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本书讨论了时间域、频率域、数字域、调制域电子测量的主要方法，介绍了应用MATLAB仿真的方法实现电子测量中的信号源(含VCO和PLL)、示波器、频谱仪、瀑布图仪、相位仪、眼图仪、逻辑分析仪、时域反射仪、取样示波器、矢量示波器、矢量网络分析仪等测量仪器，并简要介绍了各仪器的工作原理、参数设置要点和注意事项。书中还给出了大量的仿真实例，对一些典型例子进行了分析和讨论。(This book discussion has time domain, and frequency domain, and digital domain, and modulation domain electronic measurement of main method, introduced has application MATLAB simulation of method achieved electronic measurement in the of signal source (containing VCO and PLL), and oscilloscope, and spectrum instrument, and falls figure instrument, and phase instrument, and eye figure instrument, and logic analysis instrument, and Shi domain reflection instrument, and sampling oscilloscope, and vector oscilloscope, and vector Network Analyzer, measurement instrument, and briefly introduced has the instrument of work principle, and parameter set points and note matters. A lot of simulation results are also given in the book, conducted an )

卡尔曼滤波的MATLAB程序，用来解决相关的函数的问题的(Kalman Filter MATLAB procedures, to solve the problems related to the function of)

Rigol示波器保存的图片处理程序，将背景转换为白色，将两通道数据合并(figure process for RIGOL osc)

用matlab去实现基于dijkstra算法实现最小生成树(Using matlab to dijkstra algorithm based on minimum spanning tree)

说明：  matlab发送端程序,256位FFT,16QAM-OFDM，最后有时域和频域图形显示(matlab sender program, 256 FFT, 16QAM-OFDM, the last times and frequency domain graphics)

说明：  二维图像加噪声，通过小波变换分解，压缩，实现图像重构(Two-dimensional image plus noise, through the Wavelet decomposition, compression, Image reconstruction)

Parzen窗法，是Parzen的窗口算法，用MATLAB语言编写的，用于随机概率的估计(Parzen window is Parzen window algorithm, using MATLAB language, probability estimates for random)

请点击1.m运行文件（运行环境为matlab2008a） 注：不可直接运行1.fig 需使用1.m文件 附：输入各设备及物流量子窗口参数信息说明：请依次输入该零件所需的设备号，两个y设备号之间填入这两个设备间的物流量，例如一个零件需要的设备一次为3，5，7，其中3和5的物流量为10，5和7的物流量为20，则应按如下的格式输入3，10，5，20，7。只需输入数字即可，无需输入标点符号。且零件的排序为最下面的一行为零件1，向上依次递加。">

matlab 编程的函数库，便宜编程时查找(matlab programming library, look for cheaper programming)

包括最后计算压缩图像的峰值信噪比和压缩效果的源码,利用自然梯度算法，基于K均值的PSO聚类算法。( Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Use of natural gradient algorithm, K-means clustering algorithm based on the PSO.)