阵列信号处理的理论与应用

课本所有章节的代码，对应课本每章节每个案例代码均有，可以放心使用(Code for all chapters of the textbook)

有趣的二进制 软件安全与逆向分析学习资料，希望能绑到有需要的人(Interesting binary software security and reverse analysis learning materials, hoping to be tied to people in need)

说明：  一个减肥的网站，比较适合嵌入谷歌的adsence广告，自己看看吧，送给需要的人。(A weight loss website, more suitable for embedding Google' s adsence ads themselves to see it, given to those in need.)

bq27500电源控制管理 bq7500德州仪器监测电源剩余电量的最新芯片(bq27500 power control and management of Texas Instruments bq7500 monitoring power consumption remaining the latest chip)

本程序仿真了DS_CDMA的原理，直接可用，是学习CDMA的很好的教程(This procedure DS_CDMA simulation of the principle, directly available, is to learn from the good CDMA Tutorial)

推荐一个谐波抑制和无功功率补偿方面的论文资料(Recommend a harmonic suppression and reactive power compensation papers on information)

说明：  本书既是一本全面而系统地讲解反汇编与逆向分析技术的安全类专著，又是一部深刻揭示C++内部工作机制的程序设计类著作。理论与实践并重，理论部分系统地讲解了C++的各种语法特性和元素的逆向分析方法和流程，重在授人以渔；实践部分通过几个经典的案例演示了逆向分析技术的具体实施步骤和方法。(This book is not only a comprehensive and systematic security monograph on disassembly and reverse analysis technology, but also a program design monograph that profoundly reveals the internal working mechanism of C + +. In the theory part, the reverse analysis methods and processes of various grammatical features and elements of C + + are systematically explained, with emphasis on teaching people to fish; in the practice part, the specific implementation steps and methods of reverse analysis technology are demonstrated through several classic cases.)

该文件包含matlab GUI设计学习手册的全部详细注解源代码(The document contains the MATLAB GUI design study manual of all detailed notes source code)

说明：  运动目标检测的matlab代码，通过调用视频中的帧图像来进行检测。(Moving target detection matlab code, by calling the video frame images for testing.)

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.)