此方法回避了多维谱峰搜索和参数配对，克服了在短数据时离散傅立叶变换测频分辨率低和性能差的不足。
Without any spectral peak search and parameters pairing, the resulting method is computationally efficient with high resolution and small variance, even in the short data length.
而要通过快速傅立叶变换(FFT)获得准确的频域参数，又必须得到完整的时域波形数据。
Meanwhile, data of complete time domain waveform is needed in order to calculate accurate S-parameters of frequency domain via fast Fourier transform (FFT).
对采样后的数据运用快速傅立叶变换(FFT)进行数值计算，获得了高精度的风井综合参数的测量。
Sampling data using fast Fourier transform (FFT) to numerical algorithm has obtained the high accuracy air shaft synthesis parameter survey.
在这两种算法中，混沌序列控制小波变换的某些参数或决定数据的隐藏位置，提高了算法本身的安全性。
In these two algorithms, chaotic sequences control some parameters of wavelet transform or decide the hiding location to improve the algorithm itself security.
每一层都有一个简单的API：用一些含或者不含参数的可导的函数，将输入的三维数据变换为三维的输出数据。
Every Layer has a simple API: it transforms an input 3d volume to an output 3d volume with some differentiable function that may or may not have parameters.
每一层都有一个简单的API：用一些含或者不含参数的可导的函数，将输入的三维数据变换为三维的输出数据。
Every Layer has a simple API: it transforms an input 3d volume to an output 3d volume with some differentiable function that may or may not have parameters.
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