CoSaMP-and-OMP-for-sparse-recovery

一篇经典的2Ｄ转３Ｄ方面的文章，研究3DTV领域的很有用(ａclassic paper on 2D to 3D)

there is a matlab cod for Autonomous system, that describe to compute trajectory and point of the system, and plotting it.

针对无线接入系统，包括蓝牙等的matlab模型仿真(For wireless access systems, including Bluetooth, etc. matlab model simulation)

最近邻分类器，matlab环境下，可直接调用(Nearest neighbor classifier, matlab environment, can be called directly)

绘制带通滤波器的图形 坐标的变换,低通到高通，带通，带阻(Band-pass filter rendering graphics coordinates transform, low pass to high pass, band-pass, band-stop)

MATLAB is being accepted by the majority of users, the interconnection line to the explosive speed is cited, the following is relevant information

RNA example T/P Las redes de neuronas artificiales (denominadas habitualmente como RNA o en inglés como: "ANN"1 ) son un paradigma de aprendizaje y procesamiento automático inspirado en la forma en que funciona el sistema nervioso de los animales.

此程序对协作通信的中继选择和功率控制，采用了蚁群算法。(The program of collaborative communication relay selection and power control, using the ant colony algorithm.)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

主要参考书： MATLAB 6.5 辅助优化计算与设计 飞思科技产品研发中心编著 (Main reference: MATLAB 6.5 auxiliary optimization and design Fei Sike Technology R & D Center compiled)