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matlab初学者的使用教程，可处理各种模型求解问题(matlab tutorial for beginners to use, can handle a variety of models to solve problems)

在matlab中按字符读取txt文件中的十六进制数据，并把它转换为十进制。(In matlab read txt file by character hexadecimal data and convert it to a decimal.)

原来先进PID控制MATLAB中的大林算法例子在高版本的MATLAB中运行有较多错误，这是我经过修改，在MATLAB7.11.0中调试成功的代码。(The original advanced PID control the Daelim algorithm in MATLAB example high version of MATLAB run more error been modified, code debugging success in MATLAB7.11.0.)

EMD处理数据的全流程以及经验模态分解的一个经典例子(The whole process of EMD data processing and a classical example of empirical mode decomposition)

TD-SCDMA小区搜索最为关键的sync-dl码同步的确定过程，即搜索DwPTS(TD-SCDMA cell search the most critical sync-dl Code Synchronization in the process of determining that the search DwPTS)

QPSK 在高斯信道下的调制解调的matlab仿真实现，可显示输入数据的波形表示，I支路和Q支路以及调制前、解调后、接收的波形、星座图等(In the Gaussian channel QPSK modulation and demodulation matlab Simulation, the waveform can be displayed, said input data, I branch and Q branch, and before modulation, demodulation, the received waveform, constellation, etc.)

contains OFDM technique and AWGN channel acts as noise

图象处理：计算连通域的面积、周长、圆形度、矩形度以及长宽比(Image processing: computing connected domain area, perimeter, roundness, and aspect ratio rectangular degree)

使用matlab软件搭建出来的光伏电池放电模型，波形很理想(Using matlab software to build out solar battery discharge model is ideal waveform)

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )