MATLAB-VERSION2

说明：  一个JPA的学习资料。很适合初学者进行学习。对于初学者来说是个非常宝贵的资料。(A JPA of learning materials. Is suitable for beginners to learn. For beginners is a very valuable information.)

Electrical Load flow using Matlab

DESCRIPTION: This Matlab algorithm performs many tasks associated with power system analysis. First, it has the ability to perform both fully coupled and dc power flow analysis. Second, it has the ability to create measurements (with user-defined error) to be used as input for state estimation. Third, it has the ability to perform state estimation - allowing the user to examine how the placement of measurement devices as well as magnitude of distibution of measurement error affect the discrepency between power flow and state estimation results. In essence, it provides the user with a complete package for the study of both power flow and state estimation in n-bus power systems.

用c#编写了一套FIR窗口滤波器，其中数据段的提取来自于matlab的混合编程(Written in c# a FIR filter window, including data extracted from the mixture of matlab programming)

信号处理，振动方面的，很有用，绝不后悔，帮助很大(Signal processing, vibration issues, useful, never regret, very helpful)

psat 39节点数据 39节点数据 39节点数据 39节点数据 (psat 39)

常见的大脑信号处理算法的程序集合。用matlab语言编写的。可以作为研究信号可视方向的学生学习(Common signal processing algorithms of the brain collection procedures. Using matlab language. Research on visual signals can be used as the direction of student learning)

function ret = Int2Hex(var) Prints integer array to hexadecimal string varType = class(var) cast signness away: if ( u ~= varType(1) ) varType = [ u varType] var = typecast(var,varType) end nBits = str2double(varType(5:end)) if (64 == nBits) split 64 bit case into two 32 s cuz dec2hex doesn t handle 64 bit... varType(5:end) = 32 var = typecast(var,varType) end ret = dec2hex(var) if (64 == nBits) littleEndian = all(typecast(uint32(1), uint16 )==[1 0]) first = 1 + littleEndian second = 2 - littleEndian ret = [ret(first:2:end,:),ret(second:2:end,:)] end end

说明：  基于亮度直方图的纹理特征，包含均匀度、平滑度、一致性、熵等六个特征。(Brightness histogram-based texture features, including uniformity, smoothness, uniformity, entropy and other six characteristics.)

说明：  神经网络与matlab7的实现 配套资料下载 .m格式(neural network and the realization matlab7 supporting information downloaded. M format)