多曝光图像融合方法FMMR

实用的多小波工具箱 添加这些文件，以你的工作文件夹，并开始输入》multidemo 查看采取多小波变换对不同的图像，您也可以查看，编辑和使用任何其他文件提交在此文件夹中(Many practical wavelet toolbox to add the files to your working folder, and start typing " multidemo view taken by many wavelet transform of different images, you can also view, edit and use of any other documents submitted in this folder)

Clos Image With Test On Finger Picture

求信号的时频熵，对非平稳信号进行emd分解后，求时频熵，得出其故障特征(For the signal s time-frequency entropy, the nonstationary signals are EMD decomposition, and time-frequency entropy, the fault feature)

定义一个用于创建对象的接口，让子类决定实例化哪一个类。Factory Method 使一个类的实例化延迟到其子类。(Analysis and implementation of common design patterns (C++))

这是学习分数阶傅里叶变换，必须的算法这是学习分数阶傅里叶变换，必须的算法这是学习分数阶傅里叶变换，必须的算法(the of fragnaii or fft)

construct a single Lagrange polynomial

相位解缠是INSAR数据处理的重要步骤，本文用基于FFT的最小二乘法实现实现相位解缠(Phase unwrapping)

计算EEG数据集的锁相值（PLV）。 ％ ％输入参数： eegData是3D矩阵numChannels x numTimePoints x numTrials srate是EEG数据的采样率filtSpec是用于在期望的感兴趣频带中过滤EEG信号的滤波器规范。 它是一个具有两个字段的结构，顺序和范围。 范围指定频带的限制，例如，对于伽马频带，使用filtSpec.range = [35 45]。 在filtSpec.order中指定FIR滤波器的顺序。 一个有用的经验法则可以包括约4到5个周期的所需信号。 例如，对于以500Hz采样的eeg数据，filtSpec.order = 50对应于100ms并且包含~4个周期的γ波段（40Hz）。(Computes the Phase Locking Value (PLV) for an EEG dataset. % % Input parameters: % eegData is a 3D matrix numChannels x numTimePoints x numTrials % srate is the sampling rate of the EEG data % filtSpec is the filter specification to filter the EEG signal in the % desired frequency band of interest. It is a structure with two % fields, order and range. % Range specifies the limits of the frequency % band, for example, put filtSpec.range = [35 45] for gamma band. % Specify the order of the FIR filter in filtSpec.order. A useful % rule of thumb can be to include about 4 to 5 cycles of the desired % signal. For example, filtSpec.order = 50 for eeg data sampled at % 500 Hz corresponds to 100 ms and contains ~4 cycles of gamma band % (40 Hz).)

基于人工神经网络的常用数字信号调制，DC-DC部分采用定功率单环控制，采用热核构造权重，采用的是脉冲对消法，欢迎大家下载学习，多目标跟踪的粒子滤波器。

FeatureSelection_matlab......FeatureSelection_matlab......FeatureSelection_matlab......FeatureSelection_matlab......FeatureSelection_matlab......FeatureSelection_matlab FeatureSelection_matlab......FeatureSelection_matlab......FeatureSelection_matlab......FeatureSelection_matlab...