Adaptive_beam_assignment

这是我们非线性课程的一次作业，有分岔图，蛛网图，庞加莱截面，希望对大家有用。(This is our first non-linear operation of the curriculum, there is bifurcation diagram, spider diagram, Poincare section, in the hope that useful to everyone.)

数学形态学去噪 有效地还原了原始信号 去除了振动噪声和基线漂移(signal denoising )

采用最小均方差法对虹膜瞳孔定位，虹膜外圆定位，归一化及其增强处理，圆拟合(Using least-mean-variance method of positioning the iris pupil, iris outer location, normalized and enhanced handling, circle fitting)

c源代码 图象的压缩编码，JPEG压缩编码标准 功能是行程编码，JPEG压缩编码（基本系统）(c code, function: jpeg stand for image show)

说明：  可以将有重叠部分的多张图片拼接成一张全景图片(You can stitch multiple images with overlapping parts into a single panorama)

计算机图形学的多边形求交算法，实现两个多边形的求交功能(Computer graphics polygon intersection algorithm to achieve two polygon intersection function)

this Class used to re-sampling image and convert at as you wont and as u need to use it in your program

DLP LightCrafter4500 Control Software V3汉化版(DLP LightCrafter4500 Control Software V3(Chinese))

说明：  一个EM算法实现图像分割，可以实现彩色图像分割。(An EM algorithm is used for image segmentation.)

电压跌落是最严重的动态电能质量问题之一， 精确定位电压跌落起止时间是应对电压跌落问题的 重要前提和基础。由于电压采样信号往往有噪声分 量，现有的方法在定位电压跌落的起止时间时存在 局限性。本文提出利用多小波变换及相邻系数去噪 的电压跌落定位方法。多小波兼有对称性、正交性、 有限支撑性和二阶消失矩等优异的信号处理性能， 利用GHM多小波可以准确定位电压跌落起止时间。 多小波变换系数在每层之间具有对应关系，多小波 相邻系数将紧相邻的若干个系数作为一个整体来确 定阈值，考虑了系数之间的相关性，能获得更好的 去噪效果。通过 Matlab 进行仿真验证，仿真结果表 明，所提出的方法的正确性。 (Voltage sag is one of the most serious dynamic power quality problems. Critical start-time and end-time are important indices for voltage sags. But the sampling signals often have noisy component, the locations of start-time and end-time are hard to get. Wavelet is an effective tool for those non-stationary signal processing and has been used in this field. Local feature in the signal can be enlarged after the transformation using the scalar wavelet. But scalar wavelets cannot contain orthogonality, symmetry, compact support and higher order of vanishing moments simultaneously. In this thesis, multi-wavelets GHM is used to detect and locate power quality disturbances. Multi-wavelets offer many excellent properties such as the same approximation order but more compact support. The dependence of the multi-wavelets coefficients varies with the level, so neighboring coefficien)