SSB

link matlab to visual studio

these packages is for generating random data

This package is a free collection of Matlab routines for computing wave atom transforms in one, two and three dimensions.

利用主成分分析方法实现分类的Matlab源码分析(Principal component analysis Matlab source code analysis)

产生L＝31的GOLD序列，它由两个移位寄存器输出的模2相加而产生(have L = 31 GOLD sequence, which consists of two shift register output mode and have a combined total of 2)

This is a text book in Digital communication which is very good in understaing

这段代码主要是实现二阶理想高通滤波器，本段代码全部用MATLAB语言实现的。(This code is to achieve the ideal second-order high-pass filter, the above code to achieve the full use of the MATLAB language.)

基于RSSI的测距定位算法，可以直接自己写demo然后进行测试，十分的方便(RSSI-based distance positioning algorithm can write your own demo and then test, very convenient)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

3gpp release5中对TDD模式的详细描述(3gpp release5 TDD mode to a detailed description of)