haffuman

SLNR下，信噪比和误码率的关系,涉及了两域联合(SLNR, the relationship between SNR and bit error rate, involves a two-domain joint )

irst, let s create a simple vector with 9 elements called a.

speech signals in gsm

说明：  对一副测试图像进行加各种噪声，然后进行多帧平均，中值滤波，均值滤波，维纳滤波，理想低通滤波，巴特沃斯低通，带通滤波，自适应滤波九种方法处理，并分析结果，得出各种滤波方法的优缺点。包括实验报告(Plus a pair of test images of all kinds of noise, and then multi-frame averaging, median filtering, mean filtering, Wiener filter, the ideal low-pass filter, Butterworth low-pass, band pass filtering, adaptive filtering nine kinds of approach and analyze the results, obtained the advantages and disadvantages of various filtering methods. Including the lab report)

PID控制算法和遗传算法混合编程，算出最优解，智能控制部分(PID control algorithm and genetic algorithm hybrid programming, calculate the optimum solution, part of intelligent control)

induction motor simulation in matlab

在matlab中建立APF的仿真模型，证明其工作原理的可行性(APF established in matlab simulation model to prove the feasibility of its working principle)

说明：  基于模糊PID控制迟延系统具有更好的调节时间以及更小的超调量(The delay system based on fuzzy PID control has better adjustment time and smaller overshoot.)

天线阵的波束形成Matlab代码（圆阵）(antenna beamforming Matlab code (UCAs))

用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。(Use Parzen Windows method or the kn neighbor method estimated probability density function, draw the bayes classifier, to test sample to test, and the parameter estimation is based on the classifier and get the difference of classification performance. 2. At the same time the height and weight data as the characteristic, with Fisher identification method for linear classifier, and the application of the classifier to training and test sample, investigation and test training error condition. Will the training sample and utility of decision on the drawing boundary paint, and at the same time before the Bayes method with optimal classifier also paint chart, the result of the comparison of the similarities and differences. 3. Select the above or before any a kind of method of experiment, with a method for training set in the estimation error rates, and on the test set get error rates are compared.)