chap1 rbf_sinmulink

说明：  matlab科学计算继续上传，一共10个章节，由浅入深，几乎包括了绝大部分的科学计算方法以及仿真。本人把部分算法做了优化，更为完善，一共十二个，其中11、12是本人与实验室搭档共同编写，感谢王美女(matlab scientific computing continue to upload a total of 10 chapters, easy-to-digest, almost including most of the scientific method of calculation and simulation. I have done some algorithm optimization, a more perfect, a total of 12, 11,12 which is my partner, prepared jointly with the laboratory, thanks to Wang Mei)

tricolor attenuation model

国外的一篇文章，首次介绍压缩粒子滤波方法及其在目标跟踪中的应用(Foreign article first introduces compressed particle filter method and its application in Target Tracking)

matlab编写的禁忌搜索算法，解决了TSP问题，对初学者有重要的参考价值(tb algorithm for tsp problem)

Cortex-M3 Technical Reference Manual Revision r2p1(Cortex-M3 Technical Reference Manual Revision r2p1 )

张正友摄像机标定的matlab实现原代码(Zhang camera calibration is the realization of the original code Matlab)

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)

Plugin system example

Mean shift clustering algorithm is trying to help us for data set points clustering.

多径和多普勒的讲义，文件内含matlab源代码，对于了解多径和多普勒效应很有帮助。(Multipath and Doppler handouts, matlab file containing the source code for understanding the effect of multipath and Doppler helpful.)