Narrow Results

How to find a powerful method of feature representation and extraction is constantly a key issue in 1-D or 2-D signal recognition, such as face recognition. Wavelet Packet(WP) is a potential technique in this regard. However, we face the problems on how to find the optimal WP decomposition and extract the discriminant features. In this paper, we propose a fuzzy c-means shaped membership function in the evaluation of the classification abilities of WP sub-spaces or WP coefficients for seeking the optimal WP decomposition and extracting discriminant features. The classification is performed by Enhanced Fisher Linear Discriminant Model (EFM) and a conventional linear classifier. Experiments on typical Yaleface database are carried out. Compared with the well-known Principal Component Analysis(PCA), the face recognition rate of WP based method is higher than that obtained by PCA.

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.

This paper presents the method of multi-resolution analysis used in 2D image data to extract the curved edge features. The method is based on the combination of multi-resolution decomposition through Wavelet Packet and Prime Ridgelet transform. We call this combination Prime Wavelet Packet Contourlet Transform-PWPC. At each leave of Packet Wavelet Packet Tree, the prime ridgelet transform is applied on the band pass image or packet, which contains the high frequency data. The experiment shows that the PWPC coefficients are good approximations to curved edges. The speed of PWPC is faster than that of the basic Curvelet transform. This transform is very suitable to represent the noisy curved features that often exist in medicine or nano/micro images.

There are a series of advantages about the double involute gear with ladder shape of tooth. But whether the ladder-shaped of the double involute gear will cause the increase of the vibration or bring some other vibration components? This problem is especially concerned by people all the while. The essence of wavelet packet analysis is to make further decomposition of wavelet decomposed result, so the analysis will yield much better frequency localization. Duo to wavelet packet has agile and changeful characteristics in disposing signals, it can be used to analyzed and compared the vibration characteristics of common involute gear and double involute gear conveniently and exactly by decomposing and reconstructing signal components of correlative frequency components. The result shows that the double involute gear has lower noise, smaller vibration and better dynamics characteristic than common inlovute gear

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.