nrlfppg

三维血管重建——通过将文件转换为MATLAB矩阵，并缩取小矩形。。。(Three-dimensional vascular reconstruction)

This matlab code has a PCM siystem

自己编写的MATLAB函数，实现对图像的旋转。B是原图像，灰度或彩色，alpha是旋转角度（角度或弧度单位均可），A是旋转后图像。旋转后图像信息尽可能不损失。(I have written MATLAB function, to achieve image rotation. B is the original image, grayscale or color, alpha is the angle of rotation (degrees or radians per unit), A is rotated images. Rotation after the image information as possible without loss.)

In this paper, we propose and investigate a lowcomplexity human motion tracking system which is based on ultra-wideband (UWB) radio nodes.

基于matlab的混沌周期图的简单研究以及程序验证(Studies based on simple matlab chaotic cycle pattern, and program verification)

RFID ALOHA算法仿真，包括固定帧时隙、动态帧时隙和随机时隙算法(RFID ALOHA algorithm simulation, comprising a fixed frame time slot, time slot and frame dynamic random Slot Algorithm)

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)

控制DOS下ISA总线上的电信号输出，适用于工控上实现通道的开合的一个小程序。(control DOS ISA bus signals on the output, IPC applicable to achieve the opening of a channel of a small procedure.)

this codes are uses for signal process and uses ant colony

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)