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 analyzed the characteristic of noise in hyperspectral data deeply, and puts forward a de-noising method based on stationary discrete wavelet transform (SDWT).
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。
By exploiting wavelet packet transform to analyze signals both in time and frequency space, this paper researches the statistic property of non-Gaussian stationary noise and its signal structure.
该文利用小波包变换的时频局部分析能力，研究了非高斯分布平稳随机噪声的统计特性，揭示了非高斯噪声信号的信号结构。
Orthogonal wavelet transform (OWT) matrix is constructed and the residual correlation property of OWT is analyzed under the stationary and nonstationary models.
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。
In this paper, two kinds of new time-frequency analysis approaches for non-stationary signal processing are introduced, which are wavelet transform and short-time Fourier transform.
本文介绍处理非平稳信号的新型工具——小波分析、短时付氏变换两种时频分析方法。
EMD method is a new method for analyzing nonlinear and non-stationary data, which has more advantage than wavelet analysis, and it can process short time series precisely.
EMD方法是对非平稳、非线性信号进行分析的一种新的时频分析方法。它比小波分析等方法具有更强的特性并能准确地处理非常短的数据序列。
The space of prediction and application of non-stationary time series were expanded through the combined model of wavelet analysis, gray and time series prediction methods.
将小波分析理论、灰色预测理论和时间序列预测法组合进行需水量的预测，为原始非平稳时间序列的预测应用拓展了空间。
The wavelet transform has a good performance of local analyzing in both time domain and frequency domain. It is fit for analyzing non-stationary signals, such as ultrasonic signal.
而小波具有“变焦距”特性，在时域和频域中具有良好的局部分析能力，适合于超声等非平稳信号处理。
The wavelet transform has a good performance of local analyzing in both time domain and frequency domain. It is fit for analyzing non-stationary signals, such as ultrasonic signal.
而小波具有“变焦距”特性，在时域和频域中具有良好的局部分析能力，适合于超声等非平稳信号处理。
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