Narrow Results

Ultrasonic receiving wave can reflect physical properties and damage degree of coal samples. Therefore, it is of great significance to deeply study the parameters of ultrasonic. In this paper, time-domain characteristics of receiving wave are analyzed systematically, which present good correlation with stress. The frequency spectrum of receiving wave is obtained using Fast Fourier Transform (FFT), and peak frequency and centroid frequency are calculated. During the entire loading process, peak frequency fluctuates around 110kHz, but corresponding centroid frequency decreases obviously at the end stage of loading. According to multifractal theory, the multifractal spectrum of wavelet packet energy characteristics is calculated. The results show that wavelet packet energy distribution has obvious multifractal characteristics, and multifractal parameter $\mathrm{\Delta }\alpha$ presents downward trend before coal samples buckling failure. Based on damage process of the coal samples, the reason of change in $\mathrm{\Delta }\alpha$ value is related to damage degree of coal samples. This research is of great significance for understanding the deformation and failure process of coal samples using ultrasonic technology.

In view of the frequency spectrum characteristics of vibration signal of rotating machinery, the versatile model of pattern recognition and fault diagnosis of rotating machinery based on wavelet packet-neural network is presented. The abrupt change information of vibration signal can be obtained and the features related to the fault can be extracted by employing the multi-dimension and multi-resolution characteristics of wavelet to decompose and reconstruct the vibration signal. Energy of special frequency ranges is selected as feature vector and is put into ART2 neural network, then the trained neural network is able to perform real-time diagnosis of rotating machinery fault. The effectiveness of this method is proved by emulating rotating machinery failures.

Image fusion is an important concept in remote sensing. Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. Pansharpening is aimed on fusion of a low-resolution multispectral image with a high-resolution panchromatic image. Because of this fusion, a multispectral image with high spatial and spectral resolution is generated. This paper reports a new method to improve spatial resolution of the final multispectral image. The reported work proposes an image fusion method using wavelet packet transform (WPT) and principal component analysis (PCA) methods based on the textures of the panchromatic image. Initially, adaptive PCA (APCA) is applied to both multispectral and panchromatic images. Consequently, WPT is used to decompose the first principal component of multispectral and panchromatic images. Using WPT, high frequency details of both panchromatic and multispectral images are extracted. In areas with similar texture, extracted spatial details from the panchromatic image are injected into the multispectral image. Experimental results show that the proposed method can provide promising results in fusing multispectral images with high-spatial resolution panchromatic image. Moreover, results show that the proposed method can successfully improve spectral features of the multispectral image.

A new algorithm for speech enhancement based on wavelet shrinkage method is presented in this paper. First, the noisy speech by the Bark-scaled Wavelet Packet (BS-WPD) is decomposed to simulate the human auditory characteristics. Then a new thresholding algorithm which has many advantages over soft and hard thresholdings put forward by D.L. Donoho and I.M. Johnstone is proposed. Simulation results indicate that this new method is very useful and efficient in the process of white noise reduction from speech, and the new thresholding algorithm gives better SNR improvement than other traditional thresholding algorithms.