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It is the code related to jpeg image Denise. with innovation

已知自变量t，因变量y，y和t满足一下函数式： dy/dt=0.001414/(1/(4.5727*10^4*k1*(1-0.02119*y)^k2)+1/k3/45727)；（需要指出的是，该公式中参数可进一步合并，在本帖中并未作合并处理） 已知 t=；y=；拟合出参数k1，k2，k3。(Known independent variable t, the dependent variable y, y, and t satisfy my functional: dy/dt = 0.001414/(1/(4.5727* 10 ^ 4* k1* (1-0.02119* y) ^ k2)+1/k3/45727) (It should be noted that the equation parameters can be further consolidated in this post are not treated as a merger) is known t = y = fitting parameters k1, k2, k3.)

用于对股市、金融数据波动趋势的判断与分析。(For the stock market, financial data, and analysis to determine the fluctuation trends.)

Segmentation by growing a region from seed point using intensity mean measure.Simple but effective example of "Region Growing" from a single seed point.

物理层网络编码物理层网络编码物理层网络编码物理层网络编码物理层网络编码(Physical layer network codingPhysical layer network codingPhysical layer network codingPhysical layer network codingPhysical layer network coding)

最大熵分割，成功调试过，可以完全实现最大熵分割，效果完美(Maximum Entropy Segmentation, successfully debugged Maximum Entropy Segmentation can be fully realized, the effect is perfect)

交错反激电路matlab，mppt，光伏微逆变器仿真源码。(Interleaved flyback circuit matlab, mppt, photovoltaic micro inverters source of emulation.)

This page contains software and instructions for factorized graph matching (FGM)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

实现 harris角点提取，精度达到亚像素级，是基于matlab编程的。运行结果良好 。(For harris corner extraction, precision sub-pixel level, is based on matlab programming. Good operating results.)