搜索资源列表
Sinewave-plus-additive-zero-mean-random-noise
- 用来产生嵌入到随机噪声内的正弦波算法的C源程序。-used to produce embedded into random noise sine wave within the algorithm in C source code.
lms_communication
- 研究用于自适应均衡器的LMS算法的性能，数据源产生有零平均和单位方差的由符号+1和-1组成的Bernoulli的序列{I(n)}.数据源之后的信道可以用升余弦脉冲响应来模拟。 -study for the adaptive equalizer LMS algorithm performance, Data Sources have zero mean and unit variance from the -1 and a s
selfCorrelationInRandomization
- 产生零均值单位方差高斯白噪声的1000个样点,估计随机过程的自相关序列-units have zero mean Gaussian white noise variance to the 1,000-point, it is estimated that the random process autocorrelation sequence
matlabqpskwhitegaussemulator
- QPSK在加性高斯白噪声环境下，信噪比和误码率的仿真。 先产生一个四组符号的序列，再将该序列映射到对应的相位上。用相应的函数产生均值为零、方差为δ2的加性高斯噪声。 -QPSK the additive white Gaussian noise environment, and the signal-to-noise ratio of the simulation. First group have a four symbol
sources
- This program reads iid zero-mean unit-variance Gaussian variates
periodogramestimate
- Generate 100 samples of a zero-mean white noise sequence with variance , by using a uniform random number generator. a Compute the autocorrelation of for . b Compute the periodogram estimate and plot it. c Gener
BPSK_OVER_AWGN_CHANNEL
- Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean Gaussian random variables independently (separately) for each k MATLAB function random.
Kalman_Filter
- 卡尔曼滤波源代码 Some brief notes ---------------- kfdemo.m is a Matlab scr ipt file to run a demonstration of the Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4 so that it will read in either
Sinewave-plus-additive-zero-mean-random-noise
- 用来产生嵌入到随机噪声内的正弦波算法的C源程序。-used to produce embedded into random noise sine wave within the algorithm in C source code.
lms_communication
- 研究用于自适应均衡器的LMS算法的性能，数据源产生有零平均和单位方差的由符号+1和-1组成的Bernoulli的序列{I(n)}.数据源之后的信道可以用升余弦脉冲响应来模拟。 -study for the adaptive equalizer LMS algorithm performance, Data Sources have zero mean and unit variance from the-1 and a sy
selfCorrelationInRandomization
- 产生零均值单位方差高斯白噪声的1000个样点,估计随机过程的自相关序列-units have zero mean Gaussian white noise variance to the 1,000-point, it is estimated that the random process autocorrelation sequence
matlabqpskwhitegaussemulator
- QPSK在加性高斯白噪声环境下，信噪比和误码率的仿真。 先产生一个四组符号的序列，再将该序列映射到对应的相位上。用相应的函数产生均值为零、方差为δ2的加性高斯噪声。 -QPSK the additive white Gaussian noise environment, and the signal-to-noise ratio of the simulation. First group have a four symbol
sources
- This program reads iid zero-mean unit-variance Gaussian variates
AdaptiveLMS
- 用自适应数字滤波器进行谱线增强,噪声信号是均值为零,方差为1的高斯白噪声-Using adaptive digital filters for line enhancement, noise signal is zero mean, variance 1 Gaussian white noise
periodogramestimate
- Generate 100 samples of a zero-mean white noise sequence with variance , by using a uniform random number generator. a Compute the autocorrelation of for . b Compute the periodogram estimate and plot it. c Gener
BPSK_OVER_AWGN_CHANNEL
- Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean Gaussian random variables independently (separately) for each k MATLAB function random.
Kalman_Filter
- 卡尔曼滤波源代码 Some brief notes ---------------- kfdemo.m is a Matlab scr ipt file to run a demonstration of the Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4 so that it will read in either
chaonim
- 仿真函数 （1），函数形式为： 其中， 是零均值单位方差的高斯噪声。输入 服从[0，1] 均匀分布，且相互独立。样本长度为1000组，将其分为三组，自由选择200组作为模型训练数据，自由选择100组作为检验数据，其余的作为长期检验数据。 （2） 其中x1~x4相互独立， ， 为白噪声 -Simulation function (1), function of the form: which is th
otsu
- OTSU Gray-level image segmentation using Otsu s method. Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection Method fro
zncc
- ZNCC Normalized cross correlation m = zncc(w1, w2) Compute the zero-mean normalized cross-correlation between the two equally sized image patches w1 and w2. Result is in the range -1 to 1, with 1 indicatin