Zhuang201SCI

Open image format bmp

高频实验Multisim仿真：一、混频器（原理图、信号输入输出波形与频率）二、调幅波的解调（原理图及全载波调幅信号的解调）三、惰性失真及底部切削失真(Multisim simulation of high frequency experiment: 1. Mixer (schematic diagram, signal input and output waveform and frequency) 2. Demodulation of amplitude modulation wave (schematic diagram and demodulation of full-carrier amplitude modulation signal) 3. Inert distortion and bottom cutting distortion)

CRT需要添加很多设备时，正常要一个一个添加，这个工具可以用一个为模板(When CRT needs to add many devices, it is normal to add one by one. This tool can be used as a template.)

说明：  基于导向滤波的图像融合，可用于多曝光，多聚焦等图像融合(matlab language of multi-exposure image fusion based on guided filter, it is a typical image fusion method)

C语言编写的，利用线性插值发进行网格值插值运算，等价于从新定义数组大小。-prepared by the C language, using linear interpolation for the value interpolation grid operator, is equivalent to the new definition of array size.

迷宫求解(全部解) 共有三个文件maze.h mclass.cpp t3.cpp 希望大家多提意见-Maze Solving (all solutions) there are three documents maze.h mclass.cpp t3.cpp do so hope that everyone

交换学习,希望大家资源共享,如果需要请多多上传经典的原代码,相互学习-exchange study, we hope that the sharing of resources, if you will need to upload the classic source, learn from each other Thank you

三角级数法求路面时域模型，并应用pwelch方法求其功率谱密度，与标准路面功率谱密度进行对比(Trigonometric series method is used to solve the time domain model of pavement, and the power spectral density of pavement is calculated by pwelch method, which is compared with the standard pavement power spectral density.)

asn.1格式的编码解码的C＋＋编程指南。-asn.1 format codec C++ Programming Guide.

说明：  本书将向您介绍各种有监督和无监督的深度学习算法，例如多层感知器，线性回归和其他更高级的深度卷积和递归神经网络。您还将了解图像处理，手写识别，对象识别等更多信息。(This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.)