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说明：  书籍 Design and Fabrication of Diffractive Optical Elements with MATLAB里面提到的所有案例的Matlab源代码，本资源不包含该书，本人有该书，但各位也可以通过豆丁文档找到该书的网络版(Design and Fabrication of Diffractive Optical Elements with MATLAB's matlab code,for you referance)

功率谱估计程序，选用一电力系统谐波信号为输入。其中包括有：burge算法，esprit算法，music算法，mvdr算法，pisarenko算法，prony算法，修正协方差算法，自相关算法。这些程序均为本人自编，并且对程序所参考书籍和页数进行了标注。经过调试，绝对正确，请放心使用。(Power spectrum estimation procedure, selection of one power system harmonics signal as input. Including: burge algorithm, esprit algorithm, music algorithm, mvdr algorithm, pisarenko algorithm, prony algorithm, as amended covariance algorithm, since the correlation algorithm. I own both of these procedures, and procedures of reference books and the number of pages have been marked. After debugging, absolutely correct, please rest assured that use.)

有限单元法及程序设计（第二版），天津大学刘尔烈，非常经典的fortran学习材料(Finite element method and program design (Second Edition),Tianjin University, Liu Erlie, a very classic FORTRAN learning material)

我最近在研究frft-分数阶傅里叶变换的数值计算问题，主要是Ozaktas提出的采样型算法，以及Pei 等提出的采样型算法。(I have recently studied the numerical computation of frft- fractional Fourier transform, mainly the sampling algorithm proposed by Ozaktas, and the sampling algorithm proposed by Pei.)

tiny c compiler

强杂波背景下的的高斯点目标的检测Matlab源代码，有需要的就下载吧 希望能有所帮助 (Strong clutter the background of the Gaussian point target detection Matlab source code, there is a need to download it on the hope to be helpful)

说明：  DSP从入门到精通，对于初学者是非常好的教程，希望能帮到大家(From introductory to proficient in DSP ,which is a very good course for beginners. I hope it can help you all.)

说明：  随机森林算法与 Bagging 算法类似，均是基于 Bootstrap 方法重采样，产生多个训练集。不同的是，随机森林算法在构建决策树的时候，采用了随机选取分裂属性集的方法 本程序中，将乳腺肿瘤病灶组织的细胞核显微图像的 10 个量化特征作为模型的输入，良性乳腺肿瘤和恶性乳腺肿瘤作为模型的输出。用训练集数据进行随机森林分类器的创建，然后对测试集数据进行仿真测试，最后对测试结果进行分析。(Similar to bagging algorithm, random forest algorithm is based on bootstrap resampling to generate multiple training sets. The difference is that the random forest algorithm uses the method of randomly selecting the split attribute set when constructing the decision tree In this program, 10 quantitative features of nuclear microscopic image of breast tumor tissue are taken as the input of the model, and the benign and malignant breast tumor are taken as the output of the model. The training set data is used to create the random forest classifier, then the test set data is simulated and the test results are analyzed.)

matlab 神经网络 书本源代码 学习神经网络经典案例(matlab abjfdlkjklfjljfkjfkldsj)

Project on cryptography related to digital image processing. if anyone wish to perform then can download it. reference papers are also included.