xinhaochuli123

学习matlab的一本非常不错的电子文档 也是从别的网站下载的 希望对大家会有帮助

matlab code for LMI.

比特加载技术相关算法，包含Fischer算法，贪婪算法以及chow算法。(bit loading algorithm, include Hughes-Hartogs Algorithm, Fischer Algorithm and chow algorithm)

《Matlab时频分析技术及其应用》电子书。()

通过这个程序可以设计一个IIR滤波器，对滤波器的系数按4bit量化。(Through this program can design an IIR filter)

All-pole modeling using the Burg algorithm

Digital communication QAM64 modulation type

吉布斯分布代码，常用于马尔科夫随机场分割和识别(Gibbs distribution code, commonly used for Markov Random Field segmentation and recognition)

接收端采用的算法和程序流程与发送端发送的OFDM符号的帧结构有关系。具体的帧结构，以及定时估计，频偏估计，剩余误差跟踪的算法可参考算法说明文档。这里对程序的流程进行说明。 首先根据短训练字的特性进行相关运算，进行信号到达检测，当检测到相关值大于门限一定次数后，认为有信号到达。然后根据长训练字的特性，进行相关运算，进行OFDM符号FFT窗口起始位置的估计。估计出FFT窗口的位置后，先在时域进行小频偏的估计，将两个长训练字进行小频偏补偿后，进行FFT运算，根据FFT运算的结果进行整数倍频偏的估计。这些参数估计完成后，就可以进行数据解调了。先对数据部分进行完整的频偏补偿，然后根据估计的FFT窗口位置进行FFT运算得到频域的数据，进行解调。然后在对应于导频的子载波位置上提取出导频信息，根据导频信息估计出剩余定时误差以及剩余的信道响应误差，将误差量送入环路进行跟踪。当收到所有数据后，重新回到信号到达检测状态，进行下一次信号到达的检测和信号接收。 (The receiving end uses the algorithm and the program flow and the frame structure of the OFDM symbol transmitted by the sending end. Specific frame structure, and timing estimation, frequency offset estimation, residual error tracking algorithm can refer to the documentation. Here to illustrate the process of the program. First, according to the characteristics of the short training word correlation operation, the signal reaches the detection, when the correlation value is greater than the threshold of a certain number of times, that there is a signal to reach. Then, according to the characteristics of the long training word, the correlation operation is carried out, and the initial position of the OFDM symbol FFT is estimated. The estimated position of the FFT window, first in the time domain for small frequency offset estimation, two long training word for small frequency offset compensation, the FFT operation. According to the result of FFT for integer frequency bias estimation. Af)

说明：  核极限学习机是一种单隐层前馈神经网络(the single-hidden layer feedforward neural networks,SLFNs),其只需要设置隐藏层节点数,然后采用最小二乘法计算出权值即可。因此,核极限学习机在学习速度和泛化能力上具有很大优势。(Kernel limit learning machine is a single hidden layer feedforward neural networks (slfns). It only needs to set the number of hidden layer nodes, and then use the least square method to calculate the weight. Therefore, the kernel limited learning machine has a great advantage in learning speed and generalization ability.)