matlab语言实现单目标优化，采用遗传算法单目标优化

说明：  用于面板数据平稳性问题（单位根）的MW检验(MW test for panel data stationarity (unit root))

说明：  一个功能强大的 hex editor 原码 hex edit4(A powerful hex editor source)

安卓远控手机+动画教程软件已经打包，自己下载看(Android mobile phone remote control + animation tutorial)

说明：  实现了平方根容积卡尔曼滤波的过程，可以用于非线性滤波(The process of square root cubature Kalman filter is realized.It can be used for nonlinear filtering.)

说明：  基础神经网络 mnist训练，了解各个基础神经网络的组成(Basic neural network training)

一个用csharp写的计算器程序，内容较简单，适合初学者(using csharp write calculator,easy for first study!!! )

AR程序设计（2），扫描图片可以出现三维模型，并操作(AR programming(2), scanning pictures can appear three-dimensional model, and operation)

说明：  高斯烟羽模型的matlab代码，下次从这里下载(Gauss plume model Matlab code, next download from here)

用verilog实现ldpc最小和译码算法(This code is for the decode of MS-algorithm based on LDPC.)

说明：  裂纹是典型的线结构，在许多计算机视觉应用中都很有趣。在实际应用中，路面裂缝等许多裂缝连续性差、对比度低，给利用低层特征进行基于图像的裂缝检测带来了很大的挑战。在本文中，我们提出了深度裂纹——一个端到端可训练的深度卷积神经网络，通过学习用于裂纹表示的高级特征来自动检测裂纹。该方法将在层次卷积阶段学习到的多尺度深度卷积特征融合在一起，以获取线路结构。更详细的表示在大比例尺的feature maps中进行，更全面的表示在小比例尺的feature maps中进行。我们在SegNet的编码器解码器架构上构建深度裂纹网，并对在相同尺度下在编码器网络和解码器网络中生成的卷积特征进行配对融合。(Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack---an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale.)