Papers by Keyword: Wavelet Transform (WT)

Abstract: According to the problem of the corona discharge, a new image fusion rule based on wavelet transform was proposed, making a double spectrums imaging system about spectrum characteristic of high-voltage electrical corona. The validity and feasibility has been approved by using Matlab as the experiment platform in the paper. The experimental results show that proposed algorithm is very effective in image fusion, which fuses more details of input images and improve the locating precision of the corona detection system.

Abstract: Remote sensing technology has rapid development in the past half one century, it is widely used in various fields and society. But the clouds have affected the quality of remote sensing data, how to effectively use the modern computer science and technology to remove the cloud is a hot issue in the field. From the theory of cloud formation in the remote sensing image, we analyze the formation mechanism, and based on this we do two layers decomposition and reconstruct the structure according to wavelet transform in network communication, and establish the image degradation model. Combining Fourier transformation, we set up the removing cloud fusion model of remote sensing image. Through the simulation experiment, the effect is significant. To a certain extent, it provides technical support for theory study and practice operation.

Abstract: In this paper, a image segmentation algorithm based on wavelet transform are presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Abstract: Harmonics of power system include steady and transient components, extraction and analysis of them can improve the power quality. Tunable Q-factor wavelet transform (TQWT), for which the Q-factor can be easily specified, is adopted to accomplish the extraction of non-stationary harmonic component from the noisy signal. The proposed method decomposes a signal into noise component and non-stationary harmonic component based on suitable Q-factor. Feasibility and effectiveness of the proposed method are verified by the simulation study.

Abstract: In order to overcome the baseline influence during the XRF spectra analysis, baseline deviation should be corrected. The multi-scale wavelet transform was used to remove the baseline in this paper. It is benefit that 5 decomposition levels should be selected. The whole channel was divided into several intervals. The initial interval width should be set 50. Scale coefficients were corrected according to the minimum value line. Then the scale and wavelet decomposition coefficients were reconstructed. The result shows that this method can remove the baseline effectively, especially for two sections viz. 0-400 and 1380-4095 channel.

Abstract: 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.

Abstract: It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

Abstract: In this paper, a research is carried on to identify damage of reticulated shell structures based on the combination of modal curvature method and wavelets transform method. Take a scaled model of the reticulated shell structure as an example to analyze, the cross section of one member supposed to have a slight damage of stiffness reduction. In order to locate the damage, modal curvatures of the structure are taken as damage indexes to perform continuous wavelet transform. Results of the numerical analysis indicate that the difference of wavelet transform coefficients of modal curvature can be used to locate damage roughly, while the wavelet transform coefficients of modal curvature difference can be used to locate damage more precisely with easier and more reliable data processing. So it is clear that the damage identification based on modal curvature and wavelet transform is quite effective.

Abstract: Due to the complexity and volatility of microgrid load sequence, this paper proposes an Adaptive Neuro-fuzzy inference system (ANFIS) prediction algorithm using Quantum Particle Swarm Optimization (QPSO) to optimize the network parameters, combining with Wavelet analysis method for preprocessing. The example shows that the proposed approach to microgrid load forecasting is both novel and effective, can improve the accuracy of load prediction.

Abstract: Video playback has been one of the most important online communication ways. With the application of stereo video, large amount of video data need to be stored and transported so that fluency and clarity of demand system, and how to efficiently conduct compressed encoding for stereoscopic video data becomes a hot topic currently. In view of this problem, this paper puts forward the video-on-demand compression algorithm based on the optimal multi-band Haar wavelet transform, through the research on wavelet transform algorithm model to reinforce the algorithm secondly, strengthening from the binary wavelet theory into octal wavelet system theory to get better compression capability. The simulation experiments show that video-on-demand compression algorithm based on the optimal multi-band Haar wavelet transform proposed in this paper has a good compression performance not only under medium and high bit- rate conditions, and also reaches the H. 263 under low bit-rate condition.