angle2dcm

生成一个好的Turbo码，并且用MAP方法进行解码( Generate a turbo code, and decode the code iteratively using MAP detectors)

该文件是使用粒子群算法来求解BP神经网络饿最优解，进而对样本数据训练学习(The document is the use of particle swarm optimization to solving hunger BP neural network the optimal solution, and then the training sample data for the study)

为了获得更加准确的估计位置,对一种基于数据融合的定位解算算法进行了研究,在将模拟退火算法与经典的泰勒级数展开法和Chan氏算法进行性能比较的基础上,将模拟退火算法与上述两种经典的位置解算算法相结合,通过构造多算法协同定位模型,并基于该模型设计了融合定位算法,对传统定位解算算法进行改进,提出了一种基于数据融合的定位解算算法。仿真结果表明,融合算法的定位精度优于非融合算法且运算量较小(In order to obtain more accurate estimates of the location of a data fusion-based positioning solver algorithm is studied in the simulated annealing algorithm with the classical Taylor series expansion method and Chan' s algorithms performance comparison based on simulated annealing algorithm with the above-mentioned two kinds of classical solution calculate the location of the Combination, by constructing a multi-algorithm co-location model, and model design based on the location of the fusion algorithm, the traditional location solver algorithm is proposed to improve a position based on data fusion solver algorithm. Simulation results show that the positioning accuracy of better than fusion fusion algorithm and the computation of non-small)

Visual C++ 下调用Matcom转换的Matlab代码(Visual C++ under the call of the Matlab code conversion Matcom)

基于无传感器的矢量控制的matlab的程序框图，经试验，可以正常运转(Based sensorless vector control matlab block diagram, the test can be normal operation)

这个包实现了ITU - T的G.723.1语音编码器和解码器在Matlab。该方案的目标是提供一个良好的记录和模块化设计方案，实验和研究，以方便使用为基础的实际编码器。 编码器实现了G.723.1的多脉冲模式（在6.3 kb / s的运行）。解码器可以解码的5.3 kb / s的，在6.3 kb的多脉冲/秒，连续传输，数据包丢失隐藏）编码器（所有的ACELP方式。 该编码器和解码器之间的标准接口是位文件（同位文件由ITU - T参考代码产生兼容）。第二个选项之间传递的编码器和解码器使用Matlab的数据信息文件。此选项允许测试通过传递量化或unquantized之间的编码器和解码器的信息。 有报道[1]描述了G.723.1编码与Matlab实现与分布包括在内。 ITU - T建议G.723.1的[2]在线提供。该建议含(This package implements the ITU-T G.723.1 speech coder and decoder in Matlab. The goal of the package is to provide a well-documented and modular program that was designed to facilitate experimentation and research using a practical coder as a base. The coder implements the multipulse mode of G.723.1 (running at 6.3 kb/s). The decoder can decode all modes of the coder (ACELP at 5.3 kb/s, multipulse at 6.3 kb/s, discontinuous-transmission, packet loss concealment). The standard interface between the coder and decoder is a bit file (compatible with the bit files generated by the ITU-T reference code). A second option passes information between the coder and decoder using a Matlab data file. This option allows for testing by passing quantized or unquantized information between the coder and the decoder. A report [1] describing the G.723.1 and the Matlab implementation is included with the distribution. ITU-T Recommendation G.723.1 [2] is available on-line. The recommendation incl)

add sub bytes through incrypting image

LDPC 基于似然函数的译码算法函数，采用MATLAB编写，可以直接调用(LDPC decoding algorithm based on similar function the likelihood function, using MATLAB to prepare, you can directly call)

ActiveModels contains a Matlab toolset and examples for training and fitting AAMs (Active Appearance Models). Please refer to the code and included examples for its function. You must have an annotated database to use its features.

图像处理MATLAB源代码，这个是贝叶斯估计-bayes guess(Image Processing MATLAB source code, this is the Bayesian estimation-bayes guess)