DISSERTATION

该演示中包含的代码演示如何氡相似的功能，可以用来提高（以及部分）在Connectome电磁图像单元格边界。 请举出下列文件如果您发现此代码有用： Ritwik库马尔，阿梅里奥五雷纳和Hanspeter Pfister说“氡样的特点及其应用Connectomics”，接受，IEEE计算机学会研讨会在生物医学图像分析（MMBIA）2010年数学方法 http://seas.harvard.edu/〜 rkkumar radonLikeFeaturesDemo.m：演示边缘增强用氡相似的功能， getEdgeFeatures.m：助手为高斯函数的二阶导数滤波器 makeBarFilters.m：助手为高斯函数的二阶导数滤波器 sample.png：示例图像在此演示使用(The demo included in the code demonstrates how Radon-Like Features can be used to enhance (and segment) cell boundaries in Connectome EM images. Please cite the following paper if you find this code useful: Ritwik Kumar, Amelio V. Reina & Hanspeter Pfister, “Radon-Like Features and their Application to Connectomics”, accepted, IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) 2010 http://seas.harvard.edu/~rkkumar radonLikeFeaturesDemo.m : Demonstrates edge enhancement using Radon-Like Features getEdgeFeatures.m : Helper function for Gaussian Second Derivative Filters makeBarFilters.m : Helper function for Gaussian Second Derivative Filters sample.png : Sample image for use in this demo )

利用logistic混沌搜索方法对传统粒子群进行该进，并用于多目标决策(The use of logistic chaotic search method for carrying out the traditional particle swarm into, and used for multi-objective decision making)

比较最佳平方逼近二次多项式，Chebyshev截断级数法以及插值余项极小法这三种函数逼近方法，并给出图形(Comparison of the best square quadratic polynomial approximation, Chebyshev truncated series method as well as more than a very small interpolation method of the three function approximation methods, and gives graphical)

VLSi projet....these were recent papers published in ieee

Program for solving Laplace Equation

This is a simulink program for designing Low pass and High pass ACTIVE Filters circuit.

利用灰色模型进行电力负荷预测，输出预测数值及其与实测值的误差曲线(power load forecasting by Grey model )

model LPV the DFIG in matlab

说明：  matlab 防真自适应滤波器（部分）考虑时变代价函数 (Matlab defense really adaptive filter (part) when considering variable cost function)

说明：  一个有关核PCA的程序 主要用于模式识别和正交分解中(A nuclear PCA process is mainly used for pattern recognition and orthogonal decomposition)