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利用matlab实现各种方程如线性微分方程的模拟。具有一定的算法(Using matlab to achieve a variety of equations such as the linear differential equation simulation. Algorithm has certain)

PCDMIS电极检测方案,可以自动搜寻基准12个点的位置，并计算出电极坐标(PCDMIS electrode detection scheme that can automatically search for the location of the base 12 points and calculate the coordinates of the electrodes)

自己用Matlab写的FFT的代码，主要是为了FPGA实现的前期建模(FFT using Matlab to write their own code, mainly to preliminary modeling of FPGA implementation)

局部均值分解是由Smith提出的一种新的非线性和非平稳信号分析方法。由于LMD是依据信号本身的信息进行自适应分解的，产生的PF分量具有真实的物理意义，由此得到的时频分布能够清晰准确地反映出信号能量在空间各尺度上的分布规律。(Local mean decomposition is a new nonlinear and non-stationary signal analysis method proposed by the Smith. Since LMD information is based adaptive signal itself decomposed, resulting PF component has real physical meaning, when the resultant frequency distribution of the signal can be clearly and accurately reflect the spatial distribution of the energy of each scale.)

power factor improvement

说明：  经典MUSIC在均匀线阵的情况。本例给出了MUSIC算法估计出的算法图，从图中可以直接看到估计的角度。我们的仿真实验用的是8个阵列，2个接受信号。MUSIC算法的精度很高。(MUSIC classic in the case of uniform linear array. In this case gives the MUSIC algorithm to estimate the algorithm maps directly from the chart to see the estimated angle. Our simulation experiments using 8 arrays, two acceptable signals. MUSIC algorithm for high precision.)

1 SIFT 发展历程 SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 3 SIFT算法的主要特点： a) SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。 b) 独特性(Distinctiveness)好，信息量丰富，适用于在海量特征数据库中进行快速、准确的匹配[23]。 c) 多量性，即使少数的几个物体也可以产生大量SIFT特征向量。 d) 高速性，经优化的SIFT匹配算法甚至可以达到实时的要求。 e) 可扩展性，可以很方便的与其他形式的特征向量进行联合。 4 SIFT算法步骤： 1） 检测尺度空间极值点 2） 精确定位极值点 3） 为每个关键点指定方向参数 4） 关键点描述子的生成 本包内容为sift算法matlab源码(1 SIFT course of development SIFT algorithm by DGLowe in 1999, the perfect summary of 2004. Later Y.Ke its description of the sub-part of the histogram with PCA instead of its improvement. 2 the SIFT main idea The SIFT algorithm is an algorithm to extract local features in scale space to find the extreme point of the extraction location, scale, rotation invariant. 3 the main features of the SIFT algorithm: a) SIFT feature is the local characteristics of the image, zoom, rotate, scale, brightness change to maintain invariance, the perspective changes, affine transformation, the noise also maintain a certain degree of stability. b) unique (Distinctiveness), informative, and mass characteristics database for fast, accurate matching [23]. c) large amounts, even if a handful of objects can also produce a large number of SIFT feature vectors. d) high-speed and optimized SIFT matching algorithm can even achieve real-time requirements. e) The scalability can be very convenient fe)

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这个函数用来通过蒙特卡罗求积分， f 要求的函数； fail1 积分下限 fail2 积分上限 a，b 积分区域内x的积分上下限 c,d 积分区域内y的最大和最小值,c,d不是必须的参数，但是有之计算速度更快 n 求解迭代次数( This function is used to find the integral Monte Carlo, function f requirements fail1 integral limit fail2 integral upper limit a, regional integration within b limit c on x points, d- the maximum and minimum value of the integration region y, c, d is not required parameters, but the calculation speed is faster n Iteration number)

最新关于故障诊断的论文，有LMD算法，还有EMD算法，主要是做轴承磨损预测的(failed to translatefailed to translatefailed to translatefailed to translate)