iteratedBackProjection

利用龙格库塔法解光波导激光器的重叠因子的传输方程，从而得到输出功率(Runge-Kutta method using optical waveguide laser solutions overlap factor of the transfer equation, thus the output power)

FixedLoad simulation in matlab simulink

2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。 1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。 2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。 (Computer Experiments: 1. Consider an AR process x(n) defined by the difference equation where v(n) is an additive white noise of zero mean and variance .The AR parameters and are both real valued: a) Calculate the noise variance such that the AR process x(n) has unit variance .Hence , generate different realization of the process x (n). b) Given the input x (n), an LMS filter of length M = 2 is used to estimate the unknown AR parameters and . The step size is assigned the value 0.05. Compute and plot the ensemble average curve of and by averaging the value of parameters and over an ensemble of 100 different realization of the filter. Calculate the time constant according to the experiment results and compare with the corresponded theoretical value. c) For one realization of the LMS filter, compute the prediction error And the two tap-weight errors and Using power spectral plots of , show that)

Raghuraj 与 Lakshminarayanan提出了基于特征值内相互关系建立预测模型,用于识别的模式识别方法——多变量预测识别模型（Variable Predictive Mode Based Class Discriminate, VPMCD）,并应用于生物学的模式识别中。VPMCD利用参数估计来建立预测模型，极大地减少了计算量，缩短了模型的训练时间。vpmcd5_revise为主函数，sec2_5_revise为训练函数(Variable Predictive Mode Based Class Discriminate, VPMCD)

ofdm的matlab实现，包括bpsk fft(ofdm of matlab realization ,inlude bpsk fft)

ex9－8 相关对数正态分布杂波 ex9_8 相关weibull分布杂波 ex9_9 相干相关K分布杂波 (ex9-8 related to the lognormal distribution weibull distribution clutter clutter ex9_8 related ex9_9 coherent K distributed clutter-related)

BIT PATTERN ANALYSIS OF COMMUNICATION SIGNALS

this help u lots of imn mat labcoding bult lasa much mor coding in networkis

Design of a Control in cascada

In many applications, such as biomedical engineering, it is often required to obtain specific periodic source signals. In this paper, we propose a two-stage based approach for extracting periodic signals. At the first stage, the autocorrelation property of the desired source signal is exploited to roughly extract the desired source signal. At the second stage, the extracted signal is further processed as cleanly as possible, based on the higher-order statistics. Simulations on artificially generated data and real-world ECG data have showed its better performance, compared with many existing extraction algorithms.