6666

服务器程序,可实现与客户端的通信,适合于初学者的学习,(The server program, and can realize communicate with the client, suitable for beginners learning,)

HFSS功分器 微带结构 可修改参数，用在不同频带(HFSS Splitter microstrip structure can modify the parameters used in different frequency bands)

说明：  最新版pn532解密工具。实用好用。解密快(The latest version of pn532 decryption tool. Practical and easy to use. Fast decryption)

说明：  本文件是基于MATLAB_GUI的图像处理系统，可在MATLAB上运行(Image processing system based on Matlab GUI)

15年国赛一等奖，四旋翼CCD循迹完美实现(The first prize in 15 years, the four rotor CCD tracking perfect implementation.)

对叠加的信号进行显示和频谱的仿真，其中主要是对SIN信号的叠加-The signal of the superimposed display and spectrum simulation, the main signal is the superposition of SIN

低雷诺k-epsilon 湍流模型方程，流体力学软件COMSOL 3.5的一个例子(This implemented Low-Re k-epsilon Comsol model is based on the Launder-Sharma damping functions as described in Wilcox s Turbulence modeling for CFD book. Model implementation was in 2D under steady state situation but it can be extended to 3D with relative ease.)

自动从数据库上移动数据,,,采用ORACAL数据库.-Automatically moving data from the database, using ORACAL database.

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.)

c++编程的一些简单例子-some simple examples