Band-QPSK-ber-simulation

BER Vs SNR performance of a signal passing through Rayleigh Fading Chanel

蜂群算法求解电力系统问题,很方便，很有效，快速求解。(the ABC are used to solve power system)

卡尔曼滤波算法的MATLAB源码程序，及其应用(Kalman filtering algorithm MATLAB source programs, and its application)

这是一个基于空时分组编码的MIMO_OFDM通信系统的仿真设计。此系统包括QPSK调制解调解调，IFFT调制，空时编解码，基于训练符号的信道估计等通信模块。 (This is a space-time block coding based simulation design MIMO_OFDM communication systems. This system includes a QPSK modulation and demodulation demodulation, IFFT modulation, space-time coding and decoding, channel estimation based training symbol communication module.)

模型预测控制实例，包含一段MPC控制的程序和仿真数据(model predict control)

说明：  均匀线阵上实现DOA估计，ESPRIT算法，16阵元（可调），半波长阵列流形。(DOA estimation on a uniform linear array, ESPRIT algorithm, 16 array elements (adjustable), half-wavelength array manifold.)

使用IS-95 FIR滤波器从白噪声中滤得低通噪声,使用matlab2007b编写(Using the IS-95 FIR filter obtained from the white noise in the low-pass noise filter)

KL变换的程序,KL transform procedure,matlab源码(KL transform procedure using Matlab)

给出了几种求Lyapunov指数的matlab编程方法(Given several requirements Lyapunov exponent matlab programming method)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )