▍1. 跳频序列相关源代码
说明： 实现一个16×32的跳频序列，里面有自相关的检测代码(Realization of Frequency Hopping Sequence)
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▍2. libsvm-3.24
说明： 台湾大学林教授团队libsvm工具箱，svm参数寻优(The libsvm toolbox of Professor Lin's team at National Taiwan University, svm parameter optimization)
▍3. 3DRayTrace
实现3D环境下的射线追踪，程序很好用，与大家分享(Realize ray tracing in 3D environment. The program is very useful and can be shared with you.)
▍4. 3DRayTrace
说明： 实现3D环境下的射线追踪，程序很好用，与大家分享(Realize ray tracing in 3D environment. The program is very useful and can be shared with you.)
▍5. GRNN-MIV
基于GRNN神经网络的MIV数据筛选，并画出数据筛选后的权重散点图。(Based on the GRNN neural network MIV data screening, and draw the data after the screening of the weight of the scatter plot.)
▍6. RBF-shenjingwanglou
RFB神经网络 主要有RFB神经网络的实现、非线性滤波(RFB neural networks are mainly realized RFB neural networks, nonlinear filtering)
▍7. DM5
使用matlab进行数据预处理，包括缺失值和离群值的处理(Using matlab for data preprocessing, including missing values and outliers are treated)
▍8. DM5
使用matlab进行数据预处理，包括缺失值和离群值的处理(Using matlab for data preprocessing, including missing values and outliers are treated)
▍9. CARLA
CARLA算法是由拉夫堡大学的马克·豪厄尔（Mark Howell）在EPSRC的资助下担任博士后研究员时开发的。这些文件是为工作提供的，已经在MATLAB R14中进行了测试。(The CARLA algorith was developed by Mark Howell at Loughborough University while working as a post doctoral research fellow under an EPSRC grant. These files are provided for work have been tested in Matlab R14.)
▍10. CARLA
说明： CARLA算法是由拉夫堡大学的马克·豪厄尔（Mark Howell）在EPSRC的资助下担任博士后研究员时开发的。这些文件是为工作提供的，已经在MATLAB R14中进行了测试。(The CARLA algorith was developed by Mark Howell at Loughborough University while working as a post doctoral research fellow under an EPSRC grant. These files are provided for work have been tested in Matlab R14.)
▍11. Saliency-Detection
提出一种新的显着性检测方法，通过将区域级显着性估计和像素级显着性预测与CNN（表示为CRPSD）相结合。对于像素级显着性预测，通过修改VGGNet体系结构来执行完全卷积神经网络（称为像素级CNN）以执行多尺度特征学习，基于该学习进行图像到图像预测以完成像素级显着性检测。对于区域级显着性估计，首先设计基于自适应超像素的区域生成技术以将图像分割成区域，基于该区域通过使用CNN模型（称为区域级CNN）来估计区域级显着性。通过使用另一CNN（称为融合CNN）融合像素级和区域级显着性以形成nal显着图，并且联合学习像素级CNN和融合CNN。对四个公共基准数据集的大量定量和定性实验表明，所提出的方法大大优于最先进的显着性检测方法。(A new saliency detection method by significant regional level estimates and forecast significant pixel level and CNN (expressed as CRPSD) combined. For pixel-level significant prediction to perform a full convolution neural network by modifying VGGNet architecture (called pixel-level CNN) learning to perform multi-scale features, image to image prediction to complete the pixel level detection based on the significant learning . For regional levels significantly estimate, the first generation technology to design image is divided into regions based on adaptive super-pixel area, based on the model of the region through the use of CNN (CNN called regional level) to estimate regional levels significantly. By using another CNN (CNN called fusion) Fusion pixel level and regional level to form nal significant saliency map, and the Joint Learning pixel level fusion CNN and CNN. Four common reference data set of a large number of quantitative and qualitative experiments show that the proposed m)
▍12. supportvector
说明： 我用于脑机接口P300拼写范例中的程序，关于SVM的详尽源程序(for brain-machine interfaces P300 spelling examples of procedures for the detailed source SVM)
▍13. matlab动物识别系统
说明： matlab环境下运行算法，进行提取特征，机器学习并进行识别，开发了动物识别系统，该系统简单实用。(Identify animals by their characteristics)
▍14. 33节点配网重构
对IEEE 33节点配电网重构进行编程分析，适合初学者学习。(Program analysis of IEEE 33 node distribution network reconstruction for beginners to learn)
▍15. matlab
说明： GA-SVM预测数据，输入训练样本，运用测试样本测试(Ga-svm prediction data, input training sample, use test sample test)
▍16. AdaBoost
经典的行人检测算法，AdaBoost分类器(AdaBoost Classifier using MATLAB program)
▍17. gmdh
神经网络gmdh的详细程序，demo是主程序(Neural Networks gmdh detailed procedures, demo is the main program)
▍18. 内窥镜去反光代码
说明： 去除内窥镜图像的反射高光，并修补此高光区域(Remove the reflected highlights of the endoscopic image and patch this area)
▍19. cnn-tlvqm-master
说明： 人工智能深度学习用于图像分类神经网络 matlab平台 方便快捷 好用(Artificial intelligence deep learning applied to image classification neural network matlab platform is convenient, fast and easy to use)
▍20. SVM自编程序
说明： 支持向量机程序，支持状态分类，常用于滚动轴承故障状态分类识别(Support vector machine program, support state classification, commonly used in rolling bearing fault state classification and recognition)
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