salt-and-pepper-witjhout

VC++中调用MATLAB引擎进行绘图完整源代码(VC++ graphics engine is called a complete MATLAB source code)

自适应有源噪声控制FLMS算法仿真程序，包含主要仿真程序和最后绘图结果(Adaptive Active Noise Control Algorithm for Simulation Program FLMS)

在matlab中的ofdm的编程，包括数据生成，调制，数据传送，频道干扰，解调，数据接收，并且计算误码率，把误码率用图像的形式表现出来。(In the OFDM matlab programming, including data generation, modulation, data transmission, channel interference, demodulation, data receiving, and calculating the BER, the BER image forms.)

介绍了相关的说明文档及应用手册，及其机械构造，尺寸等(Introduce the relevant documentation and application manual, and its mechanical construction, size, etc.)

线性及卷积混合信号盲源分离论文及算法 全是算法,看不懂也不要下了,慢慢看,免费的 (Linear and mixed-signal convolution papers and blind source separation algorithms are algorithms, do not know do not look down, and slowly watch, free)

巴特沃斯滤波器设置，自行设置阶数、截止频率等滤波器参数(butter-worth filter)

主动噪声控制中的FXLMS算法研究，包括离线辨识、在线辨识。(The research of FXLMS algorithm in active noise control includes off-line identification and on-line identification.)

xPC Target 工具箱使你可以在 Simulink 的框图中加入 I/O 方块图，并用 RTW 产生代码，最后下载到另一个运行 xPC Target 实时内核的 PC 机上。对于控制和 DSP 系统来说 xPC Target 是理想的快速原型和硬件在回路测试工具，它可以使你在一台标准的 PC 机上运行实时模型。如果附加 xPC Target 嵌入模块选项，你可以把你的实时嵌入式系统放入到一台微机上，应用于生产、数据采集、标定和测试应用程序等过程中。 (xPC Target toolbox allows you to add in the Simulink block diagram of the I/O block diagram, and use RTW generated code, and finally downloaded to another running xPC Target real-time kernel of the PC machine. For the control and DSP systems is ideal for the xPC Target rapid prototyping and hardware in the loop testing tool that allows you to a standard PC, running real-time model. If the additional xPC Target Embedded Module option, you can put your real-time embedded systems to a microcomputer applied to the production, data acquisition, calibration and test applications such as process. )

数字水印中Optimal Quincunx Filter Banks的设计源代码及说明，在matlab环境下实现(Digital Watermarking Optimal Quincunx Filter Banks behalf of the design source code and explanations of the environment under Matlab)

The Hurst parameter H characterizes the degree of long-range dependence (and asymptotic selfsimilarity) in stationary time series. Many methods have been developed for the estimation of H data. In practice, however, the classical estimation techniques can be severely affected by nonstationary artifacts in the time series.