wwavelet

此程序是一个动态的非线性的滤波器程序，对编程人员的能力要求比较高(This program is a dynamic non-linear filter program, the programmer the ability to require a relatively high)

图像处理Matlab源代码，包括二维离散余弦变换的图像压缩，采用灰度变换的方法增强图像的对比度，直方图均匀化，模拟图像受高斯白噪声和椒盐噪声的影响。 (Including discrete cosine transform image compression, the use of gray-scale transformation method to enhance image contrast, histogram equalization, analog image by Gauss white noise and salt and pepper noise.)

用最大最小距离实现聚类的matlab函数(With maximum and minimum distance to achieve clustering matlab function)

MATLAB实现ELM算法，并附带UCI经典数据。(MATLAB realization ELM algorithm, along with the classic UCI data.)

根据卫星星历及测者位置，判断卫星是否可见(satellite ephemeris and location were measured to determine whether the satellite can be seen)

生成AR(p)过程模拟的参数。满足过程的弱稳定。模拟多条样本路径，利用AIC/BIC/IC进行定阶。比较三种标准的准确性。(Generate an AR (p) process simulation parameters. Meet process weakly stable. Simulation sample path, AIC/BIC/IC order determination. Three standard accuracy.)

性指标的价值函数达到最小。FCM与HCM的主要区别在于FCM用模糊划分，使得每个给定数据点用值在0，1间的隶属度来确定其属于各个组的程度。与(indicators of the value of minimizing function. HCM FCM with the main difference is fuzzy FCM, broken down to make each set of data points with the value 0, one of the membership to determine its belong to the group level. And)

个人收集的一些关于小波分析的matlab程序，里面有一些例子~(Personal collection on wavelet analysis matlab program, there are some examples to)

重要参数的提取，是本科毕设的题目，可以实现模式识别领域的数据的分类及回归，进行逐步线性回归，构成不同频率的调制信号，正确率可以达到98％，在MATLAB中求图像纹理特征。( Extract important parameters, The title of the commercial is undergraduate course you You can achieve data classification and regression pattern recognition, Stepwise linear regression, Constituting the modulated signals of different frequencies, Accuracy can reach 98 , In the MATLAB image texture feature.)

：提出一种基于独立成分分析（ICA）和位隙动态分组技术的超高频（UHF）射频识别（RFID）多标签防碰撞算法。利用ICA 算法实现多目标的同时识别，利用动态位隙标签分组保证阅读器同时读取的标签数少于或等 于其天线数，从而使ICA 工作于非欠定状态(A novel ultra high frequency (UHF) radio frequency identification（RFID）anti-collision algorithm based on independent components analysis (ICA) and dynamic bit-slot grouping technology was proposed. The ICA algorithm was used for identifying several tags simultaneously. The dynamic bit-slot tags grouping technology was used for ensuring the number of tags read by a reader at the same time was less than or equal to its antennas’ number, and enabling the ICA wouldn’t work in an underdetermined state. Theoretical analysis and simulated experimental results show that the tags identification rate of the proposed algorithm is far more than that of traditional stochastic or deterministic TDMA RFID anti-collision algorithms.)