radar_matlab

说明：  Matlab 模拟 CDMA 直接扩频。m序列生成矩阵为p(x) = 1 + x^5 + x^7 + x^8 + x^9 + x^13 + x^15。能画出每阶段的时域、频域图。(Matlab simulation of CDMA Direct Spread Spectrum. m sequence generated matrix for p (x) = 1+ x ^ 5+ x ^ 7+ x ^ 8+ x ^ 9+ x ^ 13+ x ^ 15. To draw each stage of the time domain, frequency domain Fig.)

说明：  提取图像的GLCM矩阵，并计算出Haralick建议的14个特征值(Extract images GLCM matrix, and calculate the 14 Haralick features of the proposed values)

MATLAB下FFT变换实用2个例子，可以进行绘制fft波形及计算THD(The MATLAB FFT transform practical two examples can draw the fft waveforms and calculate THD)

本程序是用于图像去噪，使用的是多小波GHM去噪方法，对比单小波去噪效果明显。(This procedure is used for image denoising, the use of multi wavelet GHM denoising method, compared to Dan Xiaobo denoising effect is obvious.)

说明：  数字下变频技术是数字接收机的核心技术之一，它包括数字混频正交变换、抽取及高效率数字滤波等。(Digital down conversion technology is one of the core technology of digital receiver, which includes digital mixer orthogonal transformation, extraction and high-efficiency filtering.)

图像拼接Matlab程序，基于块匹配的全景图像拼接(Image mosaic Matlab program, based on block matching panoramic image stitching)

将VaxD（“Dec”）格式的C3Ds数值格式转化为IEEE—LE（“PC”）格式。(Convert the numerical format of C3Ds from VaxD ("Dec") to IEEE-LE ("PC"))

Matlab经典算法程序之规划问题，包含PPT讲义，可对照程序进行学习(Matlab classic algorithm of planning procedures, including PPT handouts can control procedures for learning)

改进的分段线性表示方法，含有四种基本算法，自顶向下，自底向上，滑窗，以及滑窗式自底向上算法。是流行的降维、分类算法。改进之后，所得出的线段是连续的，解决了离散线段带来的不便。(Rewised PLR algorithms inclusing four basic methods: top down, bottom up, sliding window and sliding window bottom up. PLR is a popular algorithm for classification and demension reduction. )

MATLAB平台下的CUDA加速库。运用该库可避免在MEX函数中来回拷贝GPU数据，提高MATLAB运行速率。附件给出一个简单的矩阵乘法的示例程序，该程序在GTX TITAN下运行效率比intel i7 3930k提高了10倍。(CUDA MATLAB platform acceleration libraries. Avoid the use of the library can be copied back and forth GPU MEX function data, improve MATLAB run rate. Annex gives an example of a simple matrix multiplication program that runs under the GTX TITAN 10 times more efficient than the intel i7 3930k.)