An-Efficient-Randomized-Algorithm-for-Detecting-C

说明：  matlab 小波分析源码 小波去噪 仿真分析(matlab source wavelet analysis wavelet denoising simulation analysis)

小波包和EMD方法对信号分析后的频谱图比较，可以对EMD的运行参数进行修改。(Spectrum comparison of Wavelet package and EMD)

利用matlab中的小波变换函数，对图像进行小波降噪，并分析降噪效果。(The wavelet transform using matlab function, the image wavelet noise reduction, and analyze the noise reduction effect.)

采用小波变换的方法实现心电信号的滤波，分别使用了硬阈值、软阈值和改进阈值方法，并实现了滤波效果的评价（均方差和信噪比）(Wavelet transform of ECG signal filtering method, respectively, using a hard threshold and soft threshold and improve the threshold method, and to achieve the filtering effect of the evaluation (both variance and signal to noise ratio))

能够实现二维S变换的MATLAB程序，与Stockwell的经典程序不同(the code which can implements the 2D S-transform)

对于PCA ，LDA，BIC，EBGM都有很详细的代码。但是需要在LINUX下配置运行。不过代码较有参考价值。(For PCA, LDA, BIC, EBGM have a very detailed code. But the need to configure to run under LINUX. However, a more useful code.)

mimo_BP用于十字型阵列雷达，利用匹配滤波理论进行算法仿真(mimo_BP Used for cross-array radar, using matched filter theory for algorithm simulation)

简单的方向滤波器， 带测试图的， 可以直接运行(Simple directional filter, with a test chart, you can directly run)

彩色RGB图像小波变换，并且重构出低频、中频、高频各个频带子块的图像。(RGB image wavelet and restruct the different frequency band)

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)