文章提出了基于离散平稳小波变换和最佳阈值分割算法的织物疵点边缘检测方法。
This paper proposed a method for fabric defects edge detection based on discrete stationary wavelet transform (DSWT) and optimal threshold segmentation algorithm (OTSA).
文章深入分析了高光谱遥感数据中噪声的特点，提出了一种基于平稳小波变换的改进小波滤噪算法。
This paper analyzed the characteristic of noise in hyperspectral data deeply, and puts forward a de-noising method based on stationary discrete wavelet transform (SDWT).
由于小波变换有效地克服了傅氏变换在处理非平稳的复杂信号时所存在的局限性，因而在图像与信号处理领域受到了广泛的重视。
Due to wavelet transforms efficiently overcoming the limitations of Fourier transform in dealing with unstable and complicated signal, they are popular in image compression and signal processing.
研究结果表明，小波变换是分析非平稳随机时间序列的有效工具，在水文水资源领域应用潜力很大。
The results indicate that the wavelet transform is an effective tool for nonstationary stochastic series analysis, which has great potential in hydrology and water resources research.
介绍了小波变换的基本概念和滤波特性，给出了小波变换作为一种新的分析工具在非平稳信号分析中的应用方法。
The concepts and its filter of wavelet transform are introduced, the application of wavelet as a kind of new analysis tools in unstable signal analysis is presented.
构造了正交小波变换矩阵，分析了平稳模型和非平稳模型下正交小波变换的残余相关特性。
Orthogonal wavelet transform (OWT) matrix is constructed and the residual correlation property of OWT is analyzed under the stationary and nonstationary models.
地震动是由于地震引起的地面运动，其时间历程函数表现为具有突变特征的非平稳随机过程，因此更适于利用小波变换进行频谱分析。
The ground motion time function is a kind of time serial signals, which is a highly varying and nonstationary randow procedure and fit for wavelet analysis.
对此，本文研究了一种非平稳环境下基于小波变换的信号去噪算法。
This algorithm can overcome the deficiency of the conventional algorithms of noise reduction, which were only efficient for stationary environments and have large level of signal residual noise.
实验结果表明，小波变换的多分辨率分析对于分析处理具有时变谱特性的非平稳信号是一种新的有效方法。
Experiments show that the multiresolution analysis of wavelet transform is an effective new method for processing unstable signals having time variant spectra.
实验结果表明，小波变换的多分辨率分析对于分析处理具有时变谱特性的非平稳信号是一种新的有效方法。
Experiments show that the multiresolution analysis of wavelet transform is an effective new method for processing unstable signals having time variant spectra.
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