markowitz

这是一个相对完整的OFDM通信系统的仿真设计，包括编码，调制，IFFT，上下变频，高斯信道建模，FFT，PAPR抑制，各种同步，解调和解码等模块，并统括系统性能的仿真验证了系统设计的可靠性。 (This is a relatively complete simulation of OFDM communication system design, including coding, modulation, IFFT, the upper and lower frequency, Gaussian channel model, FFT, PAPR suppression, a variety of synchronization, demodulation and decoding module, and system performance omnibus simulation of the system reliability.)

自适应滤波器的最陡下降法示意图(adaptive filter steep decline in the most schematic)

MVDR VS MUSIC algorithm

说明：  通过对图像的二值化以及矩阵计算 识别汽车的车牌(Through the binary image and the vehicle license plate recognition Matrix)

HHT的MATLAB程序实现，对每个语句都有注释和说明。(HHT of the MATLAB program for each statement has notes and instructions.)

header file foe edf file

This file includes QPSK mapping function.

包括自适应控制中的MIT以及李雅普诺夫控制方法的简单编程示例(Simple programming examples include adaptive control of MIT and Lyapunov control method)

encoder of speech compression

说明：  EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。 (EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.)