nprot.2011.424-S5

说明：  例子群优化的一个的简单小程序，是利用matlab写的。(Swarm optimization example of a simple small program, was written by matlab.)

离散傅立叶变换(DFT)及其反变换的实现,有限长序列的循环移位、循环卷积(Discrete Fourier Transform (DFT) and its inverse transform of the realization of the cyclic shift sequence of finite length, circular convolution)

MATLAB边缘检测程序，在用MATLAB进行图像处理时非常有用！(MATLAB edge detection procedure, image processing with MATLAB is useful!)

用MATLAB编写的混沌序列图像加密程序,加密密钥跟解密密钥(MATLAB prepared by the chaotic sequence image encryption program, encryption key and decryption key)

MATLAB画图 这个纯属美术娱乐和分享(MATLAB painting heart diagram this is purely art entertainment and sharing)

搜集了几种人工只能算法，基于Matlab平台编写，包括聚类、统计稀疏、最小范数法、DOA、投影追踪、稀疏贝叶斯等(Several artificial algorithms are collected and compiled on the platform of matlab, including clustering, statistical sparseness, minimum norm method, DOA, projection tracking, sparse Bayesian, etc.)

Generalized program for calculation of BER for various wireless system

Finding localization error for wireless sensor network by Matlab

matlab 实现图像 的缩小放大 (Enlarge Image narrowing 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)