一种基于离散小波变换的音素分段算法

提出了一种基于离散小波变换的新的音素分段算法。首先对原始语音信号取绝对值,然后对其进行小波变换,认为8 阶小波变换绝对值的极大值点对应于原始语音的音素分段点。该算法较传统的算法计算量小。实践证明。

基于Mallat-Zhong离散小波变换小波的超声图像各向异性扩散抑噪方法

针对传统各向异性扩散方法在超声图像散斑噪声抑制中存在的噪声抑制不充分与边缘特征保持不足的问题,提出一种基于Mallat-Zhong离散小波变换(MZ-DWT)小波的散斑噪声抑制方法。该方法将MZ-DWT小波分析与期望值最大化(EM)算法作为图像中均匀区域与边缘区域的鉴别因子,使扩散系数能够更准确地控制扩散强度与扩散速度,从而达到充分抑制噪声和保护边缘的目的。实验结果表明,所提方法在有效抑制散斑噪声的同时,更好地保持了图像细节信息,其性能优于传统各向异性扩散方法。

VLSI Implementation of a Wavelet Image Coder

A modular architecture for two dimension (2 D) discrete wavelet transform (DWT) is designed.The image data can be wavelet transformed in real time,and the structure can be easily scaled up to higher levels of DWT.A fast zerotree image coding (FZIC) algorithm is proposed by using a simple sequential scan order and two flag maps.The VLSI structure for FZIC is then presented.By combining 2 D DWT and FZIC,a wavelet image coder is finally designed.The coder is programmed,simulated,synthesized,and successfully verified by ALTERA CPLD.

A high-throughput VLSI design for JPEG2000 9/7 discrete wavelet transform

To achieve high parallel computation of discrete wavelet transform （DWT） in JPEG2000, a high-throughput two-dimensional （2D） 9/7 DWT very large scale integration （VLSI） design is proposed, in which the row processor is based on flipping structure. Due to the difference of the input data flow, the column processor is obtained by adding the input selector and data buffer to the row processor. Normalization steps in row and column DWT are combined to reduce the number of multipliers, and the rationality is verified. By rearranging the output of four-line row DWT with a multiplexer （MUX）, the amount of data processed by each column processor becomes half, and the four-input/four- output architecture is implemented. For an image with the size of N x N, the computing time of one-level 2D 9/7 DWT is 0.25N2 ＋ 1.5N clock cycles. The critical path delay is one multiplier delay, and only 5N internal memory is required. The results of post-route simulation on FPGA show that clock frequency reaches 136 MHz, and the throughput is 544 Msample/s, which satisfies the requirements of high-speed applications.

Research of Underwater Bottom Object and Reverberation in Feature Space

The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.

Denoising of hyperspectral imagery by cubic smoothing spline in the wavelet domain

The acquired hyperspectral images （HSIs） are inherently attected by noise wlm Dano-varylng level, which cannot be removed easily by current approaches. In this study, a new denoising method is proposed for removing such kind of noise by smoothing spectral signals in the transformed multi- scale domain. Specifically, the proposed method includes three procedures： 1 ） applying a discrete wavelet transform （DWT） to each band; 2） performing cubic spline smoothing on each noisy coeffi- cient vector along the spectral axis; 3 ） reconstructing each band by an inverse DWT. In order to adapt to the band-varying noise statistics of HSIs, the noise covariance is estimated to control the smoothing degree at different spectra｜ positions. Generalized cross validation （GCV） is employed to choose the smoothing parameter during the optimization. The experimental results on simulated and real HSIs demonstrate that the proposed method can be well adapted to band-varying noise statistics of noisy HSIs and also can well preserve the spectral and spatial features.

A NEW NON-INVASIVE METHOD FOR VALVE STICTION DECTION USING WAVELET TECHNOLOGY

In this letter, we present a novel approach of valve stiction detection using wavelet technology. A new non-invasive method is developed with the closed-loop normal operating data. The redundant dyadic discrete wavelet transform is used to decompose the data at different resolution scales. Based on the Lipschitz regularity theory, wavelet coefficients analysis across scales is performed to detect the jumps in the controlled variables. Adaptive wavelet de-noising is then applied to the data. Features of the valve stiction patterns are extracted from the de-noised data and the valve stiction probability is calculated.

Analysis of Electroencephalogram Based on Wavelet Spectrum and Wavelet Entropy

Based on discrete wavelet transform, both relative wavelet energy （RWE） and segment wavelet entropy （SWE） of electroencephalogram （EEG） are defined in this paper. The RWE provides quantitatively the information about the relative energy associated with different frequency bands present in the EEG. The SWE carries information about the degree of order or disorder associated with different time segment of EEG evolution, which can determine the time-segment loealizations of abnormal dynamic processes of brain activity due to the localization characteristics of the wavelet transform. The experimental results show that the RWE and SWE are different between epileptic EEGs and normal EEGs, which demonstrate that the RWE and the SWE are helpful to analyze the dynamic behavior of different EEGs.