For the problem of image enhancement, an improved nonlinear extrapolation method in frequency space is proposed.
针对图像增强的问题，提出了一种改进的频域非线性外推方法。
Theoretic analysis showed that the minimum jamming power could enter ST-FSK demodulator if frequency space were optimal.
理论分析证明，当频率间隔满足一定条件时注入ST-FSK解调器的干扰功率达到最小。
An effective denoising method on the frequency space is presented according to the characteristics of remote sensing images.
针对遥感图像的特点，给出了一种有效的基于频域的去噪方法。
The traditional neural network correction has a good adaptivity to the noise. But with a stronger low frequency space noise, the correction effect is very poor.
传统的神经网络非均匀性校正算法对噪声具有较好的自适应性，但当空间低频噪声较大时，校正效果明显下降。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
The good localization characteristics of wavelet functions in both time and frequency space allowed hierarchical multi-resolution learning of input-output data mapping.
利用小波变换所具有的良好的时频分析特性，实现了输入输出之间映射关系的多分辨学习。
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.
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。
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。
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。
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