sift

说明：  802.16a协议物理层的matlab实现(802.16a physical layer to achieve the Matlab)

In this file, we have to find how the images are imported and how it will be import and to extract the frames. Next to add the gray scale representation and color images. Finally to detect the RGB color image.

大学数值分析算法，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，预报误差法参数辨识-松弛的思想。( University of numerical analysis algorithms, PV modules contain, MPPT module, BOOST module, inverter module, Prediction Error Method for Parameter Identification- the idea of relaxation.)

说明：  精通MATLAB优化计算算法，无约束多维极值问题。共11个程序。(Proficient in MATLAB optimization calculation algorithm, unconstrained multi-dimensional extremum problem. A total of 11 procedures.)

Vowel filter for matlab

Breiman的bagging算法，是bootstrap aggregating的缩写，是最早的Ensemble算法之一，它也是最直接容易实现，又有着另人惊讶的好效果的算法之一。(Breiman’s bagging, short for bootstrap aggregating, is one of the earliest ensemble based algorithms.)

本模块对车用动力锂电池的三种主要等效电路模型进行建模和仿真，搭建了相应的仿真模块。(In this module, three main equivalent circuit models of vehicle power lithium battery are modeled and simulated, and the corresponding simulation module is built.)

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

说明：  使用MATLAB对MIMO系统进行辨识，其中有各种算法仿真算例，N4SID，针对多变量系统的一种识别算法(MOESP (Multivariable Output-Error State sPace) is a deterministic method to identify linear time invariant systems. The MOESP method developed by M. Verhaegen and P. Dewilde is based on the LQ decomposition of Hankel matrix formed from input-output data, where L is lower triangular and Q is orthogonal. A Singular Value Decomposition (SVD) can be performed on a block from the L (L22) matrix to obtain the system order and the extended observability matrix. From this matrix it is possible to estimate the matrices C and A of state-space model. The final step is to solve overdetermined linear equations using the least-squares method to calculate matrices B and D.)

用matlab做的有限元编程的源代码，子例程丰富(Using matlab to do finite element programming source code, extensive subroutine)