UraPIV

说明：  在PSIM仿真软件中，对DC/AC，AC/DC，DC/DC做了基本仿真实验，对设计电源的人有一定帮助。(In PSIM simulation software, on the DC/AC, AC/DC, DC/DC made a basic simulation experiments, the power of design to help people who have some.)

说明：  actif power filter with inductance

用于处理地震数据的一系列操作代码，包含对地震书籍的处理和对井资料的处理，非常适合勘探地震学初学者学习。里面附有英文使用说明(Code for seismic data processing a series of operations, including handling and processing of seismic well data books, is very suitable for beginners to learn exploration seismology.)

GUI设计蜘蛛爬行，针对互相追逐的蜘蛛，建立他们的爬行轨迹，同事，计算相遇时间等的Matlab程序文件。(GUI design spider, spider chasing each other, build their crawling track, colleagues calculated to meet the time of the Matlab program file.)

雷达回波信号的高精度一维距离像分析，功能较完整，适合初学者(the high-resolution range profile analysis of radar echos, which is complete and is suitable for beginners)

基于MH算法的贝叶斯模型更新，结构为五层框架结构，更新得到结构的物理参数(The Bayesian model based on the MH algorithm is updated and the structure is a five-story frame structure. The physical parameters of the structure are updated)

一个可以实用的LMS,NLMS等四种自适应滤波的MATLAB算法程序，实际调试成功。(A practical LMS, NLMS four MATLAB algorithm for adaptive filtering process, the success of the actual debugging.)

卡尔曼跟踪代码 卡尔曼跟踪代码 卡尔曼跟踪代码 卡尔曼跟踪代码 卡尔曼跟踪代码 (Kalman Kalman tracking code tracking code tracking code Kalman Kalman Kalman tracking code tracking code)

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)

信号与系统matlab实验,各种基础例程，实用(Signals and Systems matlab experiment, a variety of basic routines, practical)