atmosisa

haar wavelet source code implemented by matlab

一种改进的方法，自适应的高斯chirplet分解信号，文章很深入的讲解了退到过程。(a fast refinement for adaptive gussian chirplet decoposition)

程序可以实现所有的汉明码的要求， （7，4）（15，11）等等都可以的

现代控制理论中权威观测器的实现 matlab中用于UI6008数据采集卡的数据采集以及输出的程序 使用方便快捷(Modern control theory in the implementation of the authoritative observer matlab for UI6008 data acquisition card for data acquisition and output procedures using the convenient)

leach算法和leach改进的算法对比默契改进算法为对阈值的改变(leach algorithm and improved algorithm leach algorithm for Bi Moqi threshold for change)

常用matlab语音编程流程（语音信号的分帧加窗的matlab实现），学习语音信号处理的人必须要知道这些基本的编程方法。(Common Voice matlab programming process (sub-frame windowed speech signal matlab implementation), learning of speech signal processing must be aware of these basic programming methods.)

ISODD Determines which elements of an array are odd numbers

本程序为利用给定节点，求未知节点插值法，包括拉格朗日插值法和牛顿向前插值法(This procedure for the use of a given node, seeking the unknown node interpolation method, including the Lagrange interpolation method and Newton' s forward interpolation)

基于 DCT(离散余弦变换 )域的信息隐藏算法是目前应用最广的变换域算法 ,此类算法的最大不足是嵌信息量小。通过分析 DCT域隐藏算法隐藏容量少的原因 ,提出了一种大容量的 DCT域信息隐藏算法。算法首先对载体图像进行频谱均匀化处理 ,然后对图像进行全局 DCT变换 ,并将隐藏信息嵌入到载体图像的低频 DCT系数上 ,信息嵌入采用分段量化的方法实现 ,提取隐藏信息时不需要原始载体图像。实验结果表明 ,该算法的嵌入容量大 ,不可感知性好。(Based on DCT (discrete cosine transform) domain information hiding algorithm is the most widely used transform domain algorithms, the largest of these algorithms is the lack of a small amount of information embedded. Hiding Algorithm for DCT domain by analyzing the hidden reasons for the low capacity, a large capacity of information hiding algorithm for DCT domain. Firstly, the spectrum of the carrier image homogenization, and then the image is a global DCT transformation, and hidden information embedded into the image of the low-frequency DCT coefficients, the information embedded in the methods used to achieve sub-quantization, extract hidden information without the original Vector image. Experimental results show that the algorithm embedding capacity, can not be perceived good.)

这篇文章是盲信号处理，因为是英文文章，所以大家看内容就好了。(Principal component analysis (PCA) is a mainstay of modern data analysis- a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works furthermore, it crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.)