bihuantiaosu

Alamouti and Maximal-Ratio Combining comparison

利用Matlab仿真软件，完成如图1所示的一个基本的数字通信系统。信号源产生0、1等概分布的随机信号，映射到16QAM的星座图上，同时一路信号已经被分成了实部和虚部，后边的处理建立在这两路信号的基础上。实部、虚部信号分别经过平方根升余弦滤波器，再加入高斯白噪声，然后通过匹配滤波器（平方根升余弦滤波器）。最后经过采样，判决，得到0、1信号，同原信号进行比较，给出16QAM数字系统的误码。(Using Matlab simulation software, as shown in Figure 1 to complete a basic digital communications systems. Signal source such as arise over the distribution of 0,1 random signal, is mapped to 16QAM constellation diagram, at the same time all the way the signal has been divided into real and imaginary parts, behind the deal with the establishment of these two signals in the basis. Real part, imaginary part of the signal, respectively, after a square root raised cosine filter, then add white Gaussian noise, and then through the matched filter (square root raised cosine filter). Finally, after sampling, the verdict has been 0,1 signal, to compare with the original signal is given 16QAM digital system error.)

说明：  小脑模型的非线性模型识别小脑模型的非线性模型识别(CMAC Identification for nonlinear model)

采样率转换程序，程序主体分为上采样与下采样(Sample rate conversion process, the program is divided into the main sampling and down-sampling)

图像分割的图割方法代码，matlab编程(image segmentation by graphcut based on matlab)

Remove Extra Spaces (Sometimes text values contain leading, trailing, or multiple embedded space characters, this function removes spaces from text except for single spaces between words)

电池模型，锂离子电池参数估计模型，matlab源代码(battery 1 RC model using in matlab soft)

说明：  基于chebyshev的水声信号分析算法，包括多种分析结果(Chebyshev acoustic-based signal analysis algorithms, including a variety of analytical results)

This is a matlab based codings to identify the related images. from a data base. using chuns algorithm

MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)