequency ◽  
Average Energy ◽  
Frequency Component ◽  
Discrete Wavelet ◽  
Fusion Procedure ◽  
Fusion Approach

Better understanding of the real world can be obtained by fusion of images with complementary information. It was shown that an image fusion technique based on wavelet decomposition seems to be a better trade-off between spectral and spatial information in a single image. This paper presents a novel image fusion scheme that is based on wavelet transform and fuzzy logic. The two source images are first decomposed using the discrete wavelet transformation (DWT). Then different rules are used for different components in the fusion procedure. Local average energy is utilized as the fusion parameter for the low frequency component first; then membership degree of wavelet transform coefficient is used as the fusion parameter for the high frequency parts; finally, the fused image is obtained by taking inverse wavelet transform from the combined coefficients. The proposed fusion approach is shown to be effective using some remote sensing test images.

Dynamics of Cantilever Beams due to Time-Dependent Excitations and their Suppression

2012 ◽  
Vol 232 ◽  
pp. 465-468
Author(s):  
Adnan Rebei ◽  
Khalid Al-Saif
Keyword(s):  
Fourier Transform ◽  
Cantilever Beam ◽  
Low Frequency ◽  
Frequency Component ◽  
Time Dependent ◽  
Cantilever Beams ◽  
External Excitation ◽  
Transform Method ◽  
Fourier Transform Method ◽  
Optimal Position

A Fourier Transform method is applied to determine the displacements of a cantilever beam subjected to a multi-frequency excitations at the base. To dampen the displacements of the beam, a point mass is attached to the beam. The position of the mass on the beam is determined such that the overall deflections of the beam are minimal. It is shown that these attachments to the beam are effective in reducing the vibrations of the beam but their position is frequency dependent. It is shown that the low frequency component of the external excitation is most important in finding the optimal position of the mass.

scholarly journals A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 440 ◽  
Author(s):  
Jun Peng ◽  
Rui Wang ◽  
Hontao Liao ◽  
Yanhui Zhou ◽  
Heng Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Storage System ◽  
Low Frequency ◽  
Energy Storage System ◽  
Frequency Component ◽  
High Frequency Component ◽  
Adaptive Wavelet ◽  
Hybrid Energy ◽  
Hybrid Energy Storage

In this paper, a real-time energy distribution strategy is designed by a layer-adaptive wavelet transform algorithm and proposed to meet the load power demand while distributing the high-frequency component to supercapacitors and the low-frequency component to batteries in a hybrid energy storage system. In the proposed method, the number of decomposition layers of wavelet transform corresponding to the load power is adaptively determined by dividing the operation zone of supercapacitors into eight cases to respectively distribute the low frequency component to batteries and the remaining high frequency component to supercapacitors. Firstly, since the state of charge of supercapacitors decreases faster as the decomposition layers increases, the state of charge of supercapacitors is divided into eight cases of operation zones. Secondly, since supercapacitors act as the peak power buffer unit, the corresponding number of decomposition layers is finally adaptively determined according to the operation zone of supercapacitors. An experiment testbed is built to verify the effectiveness of the proposed method. Extensive experiment results show that the proposed method provides a better real-time energy sharing between supercapacitors and batteries when compared with the conditional method.

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