testnlm

针对多椭圆检测问题提出了一种快速随机检测算法。该算法利用在图像中随机采样到的一个边缘点和 局部搜索到的两个边缘点以及这三个点的邻域信息确定候选椭圆,再将候选椭圆变换为对应圆,通过确认真圆来确 认真椭圆。在确定候选椭圆时,最大限度地减少随机采样点数‰ 剔除更多的非椭圆点,降低了无效采样,减少了无效 计算。数值实验结果表明:该算法具有良好的鲁棒性,其检测速度比同类算法快(Ellipse detection problem for many a fast random detection algorithm. The algorithm uses random sampling in the image of an edge point and the local search to the two edge points, as well as the three-point neighborhood information to determine the candidate ellipse, and then transformed into the corresponding elliptical candidate won, through the identification of real yen to confirm really elliptical. In determining when a candidate ellipse, to minimize random sampling points ‰ remove more non-oval points, reducing invalid sampling, to reduce the calculation invalid. Numerical experimental results show that: the algorithm has good robustness, and its detection faster than similar algorithms quickly)

generate LFM pulse waveform

Code source of Distribution Wigner ville

包括了matlab灰色预测模型以及其他重要的绘图分析思路(Including matlab gray forecasting model and other important plot analysis of ideas)

IIR design using matlab using all four types of iir filter

Image classification using RBFSVM and Manhattan distance. It detects face and eye. Face detection using Viola jones.

以高斯函数为载波的PPM调制解调，及于理想误码率曲线相比较的误码率曲线(Gaussian function of PPM modulation and demodulation of the carrier, and the desired BER curves compared to BER curves)

针对水声中的矢量圆阵，比较MUSIC算法与MVDR算法的分辨率和抗各向同性噪声的能力。(For underwater acoustic vector in circular array, the MUSIC algorithm with the ability to compare MVDR algorithm isotropic resolution and anti-noise.)

卡尔曼滤波轨迹跟踪target tracking using kalman(target tracking using kalman)

希望给辛苦科研的人带来一点点帮助--学习因子可以变化的PSO算法，不再是c1=c2=2，粒子进化更加灵活，下面是学习因子同步变化的pso，调用形式为[xm,fv]=LNCPSO(fitness,N,cmax,cmin,w,M,D)(People who want to work hard to bring a little bit of research to help- learning factor can vary PSO algorithm is no longer c1 = c2 = 2, particle evolution is more flexible, learning factor is below synchronous changes pso, calling in the form of [xm, fv] = LNCPSO (fitness, N, cmax, cmin, w, M, D))