Hirose FH26-33S-0.3SHW.pdf

说明：  单片机初学者必备，看看就知道，可以下载学习，很有帮助(SCM beginners essential to see to know, you can download learning helpful)

说明：  双通道语音增强算法，消除环境噪声。采用归一化自适应方法，噪声抵消10dB，语音保持较好可懂度。(Dual-channel speech enhancement algorithm, to eliminate environmental noise. The use of normalized adaptive methods, noise cancellation 10dB, to maintain good voice intelligibility.)

说明：  写入txt文件，多行写入，同时可以写入汉字，方便记录数据(write the data to the text file, and also could write the Chinese character to the file, it is easy to read it and make it.)

说明：  卷积神经网络实现手势数字的识别（基于tensorflow）(Recognition of gesture numbers by convolutional neural network (based on tensorflow))

网上关于YUV转RGB的还真不少,我这个还是不错的,查表形,很快的,一个简单的VC程序但还不错

港口泊位分配问题，采用贪心算法和模拟退火算法(Port berth allocation problem)

说明：  Unluac help me dont feeling

说明：  VIVADO pl端文档 基于zynq 7020(vivado soc pl example text of zynq)

说明：  基于PID的暖房温度控制文件，实测可用，使用STM32F429芯片编程(PID greenhouse controller)

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.)