MFO

OFDM channel using LMS

压缩感知可以用远低于奈奎斯特速率对信号进行采样，然后用重构算法对测量的信号进行重构，这程序就是CS的重构算法。(Compressed sensing can be used far less than the Nyquist sampling rate of the signal, and then use the measurement signal reconstruction algorithm to reconstruct this program is CS reconstruction algorithms.)

说明：  经典的matlab与VC++混合编程例子(using Matlab and c++ to program togother。)

指纹识别的一套完整成，还有指纹图像。绝对有用，适合毕业设计研究(Into a complete fingerprint recognition, as well as the fingerprint image. Definitely useful for the design of graduation)

讲述UKF无迹滤波的原理得子程序，值得一看(About the principles of UKF was no trace filter subroutine, worth a visit)

系统辨识与自适应控制MATLAB仿真教材里批处理 最小二乘法的参数估计的matlab实现的程序(System identification and adaptive control of MATLAB simulation teaching materials in batch Parameter estimation of least squares method of MATLAB program)

DCT and Image Compression In the JPEG image compression algorithm, the input image is divided into 8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each block. The DCT coefficients are then quantized, coded, and transmitted. The JPEG receiver (or JPEG file reader) decodes the quantized DCT coefficients, computes the inverse two-dimensional DCT of each block, and then puts the blocks back together into a single image. For typical images, many of the DCT coefficients have values close to zero these coefficients can be discarded without seriously affecting the quality of the reconstructed image. The example code below computes the two-dimensional DCT of 8-by-8 blocks in the input image, discards (sets to zero) all but 10 of the 64 DCT coefficients in each block, and then reconstructs the image using the two-dimensional inverse DCT of each block. The transform matrix computation method is used.

通信相关技术文档，关于能效方向，无线传输中的能耗控制(Communication related technical documents, about the direction of energy efficiency, energy consumption of wireless transmission control)

公式法产生任意均值、方差和长度的高斯白噪声(Formula method produce any mean, variance and the length of the Gaussian white noise)

：在有限区域内分布的稀疏不均的、具有一定分布结构的海量数据集的高效、高精度聚 类问题是一个尚未完全圆满解决的难题。针对Affinity Propagation 聚类算法(AP)的不足之处， 提出了两个改进型的聚类算法(n limited areas of uneven distribution of sparse, has certain distribution structure of the mass datasets of high efficiency, high precision together Problem is a perfectly solved problem has not yet completely. For Affinity Propagation clustering algorithm (AP) of the deficiencies, Put forward the two improved version of the clustering algorithm)