HilbertTransformer

object removal在图像处理中的意思是用户可以随意擦去图像中的一个物体，算法可以自动的用背景中的信息补去空洞，达到看不出来是处理过的效果，又叫inpainting，该程序由matlab写成，效果非常好，文档、演示、测试图像均齐全-(object removal in image processing means that users can remove images of an object, the algorithm can automatically use the background information to fill empty, reaching out is not to look over the effectiveness of treatment, also known as inpainting, the program by matlab languages, has very good results, documents, presentations, test images have complete )

提出了基于损失分布的操作风险相关性的计算方法.(Proposed operational risk loss distribution based on the correlation calculation.)

matlab 实现的小波变换 希望对大家有用(wavelet transform)

matlab中指数拟合的源程序 很好 很有用(the matlab in exponential fitting good source useful)

改进的积分方程模型，微波遥感领域，用于求土壤的后向散射系数等；该模型为双站散射。(Improved integral equation model, the field of microwave remote sensing, and soil after backscattering coefficient model for bistatic scattering.)

用matlab编写函数实现4qc运算，已经通过测试(Write a function with matlab implementation 4qc operations have been tested)

Vector control for induction machine

APFC电路，利用市电220V,将Boost电路的功率因数提高，仿真了电流电压波形(APFC circuit)

ieee paper on sensor routing

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)