Actually the kernel design in the recognition method based on discrete time - frequency representation is a problem of feature selection from the ambiguity functions to reduce feature dimension.
基于离散时频分布的信号识别方法，将时频核设计问题转化为以信号自模糊函数为原始特征的特征选择问题，以实现特征降维和信号识别。
The signal of instant vibration testing for soil compactness is analyzed both in time domain and frequency domain. Some feature parameters reflecting the characteristics of the signal are extracted.
从时间域和频率域两个角度对土密实度瞬态振动测试信号进行了分析，提取了信号的特征参量。
Wavelet transform has a good analyzing feature and a good time frequency localization. Spectrum whitening is a effective tool of frequency compensation in high resolution processing.
小波变换具有分析性质好和时—频局域化好的特性，而谱白化方法是高分辨处理中一种有效的频率补偿手段。
This paper mainly introduces that the fault vibrating signal of gears was decomposed into time-frequency domains by double-orthogonal wavelet analysis and the fault feature of gears was picked up.
利用双正交小波基将齿轮的故障振动信号分解到时频域，并提取出齿轮的故障特征。
In order to solve these problems, we proposed a single feature vector recognition model based on whole time-frequency information structure of digit speech.
为了解决这些问题，我们提出了基于数字语音时频信息整体结构的单特征向量识别模型。
In the view of feature signal extraction, the local wave time-frequency analysis and information entropy were used to deal with fault diagnosis.
从特征信号提取的角度出发，采用局域波时频分析和信息熵结合的方法进行往复式压缩机故障诊断。
This paper aims to consummate the wavelet theory, combining with its excellent time and frequency feature , present some new algorithms and use to image recognition.
旨在完善小波理论的同时，结合小波的时频域特性设计出新的应用算法，将其用于图像识别。
Aluminum wire ultrasonic bonding; transducer drive current; signal time-frequency analysis; feature selecting; quality online monitoring.
粗铝丝超声引线键合；换能器驱动电流；信号时频分析；特征提取；质量在线监测。
The technique of wavelet transform has been applied and studied in the image coding field extensively because of its good feature of time-frequency and human visual system.
小波变换技术以其良好的空间—频率局部特性和与人眼视觉特性相符的变换机制，在图像编码领域得到了广泛的应用和研究。
Wavelet analysis is widely used in digital signal and image fields because of its good time frequency localization feature, but it doesn't do well in timely processing of long signals.
小波分析有良好的时-频局部化性能，现被广泛应用于数字信号和图像处理等领域，但其在处理有限长信号时的实时性不太理想。
The experiment result shows that the time-frequency method based on EMD can effectively extract the feature of unbalanced fault signal and is proper for non-frequency modulation signal procession.
研究结果表明：基于经验模式分解的时频分析方法可以很有效地提取到非平稳故障特征信号，是一种适合于非线性信号处理的方法。
This paper USES joint time-frequency analysis (JTFA) for processing transient electromagnetic (TEM) field signals that have diffuse feature in geophysical exploration.
本文将联合时-频分析(JTFA)用于处理地球物理勘探中具有扩散性质的瞬变电磁场信号。
The good response relationship between time-frequency spectrum and geologic structural feature provides straightforward foundation for seismic-geologic fine interpretation.
时频谱与地质结构特征之间的良好响应关系为地震地质精细解释提供了直观的依据。
The feature extraction method based on efficient points of response curve is studied in time domain and frequency domain.
在时域和频域中给出了基于响应曲线波形有效点的模拟电路故障特征提取方法。
Due to the good localization feature of the wavelet transform both in time plane and in frequency plane, a wavelet-based de-noise method is presented for extracting EPs in single trails.
由于小波变换在时域和频域上都具有良好的局部化特性，基于小波变换的去噪方法，可以达到从单次样本中提取视觉诱发电位的目的。
Fault sensitive feature is abstracted from high and low frequency of fault residual signal, by which sensor fault time, fault reason, fault degree can be diagnosed.
通过残差信号的故障高低频特性，提取故障敏感特征，实现了传感器故障时间、故障原因、故障程度的诊断。
The concept of local energy in time-frequency plane, based on local wave theory, is defined. Then a new feature extraction method based on the local energy in time-frequency plane is given.
在局域波理论的基础上，引入了时频局部能量的概念，进而提出了一种基于时频局部能量的特征提取方法。
Due to the good localization feature of the wavelet transform both in time plane and in frequency plane, we present a wavelet-based denoising method for extracting EPs in single trails.
由于小波变换在时域和频域上都具有良好的局部化特性，本文提出了一种基于小波变换的去噪方法，以期达到从单次样本中提取视觉诱发电位的目的。
This paper includes studying many feature extraction methods such as time-frequency method, multi-resolution analysis and correlation analysis to analyze the Micro-Doppler signal.
本论文主要研究采用多种特征提取方法对激光微多普勒信号进行时-频分析、多尺度分析和相关变换技术。
After analyzing their properties in time and frequency domain, we propose two feature-extraction methods and performed some classification experiments based on support vector machine. Ther...
在分析两类飞机时域、频域特性的基础上给出了两种特征提取方法，利用支持矢量机进行了识别实验，结果表明所提出的方法是可行的。
After analyzing their properties in time and frequency domain, we propose two feature-extraction methods and performed some classification experiments based on support vector machine. Ther...
在分析两类飞机时域、频域特性的基础上给出了两种特征提取方法，利用支持矢量机进行了识别实验，结果表明所提出的方法是可行的。
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