floyd

Additive delay Linear matrix inequality simmulation

生成白噪声的程序，基于MATLAB，可用于地震工程和机械等方面的信号处理(white noise)

详尽的LPC编码解码算法，基于matlab，适合学习LPC算法。(Detailed LPC codec algorithm, based on matlab, LPC algorithm for learning.)

skin-tone 图片处理 将RGB转换为Lab，然后再Lab格式下检测人肤色，处理后转换回RGB输出(skin-tone image processing)

2-D FDTD TE code with PML absorbing boundary conditions Program author: Susan C. Hagness

source codes--bluetooth,fsk,wcdma ,rake receiver

说明：  4种光束动画,FLASH素材,FLASH源码,FLA格式(Four kinds of beam animation, FLASH material, FLASH source, FLA format)

说明：  关于用MATLAB设计对信号进行频谱分析和滤波处理的程序。(With regard to design using MATLAB signal processing spectral analysis and filtering procedures.)

Edge detection result should be enhanced using linear method like Median filter to remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the centre of the pupil by counting the number of black pixels (zero value) of each column and row. Then get each row and column that has the maximum number of these black pixels. Then determine the center by simple calculation according to the image coordinate to set it correct on the image, consequently we can determine the radius of the pupil. Thus we can find the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms of circle and ellipse.

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.)