应用离散傅里叶变换DFT，通过MATLAB实现对连续信号频谱分析，以满足理论分析和工程实际的需要。...

自适应 Golomb 编码， 为高级数据压缩算法的一种(Adaptive Coding for advanced data compression algorithm of a)

说明：  这是一个基于空时分组编码的MIMO_OFDM通信系统的仿真设计。此系统包括QPSK调制解调，IFFT调制，空时编解码，基于训练符号的信道估计等通信模块。 (This is a space-time block coding based on the MIMO_OFDM Communication System Simulation and Design. This system consists of QPSK modulation and demodulation, IFFT modulation, space time coding and decoding, based on training symbols such as communication channel estimation module.)

自适应滤波 开发环境：ccs3.1 设置事项：project-build options-linker-stack options 500 (adaptive filtering development environment : ccs3.1 set matters : project-build options-linker-stack options 500)

PLS - DN是一个有限的牛顿为解决非退化的分段线性系统的算法。 PLS - DN的展品可证明半迭代 财产即在全球范围内的精确解在有限数量的迭代算法收敛。被证明是收敛速度 至少前终止线性。 广泛的算法是在我们的AISTATS 2011纸描述：“一个非退化的分段线性系统的有限牛顿算法”。此演示包重新运行 在解决非负最小二乘法（NNLS）问题上的三个稀疏的设计矩阵，从哈威尔波音收集（达夫等人，1989年）第4.2节的实验。 随着PLS - DN，此演示包建议实施五 其他NNLS求解：LCP_Fisher_Newton，LCP_Lemke，PQN，TRESNEI，和SCD (PLS- DN is a finite non-degenerate Newton to solve the piecewise linear system algorithms. PLS- DN exhibits semi-iterative property to prove that the exact solutions worldwide in a limited number of iterative convergence. Proved to be terminated before the linear convergence rate at least. A wide range of algorithms in our AISTATS 2011 paper describes: " a non-degenerate sub-linear systems with limited Newton algorithm." This demo package to re-run in the settlement of non-negative least squares (NNLS) on the design of the three sparse matrices from the Harwell Boeing collection (Duff et al, 1989) Section 4.2 of the experiment. With PLS- DN, this demonstrates the implementation of five other NNLS package proposed solution: LCP_Fisher_Newton, LCP_Lemke, PQN, TRESNEI, and SCD)

pwm方波控制仿真 通过仿真输出方波pwm 并输出正弦波 谢谢(PWM square wave control simulation by simulation output square wave and sine wave output for PWM)

信号过载情况下的SVM波束形成, 比其他常用的方法效果好很多.-signal overload of SVM beamforming than other commonly used method is much better.

document for code of brain compture interface with EEG Signal

Compressive Sampling是最近讨论非常热烈的数据处理方法。用一个与变换基不相关的观测矩阵将变换所得到的高维信号投影到一个低维空间上，然后通过优化求解从这些少量的投影中以高概率重构出原信号。(Compressive Sampling is a very lively discussion of recent data processing method. A base with the transformation matrix will not change the relevant observation obtained a high-dimensional signal projected onto low-dimensional space, and then solved by optimizing the projection from these small reconstructed with high probability in the original signal.)

Logging message in matlab

D-S证据理论数据融合，Matlab实现界面友好，Relief计算分类权重，供做算法研究人员参考，仿真效率很高的，各种资源分配算法实现。