ncut_multiscale_1_5

assingment problem With this package, I provide some MATLAB-functions regarding the rectangular assignment problem. This problem appears for example in tracking applications, where one has M existing tracks and N new measurements. For each possible assignment, a cost or distance is computed. All cost values form a matrix, where the row index corresponds to the tracks and the column index corresponds to the measurements. The provided functions return an optimal or suboptimal assignment - in the sense of minimum overall costs - for the given matrix.

系统实时仿真开发环境与应用,主要讲解matlab RTW技术分上下两部分。(Real-time simulation development environment and application, mainly on matlab RTW technology was divided into two parts.)

一种基于matlab的位同步提取方法，通过微分，滤波提取信号(a new matlab system)

在飞机无源跟踪中，用扩展卡尔曼滤波算法来实现平面变角速度模型。(Passive tracking the aircraft, using extended Kalman filter algorithm to achieve the flat model of variable angular velocity.)

说明：  轴系设计计算-4 人字架结构尺寸的优化设计-1 曲线拟合和多项式拟合-2(Shaft design calculation-4 Renzi Jia Structural size optimization of the design curve fitting and polynomial fitting-1-2)

追赶法解数学方程，主要用于数值运算，希望对大家有帮助(Solving mathematical equations to catch up, mainly for numerical computation, we want to help)

这是数字信号处理的好资料，详细地介绍如何用MATLAB进行信号分析与处理，想学习的可以下载看看。(This is good information on digital signal processing, detailed information on how to use MATLAB for signal analysis and processing, want to study can be downloaded to see.)

preconjgrad：预处理共轭梯度法求线性方程组Ax=b的解 conjgrad：共轭梯度法求线性方程组Ax=b的解 twostep:两部迭代法求线性方程组Ax=b的解 fastdown:快速下降法求线性方程组Ax=b的解(preconjgrad: preconditioned conjugate gradient method for solving linear equations Ax = b solution conjgrad: conjugate gradient method for solving linear equations Ax = b solution twostep: Two iterative methods for solving linear equations Ax = b, Solution fastdown: rapid descent method for solving linear equations Ax = b solution)

simulink model of dc to dc boost converter. the important thing it is in closed loop using p i controller. it is for study purpose..

Recent work by Petricoin and Liotta and co-workers (Petricoin et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002 Feb 16 359(9306):572-7. PMID: 11867112) has generated a lot of excitement and controversy. This example shows some ways that MATLAB can be used to read, visualize, pre-process (base-line correction, resample) and classify the data. The data can be downloaded from http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp (Recent work by Petricoin and Liotta and co-workers (Petricoin et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002 Feb 16 359 (9306) :572-7. PMID: 11867112) has generated a lot of excitement and controversy. This example shows some ways that MATLAB can be used to read, visualize, pre-process (base-line correction, resample) and classify the data. The data can be downloaded from http://home.ccr.cancer. gov/ncifdaproteomics/ppatterns.asp)