The new algorithm mainly recurred to the ability of detecting singularity in signal by continuous wavelet transform and multi-resolution analysis of wavelet transform.
算法充分利用连续小波变换探测信号奇异性的能力和小波的多分辨率特性。
The features of gene expression are extracted by the wavelet multi-resolution analysis, the features are classified by the support vector machines and BP neural network methods.
采用小波多分辩率分析方法提取基因表达的特征，利用支持向量机和BP神经网络方法进行分类。
While wavelet transform is suitable in image compression due to its multi-resolution and multi-scale analysis property, and is used in the standards of image compression.
小波变换具有多分辨率和多尺度特性，特别适合于二维图像信号的处理，目前已应用在静止图像的压缩标准中。
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