Noise reduction with wavelet analysis used in phalange recognition of computerized skeletal age assessment system can enhance the precision of skeletal age assessment.
小波去噪声技术应用于计算机骨龄评价系统中的指骨识别能提高骨龄评价的准确度。
The present method for noise reduction with wavelet transform is based on the thresholding method and threshold selection will depend on the estimation of the noise standard deviation.
目前的小波去噪绝大多数都是基于阈值决策的方法，而阈值选择往往都是建立在对信号小波变换系数的方差估计上。
In this paper, the theory of wavelet transformation in signal noise reduction is introduced, together with the algorithm of signal decomposition and reconstruction.
介绍采用小波变换进行信号降噪的原理；利用小波分解和重构函数对轧机主传动轴应力和扭矩信号进行降噪处理。
After noise reduction in the signal, using "wavelet packet-energy" to extract the characteristic vector and input them to the neural network for fault identification.
在对采集到的信号降噪后，利用“小波包-能量”法提取特征量，并将其输入到神经网络中进行故障识别。
This paper achieves noise reduction by using wavelet packet transform, semisoft shrinkage function, Donoho standard deviation estimate and threshold obtain based on statistics.
计算结果表明：变形统计值与变形监测值吻合较好，统计复相关系数较大，估计标准误差较小。
This paper achieves noise reduction by using wavelet packet transform, semisoft shrinkage function, Donoho standard deviation estimate and threshold obtain based on statistics.
计算结果表明：变形统计值与变形监测值吻合较好，统计复相关系数较大，估计标准误差较小。
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