HuaDongPingJun

关于MATLAB中图像旋转等内容的一些实例，很有用。(On MATLAB, image rotation, etc. are some examples, very useful.)

模糊数学一般的教程，内容详尽，可靠 模糊分析。(Fuzzy general tutorials, detailed, and reliable fuzzy analysis.)

灰度梯度共生矩阵 H 归一化灰度梯度矩阵 H_basic 小梯度优势 T1 大梯度优势 T2 灰度分布的不均匀性 T3 梯度分布的不均匀性 T4 能量 T5 灰度平均 T6 梯度平均 T7 灰度均方差 T8 梯度均方差 T9 相关 T10 灰度熵 T11 梯度熵 T12 混合熵 T13 惯性 T14 逆差矩 T15(GGCM H normalized gray gradient matrix H_basic small advantage T1 gradient big advantage T2 gradient gray uneven distribution of T3 gradient uneven distribution of energy T5 gray T4 average T6 average gradient gray T7 average gradient T8 variance variance T9 were related to T10 T11 gradient gray entropy entropy of mixing entropy T12 T13 inertia moment T15 T14 deficit)

用matlab实现的MSR增强算法，对低照度图像有一定的增强效果(Using matlab realize the MSR enhancement algorithms for enhanced low-light images have a certain effect)

说明：  双有源全桥DC-DC变换器双重移相MATLAB仿真(Matlab simulation of double active full bridge double phase shifting)

RAKE receiver for direct-sequence spread spectrum systems Simulink model of a simple direct-sequence spread spectrum system with RAKE reception(RAKE receiver for direct-sequence spread spectrum systems Simulink model of a simple dir ect- sequence spread spectrum system with RAKE reception)

DOA经典算法系列之MUSIC MUSIC算法的深入研究改进算法众多,这里提供Music系列一:信号子空间法(DOA series of classical algorithm MUSIC MUSIC- depth study of the many improved algorithm, Music here for a series : signal-space law)

Identification of a muffle furnace

利用Matlab提取图片中的数据 从事科研或者工程的人员在文档撰写过程中，常需要将文献中的曲线与自己的结果对比，为获取原始数据，一种常用的办法是手动描点，即将原始曲线放大然后打印出来，选取一定数量的点，读出其横纵坐标，然后重绘。对于较为平坦的曲线，这种方法当然可行，但当曲线数量增加，曲线变化复杂，这种方法工作量可想而知。前段时间由于原始数据丢失，仅剩几十幅图片，本人尝试过手动描点，经历几个小时奋战，实在无法继续，索性转向matlab，借助其强大的数据处理能力，编写了两个GUI的小软件image2data、data_poly提取数据，如今大功告成，遂于大家分享。(Using Matlab to extract picture data in scientific research or engineering personnel in the process of writing documentation, often need to curve in the literature comparing the results to obtain the original data, a common approach is described in the manual, is about the original amplification curve and then print it out, select a certain number of points, read out its horizontal and vertical coordinates, and then redraw. For a flatter curve, this approach is of course possible, but when the increase in the number of curves, the complexity of the curve, this method can imagine the workload. Some time ago due to the loss of original data, except for the dozens of pictures, I tried manually tracing point, after several hours of fighting, I can not continue to simply turn to Matlab, with its powerful data processing capabilities, the preparation of two GUI software image2data, data_poly extract data, and now you' re done, then share with you.)

说明：  matlab实现分类回归，优化后的c/g完成(Matlab to achieve classification regression)