convolutional_VA_decoding3

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one orthogonal frequency-division multiplexing (OFDM) block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. By inserting some null subcarriers in the proposed tile structure, we propose a computationally efficient subspace decomposition- based algorithm for CFO estimation. The issue of optimal tile length is studied both theoretically and through simulations. By judiciously designing the tile size of the pilot, the proposed algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the time-division multiplexing-based training method and the computationally demanding space-alternating generalized expectation-maximization

说明：  这是一个编码系统，包括图像配准，一维二维huffman编码，lz算法等。(This is a coding system, including image registration, one-dimensional two-dimensional huffman coding, lz algorithm.)

利用MATLAB实现图像的离离散余弦变换的源代码可直接使用。 (The source code using MATLAB' s image away from the discrete cosine transform can be used directly.)

Multi-Instance Learning matlab code

使用matlab编写的各种类型的遗传算法，每一种都有注释说明 可以复制到M文件中运行(Matlab prepared using various types of genetic algorithms, each with explanatory notes can be copied to M to run in files)

is a small example of how to use a slider and observe and enter the current value in the slider

利用可视图理论对时间序列建网，得到复杂网络，再求取该网络的聚集系数(throuth the theory of time series of network construction, we get the complex network, and then calculate the clustering coefficient of the network)

遗传算法优化PID程序,MATLAB开发(PID genetic algorithm optimization procedures, the development of MATLAB)

Matlab 软件基础学习资料，改章为第六章“元胞与构架”(Matlab software based learning materials, change chapter is Chapter 6, " Cellular and architecture." )

Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement Usage ------- [logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options])