Wavelets-applications

去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信信号，避免噪声对心电信号特征点的识别与提取造成误判漏判 已通过测试。 (Remove the EMG interference mixed with the signal acquisition process in mind, frequency interference, baseline drift and noise channel signal to avoid noise caused by the misjudgment of the Missing has been tested on ECG feature identification and extraction.)

本程序用Mallat算法实现小波变换,方法简答以实现(This program uses the mallat algorithe to implement wavelet transform)

先进行提升小波变换，然后使用提升小波进行图像的分解和重构(Be carried out on lifting wavelet transform, and then use the images to enhance the wavelet decomposition and reconstruction)

包含压缩传感的随机矩阵程序，如小波变换和高斯随机矩阵和omp重构算法(Random matrix containing the compressed sensing programs, such as wavelet transform and Gaussian random matrices and omp reconstruction algorithm)

An Efficient Randomized Algorithm for Detecting Circles

MATLAB实现的高斯平滑滤波源代码，用了高斯函数的定义来做，附实例，效果很好(gauss filter by matlab)

小波变换在故障诊断中的应用 441 18.1 引言 441 18.2 基本原理 442 18.3 小波变换在轴承故障诊断中的应用 444 18.3.1 轴承外环故障诊断 444 18.3.2 轴承滚动体故障诊断 448 18.4 小波变换在齿轮故障诊断中的应用 450 18.5 小波包在轴承故障诊断中的应用 452 18.5.1 轴承外环故障诊断 453 18.5.2 轴承滚动体故障诊断 454(wavelet transform fault diagnosis of Introduction 441 18.1 441 18.2 442 basic tenets 18.3 wavelet transform bearing fault diagnosis of 444 18.3.1 ring bearing 444 1 Fault Diagnosis 8.3.2 rolling bearings Fault Diagnosis 448 18.4 wavelet transform in Gear Fault Diagnosis of 450 1 8.5 wavelet packet bearing fault in the diagnosis of 452 18.5.1 ring bearing fault diagnosis 453 18. 5.2 roller bearings Fault Diagnosis 454)

数字基带传输基带信号，指未经调制的信号。这些信号的特征是其频谱从零频或很低频率开始，占据较宽的频带。 基带信号在传输前，必须经过一些处理或者某些变换（如码型变换、波形和频谱变换）才能送入信道中传输。处理或变换的目的是使信号的特性与信道的传输特性相匹配。 数字基带信号是消息代码的波形表示。表示形式有多种，有单极性、双极性波形，归零和非归零波形，差分波形、多电平波形之分，各自有不同的特点。 研究数字基带信号通过加性高斯白噪声信道的传输，并且假设信道除了叠加高斯噪声外，不会对信号产生其他失真。(matlabSimulation(pulse amplitude modulation) modulation signal in AWGN (additive white Gaussian noise AWGN (Additive White Gaussian Noise)) under the channel bit error rate and bit error rate performance, and compared with the theoretical value.)

小波基矩阵乘法快速算法工具箱，里面有目录，供大家学习参考！(Tools for Fast Matrix Multiplication in Wavelet Basis)

用卷积深度置信网络CDBN的方法来处理睡眠EEG;(use Convolutional deep belief networks to sleep EEG)