匹配跟踪作为一种自适应的信号分解算法，为语音和音频正弦建模提供了一个新的框架。
As an adaptive algorithm of signal decomposition, matching pursuits provides a new framework for sinusoidal modeling of speech and audio signal.
经验模式分解（EMD）通过筛分过程将原始信号分解成若干个基本模式分量（IMF），可看作无需预设带宽的自适应高通滤波方法。
Empirical mode decomposition(EMD) is a signal processing technique to decompose data set into several intrinsic mode functions(IMF) by a sifting process.
在异步电机转子断条故障检测中，将原信号最优小波包分解，经自适应滤波后再信号重构。
During detection of asynchronous motor rotor-bar broken fault, the signal of current was reconstruct after decomposed by best wavelet packet and adaptive filtered.
该方法采用基于熵的小波包最佳基选取准则，对局部损伤信号进行自适应小波包分解，将分解结果显示于时—频空间即相平面上。
The L F signals are transformed by entropy based on wavelet packet algorithms for best basis selection, and the results are displayed in time- frequency space namely phase plane.
基于特征分解谱分析技术，提出并研究了在雷达中自适应抑制窄带数字通信信号干扰的方法。
An adaptive method applying eigenvalue decomposition-based spectrum analysis technique to narrow-band digital communication interference suppression in radar is proposed and studied.
在对带白噪声信号的小波变换特性分析的基础上，基于自相关函数的白噪声检验方法，提出了一种确定小波分解层数的自适应算法。
Threshold de-noising based on wavelet transform which is proposed to determine the decomposition order adaptively is an efficient method to reduce the white noise in the digital signal.
针对传统滤波方法处理非平稳信号的不足，提出利用经验模态分解法来处理转子启动信号，通过此方法的自适应滤波特性来提取这类信号中的低频分量。
This paper describes a method to extract the low frequency component from rotor startup signal based on empirical mode decomposition, which overcomes the difficulties of traditional filter methods.
由于这种信号分解方法是自适应的，因此也是高效的。
This decomposition method is adaptive and, therefore, highly efficient.
与通常的自适应多用户检测算法相比，该算法利用了小波变换对小波空间进行了分解，信号经小波变换后自相关性会下降，收敛速度提高。
The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence.
EMD分解法是一种自适应的信号处理方法，适用于分析非线性、非平稳过程。
The EMD method is a new method to analyze instability and nonlinearity.
它的分解基是随动态信号波形的变化而变化 ，具有自调节自适应的特征 ，因此能在时频域内描述非平稳非线性信号的局部特性。
Since the decomposition bases can vary with the local features of dynamic signals, the method is adaptive , therefore, highly efficient for describing nonlinear and non?stationary signals.
它的分解基是随动态信号波形的变化而变化 ，具有自调节自适应的特征 ，因此能在时频域内描述非平稳非线性信号的局部特性。
Since the decomposition bases can vary with the local features of dynamic signals, the method is adaptive , therefore, highly efficient for describing nonlinear and non?stationary signals.
应用推荐