buck-converter

Matlab OFDM MIMO antennas Rayleigh channel

改进的等维新息灰色模型，还有残差数列检验，后验差检验，模型精确好用。(Improved Innovation Gray Model，There are residual series test, posterior difference test, model accuracy easy to use. )

arima时间序列模型，对一个序列进行也测(arima time series models, also measured on a sequence)

Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute pointto- point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches.

说明：  用于计算目标回波信号，散射中心提取，可应用于各种目标(It can be used to calculate target echo signal and extract scattering center. It can be applied to various targets.)

说明：  求x的算术平方根，保留N位有效数字，并返回算术平方根的平方(solving the square root of x with N digits Significant figures, and saving the Square root of the square)

Shows single- and two-stage-decomposition of a sample image, as well as reconstructed branches. The reconstruction error is shown. Also demonstrates that better compression can often be obtained if different thresholds are allowed for different subbands.

用于对非线性方程的参数估计和最优估计的粒子滤波算法，保管一看就会，步骤很清晰的。(the solution of non-line function by pf filter)

SMOOTH uses a vectorized implementation that may be slow when X and/or FRAME_LENGTH are very large. The number of elements that are used for calculation is length(X)*FRAME_LENGTH. The algorithm vectorizes the operation by creating a matrix of indexes and extracting its diagonals. E.g. for a vector of length 4 and frame_length of 2, the algorithm creates a temporary zero-padded matrix x2 from which it creates a set of indexes:

说明：  按FPE定阶的 源程序：fpe.cpp M序列：M序列.txt 白噪声：Gauss.txt 程序中先用依模型阶次递推算法估计模型的参数，再用fpe方法判断模型的阶次。 程序运行结果如下： n: 1 判断阶次FPE的值: 0.0096406 -0.481665 1.07868 n: 2 判断阶次FPE的值: 0.00875755 -0.446739 0.00498181 1.07791 0.0527289 n: 3 判断阶次FPE的值: 0.0087098 -0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982 n: 4 判断阶次FPE的值: 0.000396884 -0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236 n: 5 判断阶次FPE的值: 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771 n: 6 判断阶次FPE的值: 3.23349e-007 -1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688 从以上结果可以看出，当n=5时，fpe值最小，所以这时的模型阶次和参数估计值为最优结果： 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)