distributed

对一帧图片的目标物进行特征提取，以便以后进行块匹配(On a target image feature extraction, in order to block a later match)

摘要:本算法是在经典 Sobel算子基础上 ,结合汽车本身的实际情况 ,增加了 6个方向模板 ,同时利用基于 L范数的各同性扩散去噪模型消除噪声 ,最终得出汽车轮廓图。仿真结果表明:该算法对汽车图像的噪声干扰 有较强的抑制能力 ,汽车轮廓提取定位更准 ,精度更高 ,图像更清晰。 关键词:边缘检测算法 车辆轮廓 噪声 Sobel算子

i upload a matlab code which can calculate density of atmosphere and temperature in altitude of over 100 km

有较好的参考价值，光纤无线通信系统中传输性能的研究，微分方程组数值解方法。( There are good reference value, Fiber Transmission wireless communication system performance, Numerical solution of differential equations method.)

Gauss 消元法 — 不选主元 Gauss 消元法 — 列选主元. 插值法 lagrange .cpp 插值法 lagrange .cpp 二分法.txt 二分法和简单迭代法以及埃特金.txt 复化辛卜生公式.txt快速弦截法 gauss-seidel选代.txt牛顿迭代法.txt(Gauss elimination method- do not choose the principal component Gauss elimination method- principal component out how. Interpolation lagrange. Cpp interpolation lagrange. Cpp dichotomy. Txt dichotomy and a simple iterative method, and Aitken. Txt Fu Xin Bo' s formula of . txt fast chord intercept method gauss-seidel iteration. txt Newton iterative method. txt)

audio card enumerate searching ok. for multimedia developing project. thank you

根据输入的调制方式，对输入序列MOD_IN进行调制，分别采用BPSK, QPSK, !6QAM, 64QAM，完成对星座图的映射(According to the modulation input, the input sequence MOD_IN modulation, using BPSK. QPSK,! 6QAM, 64QAM, the completion of the mapping constellation)

掌纹识别的hog算法，在MATLAB中使用。(Palmprint recognition based on hog algorithm)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

RS编码语言，源代码，适合学习，已经运行过(RS coding language, source code, for learning, has been run over)